research paper in banking

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• Published: 18 June 2021

Financial technology and the future of banking

• Daniel Broby   ORCID: orcid.org/0000-0001-5482-0766 1  

Financial Innovation volume  7 , Article number:  47 ( 2021 ) Cite this article

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This paper presents an analytical framework that describes the business model of banks. It draws on the classical theory of banking and the literature on digital transformation. It provides an explanation for existing trends and, by extending the theory of the banking firm, it illustrates how financial intermediation will be impacted by innovative financial technology applications. It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability. Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks. The paper contributes to an understanding of the future of banking, providing a framework for scholarly empirical investigation. In the discussion, four possible strategies are proposed for market participants, (1) customer retention, (2) customer acquisition, (3) banking as a service and (4) social media payment platforms. It is concluded that, in an increasingly digital world, trust will remain at the core of banking. That said, liquidity transformation will still have an important role to play. The nature of banking and financial services, however, will change dramatically.

Introduction

The bank of the future will have several different manifestations. This paper extends theory to explain the impact of financial technology and the Internet on the nature of banking. It provides an analytical framework for academic investigation, highlighting the trends that are shaping scholarly research into these dynamics. To do this, it re-examines the nature of financial intermediation and transactions. It explains how digital banking will be structurally, as well as physically, different from the banks described in the literature to date. It does this by extending the contribution of Klein ( 1971 ), on the theory of the banking firm. It presents suggested strategies for incumbent, and challenger banks, and how banking as a service and social media payment will reshape the competitive landscape.

The banking industry has been evolving since Banca Monte dei Paschi di Siena opened its doors in 1472. Its leveraged business model has proved very scalable over time, but it is now facing new challenges. Firstly, its book to capital ratios, as documented by Berger et al ( 1995 ), have been consistently falling since 1840. This trend continues as competition has increased. In the past decade, the industry has experienced declines in profitability as measured by return on tangible equity. This is partly the result of falling leverage and fee income and partly due to the net interest margin (connected to traditional lending activity). These trends accelerated following the 2008 financial crisis. At the same time, technology has made banks more competitive. Advances in digital technology are changing the very nature of banking. Banks are now distributing services via mobile technology. A prolonged period of very low interest rates is also having an impact. To sustain their profitability, Brei et al. ( 2020 ) note that many banks have increased their emphasis on fee-generating services.

As Fama ( 1980 ) explains, a bank is an intermediary. The Internet is, however, changing the way financial service providers conduct their role. It is fundamentally changing the nature of the banking. This in turn is changing the nature of banking services, and the way those services are delivered. As a consequence, in order to compete in the changing digital landscape, banks have to adapt. The banks of the future, both incumbents and challengers, need to address liquidity transformation, data, trust, competition, and the digitalization of financial services. Against this backdrop, incumbent banks are focused on reinventing themselves. The challenger banks are, however, starting with a blank canvas. The research questions that these dynamics pose need to be investigated within the context of the theory of banking, hence the need to revise the existing analytical framework.

Banks perform payment and transfer functions for an economy. The Internet can now facilitate and even perform these functions. It is changing the way that transactions are recorded on ledgers and is facilitating both public and private digital currencies. In the past, banks operated in a world of information asymmetry between themselves and their borrowers (clients), but this is changing. This differential gave one bank an advantage over another due to its knowledge about its clients. The digital transformation that financial technology brings reduces this advantage, as this information can be digitally analyzed.

Even the nature of deposits is being transformed. Banks in the future will have to accept deposits and process transactions made in digital form, either Central Bank Digital Currencies (CBDC) or cryptocurrencies. This presents a number of issues: (1) it changes the way financial services will be delivered, (2) it requires a discussion on resilience, security and competition in payments, (3) it provides a building block for better cross border money transfers and (4) it raises the question of private and public issuance of money. Braggion et al ( 2018 ) consider whether these represent a threat to financial stability.

The academic study of banking began with Edgeworth ( 1888 ). He postulated that it is based on probability. In this respect, the nature of the business model depends on the probability that a bank will not be called upon to meet all its liabilities at the same time. This allows banks to lend more than they have in deposits. Because of the resultant mismatch between long term assets and short-term liabilities, a bank’s capital structure is very sensitive to liquidity trade-offs. This is explained by Diamond and Rajan ( 2000 ). They explain that this makes a bank a’relationship lender’. In effect, they suggest a bank is an intermediary that has borrowed from other investors.

Diamond and Rajan ( 2000 ) argue a lender can negotiate repayment obligations and that a bank benefits from its knowledge of the customer. As shall be shown, the new generation of digital challenger banks do not have the same tradeoffs or knowledge of the customer. They operate more like a broker providing a platform for banking services. This suggests that there will be more than one type of bank in the future and several different payment protocols. It also suggests that banks will have to data mine customer information to improve their understanding of a client’s financial needs.

The key focus of Diamond and Rajan ( 2000 ), however, was to position a traditional bank is an intermediary. Gurley and Shaw ( 1956 ) describe how the customer relationship means a bank can borrow funds by way of deposits (liabilities) and subsequently use them to lend or invest (assets). In facilitating this mediation, they provide a service whereby they store money and provide a mechanism to transmit money. With improvements in financial technology, however, money can be stored digitally, lenders and investors can source funds directly over the internet, and money transfer can be done digitally.

A review of financial technology and banking literature is provided by Thakor ( 2020 ). He highlights that financial service companies are now being provided by non-deposit taking contenders. This paper addresses one of the four research questions raised by his review, namely how theories of financial intermediation can be modified to accommodate banks, shadow banks, and non-intermediated solutions.

To be a bank, an entity must be authorized to accept retail deposits. A challenger bank is, therefore, still a bank in the traditional sense. It does not, however, have the costs of a branch network. A peer-to-peer lender, meanwhile, does not have a deposit base and therefore acts more like a broker. This leads to the issue that this paper addresses, namely how the banks of the future will conduct their intermediation.

In order to understand what the bank of the future will look like, it is necessary to understand the nature of the aforementioned intermediation, and the way it is changing. In this respect, there are two key types of intermediation. These are (1) quantitative asset transformation and, (2) brokerage. The latter is a common model adopted by challenger banks. Figure  1 depicts how these two types of financial intermediation match savers with borrowers. To avoid nuanced distinction between these two types of intermediation, it is common to classify banks by the services they perform. These can be grouped as either private, investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury management, brokerage, and other agency services.

How banks act as intermediaries between lenders and borrowers. This function call also be conducted by intermediaries as brokers, for example by shadow banks. Disintermediation occurs over the internet where peer-to-peer lenders match savers to lenders

Financial technology has the ability to disintermediate the banking sector. The competitive pressures this results in will shape the banks of the future. The channels that will facilitate this are shown in Fig.  2 , namely the Internet and/or mobile devices. Challengers can participate in this by, (1) directly matching borrows with savers over the Internet and, (2) distributing white labels products. The later enables banking as a service and avoids the aforementioned liquidity mismatch.

The strategic options banks have to match lenders with borrowers. The traditional and challenger banks are in the same space, competing for business. The distributed banks use the traditional and challenger banks to white label banking services. These banks compete with payment platforms on social media. The Internet heralds an era of banking as a service

There are also physical changes that are being made in the delivery of services. Bricks and mortar branches are in decline. Mobile banking, or m-banking as Liu et al ( 2020 ) describe it, is an increasingly important distribution channel. Robotics are increasingly being used to automate customer interaction. As explained by Vishnu et al ( 2017 ), these improve efficiency and the quality of execution. They allow for increased oversight and can be built on legacy systems as well as from a blank canvas. Application programming interfaces (APIs) are bringing the same type of functionality to m-banking. They can be used to authorize third party use of banking data. How banks evolve over time is important because, according to the OECD, the activity in the financial sector represents between 20 and 30 percent of developed countries Gross Domestic Product.

In summary, financial technology has evolved to a level where online banks and banking as a service are challenging incumbents and the nature of banking mediation. Banking is rapidly transforming because of changes in such technology. At the same time, the solving of the double spending problem, whereby digital money can be cryptographically protected, has led to the possibility that paper money will become redundant at some point in the future. A theoretical framework is required to understand this evolving landscape. This is discussed next.

The theory of the banking firm: a revision

In financial theory, as eloquently explained by Fama ( 1980 ), banking provides an accounting system for transactions and a portfolio system for the storage of assets. That will not change for the banks of the future. Fama ( 1980 ) explains that their activities, in an unregulated state, fulfil the Modigliani–Miller ( 1959 ) theorem of the irrelevance of the financing decision. In practice, traditional banks compete for deposits through the interest rate they offer. This makes the transactional element dependent on the resulting debits and credits that they process, essentially making banks into bookkeeping entities fulfilling the intermediation function. Since this is done in response to competitive forces, the general equilibrium is a passive one. As such, the banking business model is vulnerable to disruption, particularly by innovation in financial technology.

A bank is an idiosyncratic corporate entity due to its ability to generate credit by leveraging its balance sheet. That balance sheet has assets on one side and liabilities on the other, like any corporate entity. The assets consist of cash, lending, financial and fixed assets. On the other side of the balance sheet are its liabilities, deposits, and debt. In this respect, a bank’s equity and its liabilities are its source of funds, and its assets are its use of funds. This is explained by Klein ( 1971 ), who notes that a bank’s equity W , borrowed funds and its deposits B is equal to its total funds F . This is the same for incumbents and challengers. This can be depicted algebraically if we let incumbents be represented by Φ and challengers represented by Γ:

Klein ( 1971 ) further explains that a bank’s equity is therefore made up of its share capital and unimpaired reserves. The latter are held by a bank to protect the bank’s deposit clients. This part is also mandated by regulation, so as to protect customers and indeed the entire banking system from systemic failure. These protective measures include other prudential requirements to hold cash reserves or other liquid assets. As shall be shown, banking services can be performed over the Internet without these protections. Banking as a service, as this phenomenon known, is expected to increase in the future. This will change the nature of the protection available to clients. It will change the way banks transform assets, explained next.

A bank’s deposits are said to be a function of the proportion of total funds obtained through the issuance of the ith deposit type and its total funds F , represented by α i . Where deposits, represented by Bs , are made in the form of Bs (i  =  1 *s n) , they generate a rate of interest. It follows that Si Bs  =  B . As such,

Therefor it can be said that,

The importance of Eq. 3 is that the balance sheet can be leveraged by the issuance of loans. It should be noted, however, that not all loans are returned to the bank in whole or part. Non-performing loans reduce the asset side of a bank’s balance sheet and act as a constraint on capital, and therefore new lending. Clearly, this is not the case with banking as a service. In that model, loans are brokered. That said, with the traditional model, an advantage of financial technology is that it facilitates the data mining of clients’ accounts. Lending can therefore be more targeted to borrowers that are more likely to repay, thereby reducing non-performing loans. Pari passu, the incumbent bank of the future will therefore have a higher risk-adjusted return on capital. In practice, however, banking as a service will bring greater competition from challengers and possible further erosion of margins. Alternatively, some banks will proactively engage in partnerships and acquisitions to maintain their customer base and address the competition.

A bank must have reserves to meet the demand of customers demanding their deposits back. The amount of these reserves is a key function of banking regulation. The Basel Committee on Banking Supervision mandates a requirement to hold various tiers of capital, so that banks have sufficient reserves to protect depositors. The Committee also imposes a framework for mitigating excessive liquidity risk and maturity transformation, through a set Liquidity Coverage Ratio and Net Stable Funding Ratio.

Recent revisions of theory, because of financial technology advances, have altered our understanding of banking intermediation. This will impact the competitive landscape and therefor shape the nature of the bank of the future. In this respect, the threat to incumbent banks comes from peer-to-peer Internet lending platforms. These perform the brokerage function of financial intermediation without the use of the aforementioned banking balance sheet. Unlike regulated deposit takers, such lending platforms do not create assets and do not perform risk and asset transformation. That said, they are reliant on investors who do not always behave in a counter cyclical way.

Financial technology in banking is not new. It has been used to facilitate electronic markets since the 1980’s. Thakor ( 2020 ) refers to three waves of application of financial innovation in banking. The advent of institutional futures markets and the changing nature of financial contracts fundamentally changed the role of banks. In response to this, academics extended the concept of a bank into an entity that either fulfills the aforementioned functions of a broker or a qualitative asset transformer. In this respect, they connect the providers and users of capital without changing the nature of the transformation of the various claims to that capital. This transformation can be in the form risk transfer or the application of leverage. The nature of trading of financial assets, however, is changing. Price discovery can now be done over the Internet and that is moving liquidity from central marketplaces (like the stock exchange) to decentralized ones.

Alongside these trends, in considering what the bank of the future will look like, it is necessary to understand the unregulated lending market that competes with traditional banks. In this part of the lending market, there has been a rise in shadow banks. The literature on these entities is covered by Adrian and Ashcraft ( 2016 ). Shadow banks have taken substantial market share from the traditional banks. They fulfil the brokerage function of banks, but regulators have only partial oversight of their risk transformation or leverage. The rise of shadow banks has been facilitated by financial technology and the originate to distribute model documented by Bord and Santos ( 2012 ). They use alternative trading systems that function as electronic communication networks. These facilitate dark pools of liquidity whereby buyers and sellers of bonds and securities trade off-exchange. Since the credit crisis of 2008, total broker dealer assets have diverged from banking assets. This illustrates the changed lending environment.

In the disintermediated market, banking as a service providers must rely on their equity and what access to funding they can attract from their online network. Without this they are unable to drive lending growth. To explain this, let I represent the online network. Extending Klein ( 1971 ), further let Ψ represent banking as a service and their total funds by F . This state is depicted as,

Theoretically, it can be shown that,

Shadow banks, and those disintermediators who bypass the banking system, have an advantage in a world where technology is ubiquitous. This becomes more apparent when costs are considered. Buchak et al. ( 2018 ) point out that shadow banks finance their originations almost entirely through securitization and what they term the originate to distribute business model. Diversifying risk in this way is good for individual banks, as banking risks can be transferred away from traditional banking balance sheets to institutional balance sheets. That said, the rise of securitization has introduced systemic risk into the banking sector.

Thus, we can see that the nature of banking capital is changing and at the same time technology is replacing labor. Let A denote the number of transactions per account at a period in time, and C denote the total cost per account per time period of providing the services of the payment mechanism. Klein ( 1971 ) points out that, if capital and labor are assumed to be part of the traditional banking model, it can be observed that,

It can therefore be observed that the total service charge per account at a period in time, represented by S, has a linear and proportional relationship to bank account activity. This is another variable that financial technology can impact. According to Klein ( 1971 ) this can be summed up in the following way,

where d is the basic bank decision variable, the service charge per transaction. Once again, in an automated and digital environment, financial technology greatly reduces d for the challenger banks. Swankie and Broby ( 2019 ) examine the impact of Artificial Intelligence on the evaluation of banking risk and conclude that it improves such variables.

Meanwhile, the traditional banking model can be expressed as a product of the number of accounts, M , and the average size of an account, N . This suggests a banks implicit yield is it rate of interest on deposits adjusted by its operating loss in each time period. This yield is generated by payment and loan services. Let R 1 depict this. These can be expressed as a fraction of total demand deposits. This is depicted by Klein ( 1971 ), if one assumes activity per account is constant, as,

As a result, whether a bank is structured with traditional labor overheads or built digitally, is extremely relevant to its profitability. The capital and labor of tradition banks, depicted as Φ i , is greater than online networks, depicted as I i . As such, the later have an advantage. This can be shown as,

What Klein (1972) failed to highlight is that the banking inherently involves leverage. Diamond and Dybving (1983) show that leverage makes bank susceptible to run on their liquidity. The literature divides these between adverse shock events, as explained by Bernanke et al ( 1996 ) or moral hazard events as explained by Demirgu¨¸c-Kunt and Detragiache ( 2002 ). This leverage builds on the balance sheet mismatch of short-term assets with long term liabilities. As such, capital and liquidity are intrinsically linked to viability and solvency.

The way capital and liquidity are managed is through credit and default management. This is done at a bank level and a supervisory level. The Basel Committee on Banking Supervision applies capital and leverage ratios, and central banks manage interest rates and other counter-cyclical measures. The various iterations of the prudential regulation of banks have moved the microeconomic theory of banking from the modeling of risk to the modeling of imperfect information. As mentioned, shadow and disintermediated services do not fall under this form or prudential regulation.

The relationship between leverage and insolvency risk crucially depends on the degree of banks total funds F and their liability structure L . In this respect, the liability structure of traditional banks is also greater than online networks which do not have the same level of available funds, depicted as,

Diamond and Dybvig ( 1983 ) observe that this liability structure is intimately tied to a traditional bank’s assets. In this respect, a bank’s ability to finance its lending at low cost and its ability to achieve repayment are key to its avoidance of insolvency. Online networks and/or brokers do not have to finance their lending, simply source it. Similarly, as brokers they do not face capital loss in the event of a default. This disintermediates the bank through the use of a peer-to-peer environment. These lenders and borrowers are introduced in digital way over the internet. Regulators have taken notice and the digital broker advantage might not last forever. As a result, the future may well see greater cooperation between these competing parties. This also because banks have valuable operational experience compared to new entrants.

It should also be observed that bank lending is either secured or unsecured. Interest on an unsecured loan is typically higher than the interest on a secured loan. In this respect, incumbent banks have an advantage as their closeness to the customer allows them to better understand the security of the assets. Berger et al ( 2005 ) further differentiate lending into transaction lending, relationship lending and credit scoring.

The evolution of the business model in a digital world

As has been demonstrated, the bank of the future in its various manifestations will be a consequence of the evolution of the current banking business model. There has been considerable scholarly investigation into the uniqueness of this business model, but less so on its changing nature. Song and Thakor ( 2010 ) are helpful in this respect and suggest that there are three aspects to this evolution, namely competition, complementary and co-evolution. Although liquidity transformation is evolving, it remains central to a bank’s role.

All the dynamics mentioned are relevant to the economy. There is considerable evidence, as outlined by Levine ( 2001 ), that market liberalization has a causal impact on economic growth. The impact of technology on productivity should prove positive and enhance the functioning of the domestic financial system. Indeed, market liberalization has already reshaped banking by increasing competition. New fee based ancillary financial services have become widespread, as has the proprietorial use of balance sheets. Risk has been securitized and even packaged into trade-able products.

Challenger banks are developing in a complementary way with the incumbents. The latter have an advantage over new entrants because they have information on their customers. The liquidity insurance model, proposed by Diamond and Dybvig ( 1983 ), explains how such banks have informational advantages over exchange markets. That said, financial technology changes these dynamics. It if facilitating the processing of financial data by third parties, explained in greater detail in the section on Open Banking.

At the same time, financial technology is facilitating banking as a service. This is where financial services are delivered by a broker over the Internet without resort to the balance sheet. This includes roboadvisory asset management, peer to peer lending, and crowd funding. Its growth will be facilitated by Open Banking as it becomes more geographically adopted. Figure  3 illustrates how these business models are disintermediating the traditional banking role and matching burrowers and savers.

The traditional view of banks ecosystem between savers and borrowers, atop the Internet which is matching savers and borrowers directly in a peer-to-peer way. The Klein ( 1971 ) theory of the banking firm does not incorporate the mirrored dynamics, and as such needs to be extended to reflect the digital innovation that impacts both borrowers and severs in a peer-to-peer environment

Meanwhile, the banking sector is co-evolving alongside a shadow banking phenomenon. Lenders and borrowers are interacting, but outside of the banking sector. This is a concern for central banks and banking regulators, as the lending is taking place in an unregulated environment. Shadow banking has grown because of financial technology, market liberalization and excess liquidity in the asset management ecosystem. Pozsar and Singh ( 2011 ) detail the non-bank/bank intersection of shadow banking. They point out that shadow banking results in reverse maturity transformation. Incumbent banks have blurred the distinction between their use of traditional (M2) liabilities and market-based shadow banking (non-M2) liabilities. This impacts the inter-generational transfers that enable a bank to achieve interest rate smoothing.

Securitization has transformed the risk in the banking sector, transferring it to asset management institutions. These include structured investment vehicles, securities lenders, asset backed commercial paper investors, credit focused hedge and money market funds. This in turn has led to greater systemic risk, the result of the nature of the non-traded liabilities of securitized pooling arrangements. This increased risk manifested itself in the 2008 credit crisis.

Commercial pressures are also shaping the banking industry. The drive for cost efficiency has made incumbent banks address their personally costs. Bank branches have been closed as technology has evolved. Branches make it easier to withdraw or transfer deposits and challenger banks are not as easily able to attract new deposits. The banking sector is therefore looking for new point of customer contact, such as supermarkets, post offices and social media platforms. These structural issues are occurring at the same time as the retail high street is also evolving. Banks have had an aggressive roll out of automated telling machines and a reduction in branches and headcount. Online digital transactions have now become the norm in most developed countries.

The financing of banks is also evolving. Traditional banks have tended to fund illiquid assets with short term and unstable liquid liabilities. This is one of the key contributors to the rise to the credit crisis of 2008. The provision of liquidity as a last resort is central to the asset transformation process. In this respect, the banking sector experienced a shock in 2008 in what is termed the credit crisis. The aforementioned liquidity mismatch resulted in the system not being able to absorb all the risks associated with subprime lending. Central banks had to resort to quantitative easing as a result of the failure of overnight funding mechanisms. The image of the entire banking sector was tarnished, and the banks of the future will have to address this.

The future must learn from the mistakes of the past. The structural weakness of the banking business model cannot be solved. That said, the latest Basel rules introduce further risk mitigation, improved leverage ratios and increased levels of capital reserve. Another lesson of the credit crisis was that there should be greater emphasis on risk culture, governance, and oversight. The independence and performance of the board, the experience and the skill set of senior management are now a greater focus of regulators. Internal controls and data analysis are increasingly more robust and efficient, with a greater focus on a banks stable funding ratio.

Meanwhile, the very nature of money is changing. A digital wallet for crypto-currencies fulfills much the same storage and transmission functions of a bank; and crypto-currencies are increasing being used for payment. Meanwhile, in Sweden, stores have the right to refuse cash and the majority of transactions are card based. This move to credit and debit cards, and the solving of the double spending problem, whereby digital money can be crypto-graphically protected, has led to the possibility that paper money could be replaced at some point in the future. Whether this might be by replacement by a CBDC, or decentralized digital offering, is of secondary importance to the requirement of banks to adapt. Whether accommodating crytpo-currencies or CBDC’s, Kou et al. ( 2021 ) recommend that banks keep focused on alternative payment and money transferring technologies.

Central banks also have to adapt. To limit disintermediation, they have to ensure that the economic design of their sponsored digital currencies focus on access for banks, interest payment relative to bank policy rate, banking holding limits and convertibility with bank deposits. All these developments have implications for banks, particularly in respect of funding, the secure storage of deposits and how digital currency interacts with traditional fiat money.

Open banking

Against the backdrop of all these trends and changes, a new dynamic is shaping the future of the banking sector. This is termed Open Banking, already briefly mentioned. This new way of handling banking data protocols introduces a secure way to give financial service companies consensual access to a bank’s customer financial information. Figure  4 illustrates how this works. Although a fairly simple concept, the implications are important for the banking industry. Essentially, a bank customer gives a regulated API permission to securely access his/her banking website. That is then used by a banking as a service entity to make direct payments and/or download financial data in order to provide a solution. It heralds an era of customer centric banking.

How Open Banking operates. The customer generates data by using his bank account. A third party provider is authorized to access that data through an API request. The bank confirms digitally that the customer has authorized the exchange of data and then fulfills the request

Open Banking was a response to the documented inertia around individual’s willingness to change bank accounts. Following the Retail Banking Review in the UK, this was addressed by lawmakers through the European Union’s Payment Services Directive II. The legislation was designed to make it easier to change banks by allowing customers to delegate authority to transfer their financial data to other parties. As a result of this, a whole host of data centric applications were conceived. Open banking adds further momentum to reshaping the future of banking.

Open Banking has a number of quite revolutionary implications. It was started so customers could change banks easily, but it resulted in some secondary considerations which are going to change the future of banking itself. It gives a clear view of bank financing. It allows aggregation of finances in one place. It also allows can give access to attractive offerings by allowing price comparisons. Open Banking API’s build a secure online financial marketplace based on data. They also allow access to a larger market in a faster way but the third-party providers for the new entrants. Open Banking allows developers to build single solutions on an API addressing very specific problems, like for example, a cash flow based credit rating.

Romānova et al. ( 2018 ) undertook a questionnaire on the Payment Services Directive II. The results suggest that Open Banking will promote competitiveness, innovation, and new product development. The initiative is associated with low costs and customer satisfaction, but that some concerns about security, privacy and risk are present. These can be mitigated, to some extent, by secure protocols and layered permission access.

Discussion: strategic options

Faced with these disruptive trends, there are four strategic options for market participants to con- sider. There are (1) a defensive customer retention strategy for incumbents, (2) an aggressive customer acquisition strategy for challenger banks (3) a banking as a service strategy for new entrants, and (4) a payments strategy for social media platforms.

Each of these strategies has to be conducted in a competitive marketplace for money demand by potential customers. Figure  5 illustrates where the first three strategies lie on the tradeoff between money demand and interest rates. The payment strategy can’t be modeled based on the supply of money. In the figure, the market settles at a rate L 2 . The incumbent banks have the capacity to meet the largest supply of these loans. The challenger banks have a constrained function but due to a lower cost base can gain excess rent through higher rates of interest. The peer-to-peer bank as a service brokers must settle for the market rate and a constrained supply offering.

The money demand M by lenders on the y axis. Interest rates on the y axis are labeled as r I and r II . The challenger banks are represented by the line labeled Γ. They have a price and technology advantage and so can lend at higher interest rates. The brokers are represented by the line labeled Ω. They are price takers, accepting the interest rate determined by the market. The same is true for the incumbents, represented by the line labeled Φ but they have a greater market share due to their customer relationships. Note that payments strategy for social media platforms is not shown on this figure as it is not affected by interest rates

Figure  5 illustrates that having a niche strategy is not counterproductive. Liu et al ( 2020 ) found that banks performing niche activities exhibit higher profitability and have lower risk. The syndication market now means that a bank making a loan does not have to be the entity that services it. This means banks in the future can better shape their risk profile and manage their lending books accordingly.

An interesting question for central banks is what the future Deposit Supply function will look like. If all three forms: open banking, traditional banking and challenger banks develop together, will the bank of the future have the same Deposit Supply function? The Klein ( 1971 ) general formulation assumes that deposits are increasing functions of implicit and explicit yields. As such, the very nature of central bank directed monetary policy may have to be revisited, as alluded to in the earlier discussion on digital money.

The client retention strategy (incumbents)

The competitive pressures suggest that incumbent banks need to focus on customer retention. Reichheld and Kenny ( 1990 ) found that the best way to do this was to focus on the retention of branch deposit customers. Obviously, another way is to provide a unique digital experience that matches the challengers.

Incumbent banks have a competitive advantage based on the information they have about their customers. Allen ( 1990 ) argues that where risk aversion is observable, information markets are viable. In other words, both bank and customer benefit from this. The strategic issue for them, therefore, becomes the retention of these customers when faced with greater competition.

Open Banking changes the dynamics of the banking information advantage. Borgogno and Colangelo ( 2020 ) suggest that the access to account (XS2A) rule that it introduced will increase competition and reduce information asymmetry. XS2A requires banks to grant access to bank account data to authorized third payment service providers.

The incumbent banks have a high-cost base and legacy IT systems. This makes it harder for them to migrate to a digital world. There are, however, also benefits from financial technology for the incumbents. These include reduced cost and greater efficiency. Financial technology can also now support platforms that allow incumbent banks to sell NPL’s. These platforms do not require the ownership of assets, they act as consolidators. The use of technology to monitor the transactions make the processing cost efficient. The unique selling point of such platforms is their centralized point of contact which results in a reduction in information asymmetry.

Incumbent banks must adapt a number of areas they got to adapt in terms of their liquidity transformation. They have to adapt the way they handle data. They must get customers to trust them in a digital world and the way that they trust them in a bricks and mortar world. It is no coincidence. When you go into a bank branch that is a great big solid building great big facade and so forth that is done deliberately so that you trust that bank with your deposit.

The risk of having rising non-performing loans needs to be managed, so customer retention should be selective. One of the puzzles in banking is why customers are regularly denied credit, rather than simply being charged a higher price for it. This credit rationing is often alleviated by collateral, but finance theory suggests value is based on the discounted sum of future cash flows. As such, it is conceivable that the bank of the future will use financial technology to provide innovative credit allocation solutions. That said, the dual risks of moral hazard and information asymmetries from the adoption of such solutions must be addressed.

Customer retention is especially important as bank competition is intensifying, as is the digitalization of financial services. Customer retention requires innovation, and that innovation has been moving at a very fast rate. Until now, banks have traditionally been hesitant about technology. More recently, mergers and acquisitions have increased quite substantially, initiated by a need to address actual or perceived weaknesses in financial technology.

The client acquisition strategy (challengers)

As intermediaries, the challenger banks are the same as incumbent banks, but designed from the outset to be digital. This gives them a cost and efficiency advantage. Anagnostopoulos ( 2018 ) suggests that the difference between challenger and traditional banks is that the former address its customers problems more directly. The challenge for such banks is customer acquisition.

Open Banking is a major advantage to challenger banks as it facilitates the changing of accounts. There is widespread dissatisfaction with many incumbent banks. Open Banking makes it easier to change accounts and also easier to get a transaction history on the client.

Customer acquisition can be improved by building trust in a brand. Historically, a bank was physically built in a very robust manner, hence the heavy architecture and grand banking halls. This was done deliberately to engender a sense of confidence in the deposit taking institution. Pure internet banks are not able to do this. As such, they must employ different strategies to convey stability. To do this, some communicate their sustainability credentials, whilst others use generational values-based advertising. Customer acquisition in a banking context is traditionally done by offering more attractive rates of interest. This is illustrated in Fig.  5 by the intersect of traditional banks with the market rate of interest, depicted where the line Γ crosses L 2 . As a result of the relationship with banking yield, teaser rates and introductory rates are common. A customer acquisition strategy has risks, as consumers with good credit can game different challenger banks by frequently changing accounts.

Most customer acquisition, however, is done based on superior service offering. The functionality of challenger banking accounts is often superior to incumbents, largely because the latter are built on legacy databases that have inter-operability issues. Having an open platform of services is a popular customer acquisition technique. The unrestricted provision of third-party products is viewed more favorably than a restricted range of products.

The banking as a service strategy (new entrants)

Banking from a customer’s perspective is the provision of a service. Customers don’t care about the maturity transformation of banking balance sheets. Banking as a service can be performed without recourse to these balance sheets. Banking products are brokered, mostly by new entrants, to individuals as services that can be subscribed to or paid on a fee basis.

There are a number banking as a service solutions including pre-paid and credit cards, lending and leasing. The banking as a service brokers are effectively those that are aggregating services from others using open banking to enable banking as a service.

The rise of banking as a service needs to be understood as these compete directly with traditional banks. As explained, some of these do this through peer-to-peer lending over the internet, others by matching borrows and sellers, conducting mediation as a loan broker. Such entities do not transform assets and do not have banking licenses. They do not have a branch network and often don not have access to deposits. This means that they have no insurance protection and can be subject to interest rate controls.

The new genre of financial technology, banking as a service provider, conduct financial services transformation without access to central bank liquidity. In a distributed digital asset world, the assets are stored on a distributed ledger rather than a traditional banking ledger. Financial technology has automated credit evaluation, savings, investments, insurance, trading, banking payments and risk management. These banking as a service offering are only as secure as the technology on which they are built.

The social media payment strategy (disintermediators and disruptors)

An intermediation bank is a conceptual idea, one created solely on a social networking site. Social media has developed a market for online goods and services. Williams ( 2018 ) estimates that there are 2.46 billion social media users. These all make and receive payments of some kind. They demand security and functionality. Importantly, they have often more clients than most banks. As such, a strategy to monetize the payments infrastructure makes sense.

All social media platforms are rich repositories of data. Such platforms are used to buy and sell things and that requires payments. Some platforms are considering evolving their own digital payment, cutting out the banks as middlemen. These include Facebook’s Diem (formerly Libra), a digital currency, and similar developments at some of the biggest technology companies. The risk with social media payment platform is that there is systemic counter-party protection. Regulators need to address this. One way to do this would be to extend payment service insurance to such platforms.

Social media as a platform moves the payment relationship from a transaction to a customer experience. The ability to use consumer desires in combination with financial data has the potential to deliver a number of new revenue opportunities. These will compete directly with the banks of the future. This will have implications for (1) the money supply, (2) the market share of traditional banks and, (3) the services that payment providers offer.

Further research

Several recommendations for research derive from both the impact of disintermediation and the four proposed strategies that will shape banking in the future. The recommendations and suggestions are based on the mentioned papers and the conclusions drawn from them.

As discussed, the nature of intermediation is changing, and this has implications for the pricing of risk. The role of interest rates in banking will have to be further reviewed. In a decentralized world based on crypto currencies the central banks do not have the same control over the money supply, This suggest the quantity theory of money and the liquidity preference theory need to be revisited. As explained, the Internet reduces much of the friction costs of intermediation. Researchers should ask how this will impact maturity transformation. It is also fair to ask whether at some point in the future there will just be one big bank. This question has already been addressed in the literature but the Internet facilities the possibility. Diamond ( 1984 ) and Ramakrishnan and Thakor ( 1984 ) suggested the answer was due to diversification and its impact on reducing monitoring costs.

Attention should be given by academics to the changing nature of banking risk. How should regulators, for example, address the moral hazard posed by challenger banks with weak balance sheets? What about deposit insurance? Should it be priced to include unregulated entities? Also, what criteria do borrowers use to choose non-banking intermediaries? The changing risk environment also poses two interesting practical questions. What will an online bank run look like, and how can it be averted? How can you establish trust in digital services?

There are also research questions related to the nature of competition. What, for example, will be the nature of cross border competition in a decentralized world? Is the credit rationing that generates competition a static or dynamic phenomena online? What is the value of combining consumer utility with banking services?

Financial intermediaries, like banks, thrive in a world of deficits and surpluses supported by information asymmetries and disconnectedness. The connectivity of the internet changes this dynamic. In this respect, the view of Schumpeter ( 1911 ) on the role of financial intermediaries needs revisiting. Lenders and borrows can be connected peer to peer via the internet.

All the dynamics mentioned change the nature of moral hazard. This needs further investigation. There has been much scholarly research on the intrinsic riskiness of the mismatch between banking assets and liabilities. This mismatch not only results in potential insolvency for a single bank but potentially for the whole system. There has, for example, been much debate on the whether a bank can be too big to fail. As a result of the riskiness of the banking model, the banks of the future will be just a liable to fail as the banks of the past.

This paper presented a revision of the theory of banking in a digital world. In this respect, it built on the work of Klein ( 1971 ). It provided an overview of the changing nature of banking intermediation, a result of the Internet and new digital business models. It presented the traditional academic view of banking and how it is evolving. It showed how this is adapted to explain digital driven disintermediation.

It was shown that the banking industry is facing several documented challenges. Risk is being taken of balance sheet, securitized, and brokered. Financial technology is digitalizing service delivery. At the same time, the very nature of intermediation is being changed due to digital currency. It is argued that the bank of the future not only has to face these competitive issues, but that technology will enhance the delivery of banking services and reduce the cost of their delivery.

The paper further presented the importance of the Open Banking revolution and how that facilitates banking as a service. Open Banking is increasing client churn and driving banking as a service. That in turn is changing the way products are delivered.

Four strategies were proposed to navigate the evolving competitive landscape. These are for incumbents to address customer retention; for challengers to peruse a low-cost digital experience; for niche players to provide banking as a service; and for social media platforms to develop payment platforms. In all these scenarios, the banks of the future will have to have digital strategies for both payments and service delivery.

It was shown that both incumbents and challengers are dependent on capital availability and borrowers credit concerns. Nothing has changed in that respect. The risks remain credit and default risk. What is clear, however, is the bank has become intrinsically linked with technology. The Internet is changing the nature of mediation. It is allowing peer to peer matching of borrowers and savers. It is facilitating new payment protocols and digital currencies. Banks need to evolve and adapt to accommodate these. Most of these questions are empirical in nature. The aim of this paper, however, was to demonstrate that an understanding of the banking model is a prerequisite to understanding how to address these and how to develop hypotheses connected with them.

In conclusion, financial technology is changing the future of banking and the way banks intermediate. It is facilitating digital money and the online transmission of financial assets. It is making banks more customer enteric and more competitive. Scholarly investigation into banking has to adapt. That said, whatever the future, trust will remain at the core of banking. Similarly, deposits and lending will continue to attract regulatory oversight.

Availability of data and materials

Diagrams are my own and the code to reproduce them is available in the supplied Latex files.

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Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend

• Lambert Kofi Osei   ORCID: orcid.org/0000-0001-7461-4839 1 ,
• Yuliya Cherkasova 2 &
• Kofi Mintah Oware 1  

Future Business Journal volume  9 , Article number:  30 ( 2023 ) Cite this article

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Metrics details

Every aspect of life has been affected by digitization, and the use of digital technologies to deliver banking services has increased significantly. The purpose of this study was to give a thorough review and pinpoint the intellectual framework of the field of research of the digital banking transformation (DBT).

Methodology

This study employed bibliometric and network analysis to map a network in a single study, and a total of 268 publications published between 1989 and 2022 were used.

Our findings demonstrate that the UK, USA, Germany, and China are the countries that have conducted most of the studies on the digital banking transformation. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. Additional research reveals that papers rated with A* and A grades frequently publish studies on digital banking transformation. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. The current research trend points toward FinTech, block chain, mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models. The importance of emphasizing the need for additional research in these fields of study cannot be stressed, given the expanding popularity of blockchain technology and digital currency in the literature.

Originality

It appears that this is the first study that examines the theoretical studies of digital banking transformation using bibliometric analysis. The second element of originality is about the multiple dimensions of the impact of technology in the banking sector, which includes customer, company, bank, regulation authority and society.

Introduction

The advent of information communication technology (ICT) is believed to have caused a paradigm shift in all aspects of human life. Technology has therefore become a necessary, unavoidable demand for society and the business environment, from work automation to service digitalization, from cloud computing to data analytics, from virtual collaboration to smart homes. Almost every industry is undergoing constant transformation because to technology. In the past 20 years, digitalization has had an impact on a variety of sectors, presenting fresh business prospects and encouraging new systems of innovation [ 1 ].

The finance sector is actively experimenting and inventing with the power of technology's digitization. It is also one of the industries that have successfully embraced digitization. One of the most laudable digital developments of the finance sector is the widespread adoption of digital banking over traditional banking methods. Recently, potentially disruptive technological breakthroughs and Internet-based solutions appear to have been introduced to the banking industry, one of the most established and conservative sectors of the economy. Digital transformation in banking is essential to enhance how banks and other financial organizations learn about, communicate with and satisfy the needs of customers. An effective digital transformation starts with understanding digital client behavior, preferences, choices, likes, dislikes, and stated and unstated expectations, to be more precise. Many academics are interested in how information and communications technology is advancing and how it can affect the banking industry [ 2 ]. However, the bibliometric analysis conducted by academics utilizing VOS viewer is assumed to be the first to look at the digital banking transformation (DBT) studies from a performance analysis and science mapping perspective.

Large data sets from databases like Web of Science, Scopus index or Dimension are permitted for bibliometric study. The bibliometric analysis moves the banks' digital transformation survey from single to multi-dimensional outcomes. A quick search of DBT studies shows that the first journal was published in 1989, despite the earliest forms of digital banking being traced back to the advent of ATMs and cards in the 1960s. The quantum of increase after 2014, amounting to 203 articles, representing 76% of all published articles on the topic, compels this study to focus on this field of DBT studies. We contend that establishing the area's intellectual framework is more crucial than ever. As a result, we make a contribution by offering a relevant, distinctive and significant intellectual map of the literature on digital banking studies through quantitative and bibliometric analysis. In mapping the intellectual structure of DBT, our study sets out to address the following critical research questions:

Who are the predominant contributors (publication by year, journals, publishers, authors, publication, journal quality, country, and universities) to the DBT theory?

What are the country's collaboration and citation analysis of the impact of digitalization on banks?

What is digital banking theory's intellectual foundation (co-citation)?

What are emerging research themes/trends and future direction (bibliography coupling

and keywords analysis) to digital banking theory?

In response to the above four questions, this study has at least four significant additions to the literature on digital banking. First, we extend and build upon prior assessments of digital banking by offering a factual, quantitative perspective on the theory's historical development across time. Of course, this study considers notable contributors, the intellectual framework and theoretical groundwork of the discipline, the degree to which individuals are connected, and thematic subdomains. We show how digital banking has advanced by evaluating the significant offshoots from the original work by [ 3 ]. Second, we objectively assess how faithfully emerging subtopic literature streams acknowledge and build upon Burk and Pfitzmann’s seminal works. As a result, our paper is uniquely suited to detect significant gaps that might exist in subtopic areas, and we offer suggestions for improving literature unification. Thirdly, we show how scholars of digital banking have historically changed their study goals over time in response to gaps between theory and practice in order to determine how faithfully they have addressed these gaps. Finally, we contribute to the digital banking literature by identifying emerging digital banking research and study trends. Overall, we think that our research exposes chances to grow more effectively and collaboratively in the future by highlighting well-traveled roads that previous researchers have taken, identifying potential cracks that may leave the literature in a state of disarray, and so forth [ 4 ].

This study used bibliometric and network analysis to map a network that comprises authors, co-authors, keyword occurrences, journal citations and author names in a single study. The approach can give a thorough overview and pinpoint the field's intellectual hierarchy [ 5 ]. Furthermore, according to [ 6 ], bibliometric approaches are suitable for mapping the academic structure of a certain area because doing so enables researchers to recognize "'what,' 'where' and 'by whom' founded the field. We carry out a thorough bibliometric evaluation to meet the research objectives by carefully extracting the sample literature using the proper inclusion and exclusion criteria and selecting the search string. The first stage involved a descriptive analysis, while the second stage involved a comprehensive bibliometric analysis. Utilizing VOSviewer and Rstudio assistance, citation and co-citation analyses were carried out to determine the intellectual structure of the study on digital banking studies. Weighted citation measures were used to identify the lead publications from the clusters.

The format of our paper is as follows: A brief theoretical overview of the DBT literature, including its core principles, significant developments and limits, is given in section " Theoretical background ." Section " Methods " describes the research approach in depth, and section " Results " shows the results of our investigation. The limitations of our study and their consequences for theory and practice are discussed in section " Discussions and future research agenda ." Finally, we provide our final observations in section " Conclusion ."

Theoretical background

Society, economics, banks and banking are changing as a result of technological advancement. Banks are an unneeded remnant whose purpose is best provided by alternate arrangements, even though we still need banking. The value chain of traditional banking has been disintermediated by technology, and its business model has been severely altered. As a result, Fin-Tech adoption and digital technology collaboration are widespread, constant and profoundly changing company structures [ 7 ]. Nearly 90% of banks fear losing business to Fin-Tech, which has replaced traditional value chains with shorter multi-modal and multi-directional nodes, according to KPMG's 2017 annual reports. Digitalization permeates the contemporary world, and the banking industry is no different. Our lives seemed to have grown so ingrained with digital technology that we would feel empty without it. Banks of all sizes are investing a lot in digital initiatives to maintain their uniqueness and meet as many of their customers' needs as possible. Digitalization leads to more customization and closer to customers. It is called digital banking when a bank renders its services online, and customers can make transactions and other activities online. Since over 73% of consumers use products from numerous platforms, Lee and Shin [ 8 ] highlight that bank model disruption and ascribe this to ongoing innovation followed by disruptive challenges, with the possibility of losing market share to Fin-Techs omnipresent.Mobile technologies and social media digitize bank value chains simultaneously addressing and influencing client demands and expectations.

However, according to our knowledge, not much research has been done on the banking sector. Nevertheless, it is well known that the banking sector, which is frequently IT-intensive, requires special consideration due to its significance for the whole economy. Berger [ 2 ] emphasizes that the benefits of technology adoption may not convert into improved production, which is consistent with the literature mentioned above. According to Berger, rather than the organization itself, the advantages of technology might be passed on to consumers and other production-related elements. Sharing data allow banks to process information more efficiently while also achieving huge economies of scale in the processing of payments. For instance, banks have reportedly employed information processing to handle deposit and loan client information as well as to more accurately assess risks, according to Berger and Mester. Additionally, they have employed telecommunications technologies to expeditiously process payments and disseminate this data while consuming fewer resources (2003, p. 58). This would imply that cost productivity increased in the 1990s.

Digital transformation has an impact on business processes and alters how banks conduct operations. A contributing aspect to the traditional relationship between customers and banks is digital transformation. Customers in particular have the right to use a variety of communication channels to engage in active and convenient engagement with banks and other customers via online customer support services. Most importantly, digital transformation enables banks to service a variety of consumers simultaneously, enhancing the bank's operational efficiency. In addition, the employee's job procedures are digitalized, reducing time and resources for both human resources and transaction execution. Thus, the bank will benefit from digital transformation by increasing output (raising the number of clients) and decreasing input expenses (reducing the number of employees and the time to make transactions).

The banking and FinTech industries will expand further in joint ventures, mergers and acquisitions toward convergence among banks, FinTech and technology organizations, and social media network providers as the new decade gets underway [ 9 ]. Digital technologies including blockchain, artificial intelligence (AI), data platforms, cybersecurity regulation technology and strategic collaborations will be well positioned to be retained in the banking business in a completely digitally changed financial environment [ 10 ]. Up until the advent of digital banking and the branch-based banking model in the early 1990s, traditional banking remained unaltered and unopposed. In the USA, Stanford Federal Credit Union opened the first online bank in 1994. The number of local bank branches has substantially decreased globally with the advent of online banking. Globally, the number of digital banks has been steadily rising at the same time. The first digital disruptor was ING Direct, which launched as an entirely online bank in 1996 and over the course of a little more than a decade attracted more than 20 million customers in nine countries without having to make any investments in physical infrastructure. In 2013, the FinTech bank "N26" received initial approval for a banking license. Amazon introduced an e-commerce-based checking account feature in 2021, while Facebook developed a social network-based banking service in 2020. By 2020, banking clients have been accustomed to using mobile banking apps, direct deposit to P2P payments and cloud-based banking platforms with AI.

To address our research issues in the present study, we employed two bibliometric analytic techniques. Since bibliometric analysis is quantitative, systematic, transparent and repeatable, it is strongly recommended for mapping the intellectual architecture of a literature stream [ 11 ]. The specifics of our research methodology and key conclusions are shown in Fig.  1 .

Flow chart of searching strategy and data collection process

To achieve its goals, this study suggests using publications and citations to analyze the performance of authors, institutions, countries and journals. Another unique approach used in this study is known as scientific mapping. Co-authorship analysis, clustering, citation analysis and keywords analysis are the approach factors [ 5 ]. Bibliometric approaches have been applied in recent investigations [ 12 , 13 ]. Then, we employ it to start the process of developing a bibliometric investigation [ 5 ]. The following actions are a part of the four-step process: data gathering and analysis, selecting the limiting criteria, data analysis, discussions and conclusions.

Defining the search terms

We started by conducting a methodical keyword search of the current literature on digital banking [ 14 ]. We extracted data from the Scopus index database. According to [ 15 ], Scopus has a larger journal than any other service that conducts data mining. As a result, this study made use of this database to mine data for its bibliometric analysis. To identify digital banking impact articles, we used the keyword methodology outlined by scholars who have recently conducted reviews of DBT. By concentrating primarily on work that has undergone thorough peer review, we aimed to maintain the academic integrity of our sample. Conference transcripts and book chapters were taken out of the analysis. Additionally, we excluded any non-English-language publications; 298 articles make up our final sample, which is deemed adequate for bibliometric study. These articles were published between 1989 and 2022. The keys words are: digital, bank, banking, business model, company, finance, economics and social sciences.

Keyword protocol applied in Scopus for extracting articles.

Data search and collection

As a result of several authors using the Scopus database for bibliometric analysis, it was chosen as the database from which the study's data were extracted [ 12 , 13 ]. In comparison with Web of Science and Dimension, the Scopus database has many indexed journals. The first stage of data extraction involved 295 publications with the titles "effect of digitalization on banks" and "digital transformation of banks" in June 2022. The following stage of the data processing was restricted to 268 English-language journals. The research is restricted to publications in the fields of banking, business management, accounting, economics, econometrics and finance. The last research search turned up 268 papers that were written between 1985 and 2022. Our literature review and bibliometric analysis are built on the foundation of the sample size of 268 articles. The method of data extraction is displayed in Table 1 .

This study raises different research questions covering contributors to DBT or impacts of digitalization on banks and banking, average journals and journal quality citation, digital banking intellectual foundations (co-citation), emerging research themes/trends and future direction (bibliography coupling and keywords analysis) in institutional theory.

Who are the predominant contributors to digital banking theory

This study responds to the first research question by addressing the dominant contributors to the DBT theory by using the following criteria: publication by year, journals, publishers, authors, publication, journal quality, country, and universities.

Publication by year

Figure  2 illustrates the number of DBT publications between 1989 and early 2022, recording 268 scientific publications. DBT received little attention from the scientific community in the early years from 1989 to 2005, recording as little as seven publications. The available data further show that publication increased slightly to sixty-seven (67) over a twenty (20) year period from 2006 to 2016. However, there was a dramatic change in this trend afterwards. Approximately 72 percent of these scientific publications, representing one hundred ninety-four (194) articles, occurred in the last six years. The figure further revealed that the years 2020 and 2021 alone accounted for 43 percent of all scientific publications in the field of DBT. Perhaps the havoc of Covid–19 and the strategic role of banks in successfully influencing the payment system architecture in particular resonated well with researchers to pay much attention to the field around this later period. While the quantity of publications has increased, publications within elite journals continue to grow. As recently as 2017, more over 40% of DBT research was published in prestigious publications. In fact, since 2017, the average annual proportion of publications in the top tier to all publications is 62 percent. As a result, our findings imply that the standard of published research has generally kept up with the volume of publications.

Trends in digital banking publication since 1989

Publication activity by country

Our findings also show that DBT research has a truly global reach, as shown by the participation of authors from 65 different countries. Figure  3 gives a graphic representation of the top countries publishing DBT research. For better clarity, the study limited Fig.  3 to cover countries with more than five publications. Although the publication of digital banking is international, it is interesting to notice that a significant portion of the work originates from a limited group of wealthy nations. More specifically, more than 46% of all published DBT studies come from the USA, UK, India, China, Germany, Netherlands, Hon Kong, Romania, Finland, Poland, Ukraine, Italy and Spain. Only China and India are from emerging economies. Figure  3 illustrates publication activities by country.

Top publishing countries on DBT

Publishing activity by journal

Two hundred thirteen different journals published the 268 articles in our sample. Table 1 lists the top publishing Journals. Based on publication count, we found that the leading journals for DBT include Financial Innovation, Journal of Cleaner Production, Journal of Economics and Business, International Journal of Information Management, Journal of Information Technology and Sustainability Journal. Our observation revealed that even though the Journal of Financial Innovation had only two publications, it claimed the top spot with two hundred and twelve citations total citation, given an average citation of one hundred and six. This study also used Australian Business School Council (ABDC) rating & ranking. Journal quality is rated and ranked by ABDC, with A* being the highest-quality journal, followed by A and B as the second- and third-best journals, respectively. According to the ABDC ranking, journal C is the lowest ranked. The data available to us have shown that the high-quality journals in class A and A* are publishing works on digital transformation. Three of the top five journals in our data are in the A class.

Publishing activity by author and organization

According to [ 16 ], bibliometric methodologies can be used to evaluate the intellectual influence of universities and their research personnel. To determine the sources of digital transformation in banking, we assessed the research output of individual academics and institutions. We found 598 distinct writers from 224 organizations publishing on the subject of banking digital transformation inside our dataset. The top publishing scholars and institutions are listed in Tables 2 and 3 . The descriptive statistics also show that [ 17 , 18 , 19 , 20 ] are the authors with the highest citation. In addition, the Financial University under the government of the Russian Federation, Comsats University—Islamabad, National Chiao Tung University—China and the State University of Management—Russia are the top four.

Country collaboration and citation analysis

Country collaborations of co-authors analysis.

The UK is the most productive nation in terms of publishing changes in digital banking. Australia, Canada, Indonesia and the Russian Federation have the lowest populations. Figure  4 demonstrates that, with seven linkages and 18 times as many co-authorships, the UK has the highest level of collaboration. Countries like China, Hong Kong and the Netherlands, each with six links, tie for second place. The inflow of overseas students completing second and third degrees in the UK and the US may be one reason there are more significant connections between the two countries [ 21 ]. Additionally, the UK and China are two other significant technology superpowers laying the groundwork for digitization. This might have inspired and drawn academics to carry out studies in the area.

Country collaboration of co-authors analysis

Citation analysis

The most read articles in the field of research on DBT were found through citation analysis. Citation analysis examines the connections between publications and finds the most significant publications in a given study area [ 5 ]. Similar studies that used citation analysis based on the Scopus database have also been looked at research [ 21 ]. The authors' and the study's primary focus are analyzed based on their citations in Table 4 . The Financial Innovation Journal and Journal of Cleaner Production publish the most-cited article. Liu et al. [ 22 ] and Yip et al. are the authors of these articles [ 23 ]. Even though publications on the evolution of digital banking began in 1989, the most highly cited papers are in 2016 and 2018, respectively.

Cluster analysis (results of reference co-citation analysis with reference map)

By conducting the co-citation analysis of references as previously described and grouping the references cited by papers on DBT into clusters, we next looked at the intellectual foundation and structure of the DBT to answer the third research question. The 268 papers in our sample used 8720 different references in total. Our examination of co-citations revealed five interconnected clusters with a total of 67 articles. At least 20 of the 268 papers in our sample, which contained all 67 of these reference articles, collectively cited them. In other words, these 67 publications are the quantitatively most significant references in the literature on the shift of banking into the digital age. Similarly, we used the weighted citation count provided by VOS viewer to ensure high-quality articles in cluster analysis. We looked at the top 5 articles in each cluster as presented in Table 5 , to find a common topic, and we labeled each theme accordingly, following [ 24 ]. We summarize the findings of the five most influential studies in each cluster. In the following sections, we give a quick overview of these reference clusters and how they integrate into the larger framework for digital banking (Fig. 5 ).

Co-citation network of the reference map

Cluster 1: Digital banking innovation

A cluster that established its boundaries improved its theoretical relevance and defined it as the first and most noticeable cluster to arise. Therefore, it makes sense that [ 25 ] are the most important tenet of this fundamental research stream. In 2022, digital transformation will continue to be a crucial trend in banking. The financial services sector is slowly changing as a result of technology, just like how it has affected other economic sectors. Physical bank branches have historically served as the primary point of contact for facilitating customer and retail banking transactions, according to [ 25 ]. Customers are continuing to transition from in-person to digital transactions as technology advances because of a complementary influence brought about by more access to digital banking services and an improved experience of new digital access, goods, services and functionality. They have developed a novel mapping technique for FinTech developments that assesses the extent of changes and transformations in four subfields of financial services: operations management, technological advancements, multiple innovations, and blockchain and other FinTech innovations. According to [ 26 ], the current wave of mergers and acquisitions in the financial services sector, combined with the broad availability of sophisticated technology, has increased competitiveness in the sector. Also, Henseler et al. [ 27 ] used discriminant validity assessment analysis to establish relationships between latent variables in business transformation. The digital banking revolution cannot go without challenges. All innovations encounter client resistance, claims [ 28 ] tested hypotheses using binary logit models comparing mobile banking adopters versus non-adopters, mobile banking postponers versus rejecters and Internet banking postponers versus rejecters using data from two comprehensive national surveys conducted in Finland ( n  = 1736 consumers). The value barrier is the main obstacle to the adoption of online and mobile banking, according to the study's findings. He also discovered that age and gender strongly influence decisions to adopt or reject. When [ 29 ] looked at the effect of cognitive age in explaining older people's resistance to mobile banking, they discovered that traditional and image barriers had an impact on usage, value and risk. All impediments, in turn, have an impact on resistance behavior. Furthermore, cognitive age was found to moderate these relationships. In order words, younger elders have limited or no resistance to DBT as opposed to elderly ones. All writers in this cluster agree that technology and evolving customer demands dramatically affect how banks operate in the twenty-first century. Indeed, the coronavirus outbreak has made it clear that banking institutions need to speed up their digital transitions. But the banking sector needs to modify its business models for front-facing and back-office operations to keep up with the changes and avoid potential upheavals. True digital banking and a complete transformation are built on implementing the most recent technology, such as blockchain cloud computing and Internet of Things (IoT).

Cluster 2: FinTech and RegTech in Banking

Scholars in this cluster preoccupied themselves with the concept of FinTech (Financial Technology) and RegTech (Regulatory Technology) thus the application of emerging technology to improve the way businesses manage regulatory compliance). They provided a range of viewpoints to make the disruptive potential of FinTech and its consequences for a more thorough financial ecosystem application in the banking and financial ecosystem easier to understand. Despite the widespread agreement that FinTech will have a big impact on the financial services industry, little academic literature has examined this topic, according to [ 30 ], citing [ 8 ]. Kindly assist with the changes.. Additionally, no accepted definition of FinTech has yet been established. On the other hand, according to Google, the query what is FinTech is presently ranked seventh among the most popular FinTech-related questions (Google, 2016b). He gave the most up-to-date definition of FinTech, which is a new financial business that uses technology to enhance financial activity. Contrarily, RegTech, or regulatory technology, uses cutting-edge tools and methods to assist financial institutions in enhancing their regulatory governance, reporting, compliance and risk management. According to [ 31 ] research, many desirable results might certainly be attained if regulators were willing to implement cultural change and integrate technical improvements with regulation. Such outcomes can include stabilizing the financial system, fostering systemic stability. The disruptive invention by [ 31 ] has the potential to improve consumer welfare, regulatory and supervisory outcomes, and the financial services industry's reputation. According to [ 10 ], the traditional business models of retail banks are seriously threatened by the emergence of digital innovators in the financial services industry. Lee and Shin [ 8 ] who contend that FinTech ushers in a new paradigm in which information technology drives innovation in the financial industry endorse this point of view. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. The corporate world is quickly digitizing, shattering borders between industries, providing new opportunities and eliminating long-successful business models, according to [ 22 ], who added to the literature. They added that, on the plus side, growing digitalization presents opportunities, including the chance to take advantage of a solid customer connection and boost cross-selling. The dangers are typically precise and immediate, which is a drawback.

Cluster 3: The new digital business model of banks and other financial service providers

The papers in this cluster delved into the business model concept and, to a more significant extent, the new banking business model, which is technology-led. According to [ 32 ], business strategists and academics are paying more attention to business models as they try to understand how businesses create value and function well in order to gain a competitive advantage. Additionally, they argued that the digital economy had given businesses the chance to test out novel systems for networked value creation, where value is collaboratively produced by a firm and a big number of partners for a large number of users. The researchers came to the conclusion that four key themes are emerging, largely centered on the idea of the business model: as a new analytical unit, providing a systemic perspective on how to "do business," encompassing boundary-spanning activities (performed by a focal firm or others), and focusing on both value creation and value capture. These ideas are related and reinforce one another. Chesbrough [ 33 ] says that businesses must use their business models to commercialize novel concepts and technology. While businesses may make significant investments and have elaborate systems for investigating novel concepts and technologies, they frequently lack the ability to develop the business models that would be used to implement these inputs. He proposed that organizations should build the capacity to innovate their business models in order to make sound business decisions. Durkin et al. [ 34 ] did an excellent job investigating social media's role in a bank’s new digitally oriented business model. They suggested that social media had the power to profoundly alter customer-bank relationships and improve how the two sides communicate in the future. Their research shows that a wide range of clients regularly use transactional e-banking services. Loebbecke and Picot [ 35 ] presented a position paper that considers the factors driving how digitization and big data analytics drive the change of business and society. There is also discussion of the potential effects of digitalization and big data analytics on banking or employment, particularly in terms of cognitive work. Although several authors have recently proposed definitions of "business model," Shafer et al. [ 36 ] claim that none of them seem to be broadly recognized. This lack of agreement could be ascribed to the concept's interest from a variety of fields, all of which have connected it to something. To develop business models in the age of digital transformation, there must be an exponential shift in corporate culture and leadership concentration. The authors concur that banking is evolving as a result of a new wave of digital-only firms who are fragmenting the industry, componentizing products, and upending established business models. They claimed that switching from the previous business model to the new one is not the only way to succeed in this adaptable, fluid world. Instead, it will shift away from relying on a single, vertically integrated business model and toward a variety of non-linear models and value chain roles. In actuality, the Covid-19 epidemic has accelerated the development of business ecosystems for digital banking. Opportunities to develop, deliver and realize the value in new ways are made possible by digital technologies. The pipeline concept, the foundation of the classic universal bank, allows it to independently manufacture, sell and distribute products using its internal resources. This vertically integrated pipeline business model is disintegrating, making room for value chains that are becoming more fragmented and chances for new business models. A network of diverse business players from backgrounds including banking, insurance, pension, communications, real estate, education, healthcare service providers and IT are part of the new business model that the researchers have found. They work together to benefit each other through coexisting. The result of these developments and transformation is that financial services will continue to function in innovative and distinctive ways from those previously observed.

Cluster 4: Role of IT in banking

The fourth cluster concentrated on the crucial part information technology (IT) plays in the supply of financial services. According to [ 37 ], several banks have used information technology (IT) to provide consumers with a variety of more effective services. They think that in order to gain clients and boost profits in a cutthroat business environment, bank management must simultaneously use a variety of service channels. The majority of earlier research on IT investment in the banking sector has been on implementing cutting-edge IT-based service channels, including Internet banking, from the perspectives of clients [ 37 ]. From the standpoint of the bank, Barkhordari et al. [ 37 ] demonstrate that IT has a beneficial effect on performance by taking into account both the conventional physical and alternative IT-based service channels at once. They came to the conclusion that the purpose of using IT-related tools in banking is to forward a strategic, transformative objective. Due to the advancement of modern IT, the relationship between banks and their customers has changed substantially over the past few decades. They claimed that some of the examples include well-known innovations such as automated teller machines (ATMs), online banking (e-banking), and straight-through processing (STP), as well as others that have not (yet) gained widespread adoption, such as electronic cash (e-cash), or electronic bill presentment and payment (EBPP). At least the first has changed how people and businesses manage their finances and had an impact on the entire sector. They outlined how the aforementioned advances needed structures that took trends into account and might broaden the scope of current bank architectures to include horizontal and vertical integration dimensions. According to [ 38 ], enterprise architecture is typically represented by the following layers and design objects:

Product/services, market segments, corporate strategy goals, strategic plans/projects and interactions with customers and suppliers are all included in the strategic layer.

Organizational layer: Information flows, organizational units, roles/responsibilities, sales channels and business processes.

Applications, application domains, business services, IS functionalities, information objects, and interfaces make up the integration layer.

Software layer: programs, data structures, etc.

Hardware components, network components, and software platforms make up the IT infrastructure layer.

When it comes to transformations, architectures are really useful, because they integrate many layers. Creating new businesses or reorganizing old ones is transformation.

According to [ 32 ], organizations that are successful over the long term have basic principles and purposes that never change while continuously adapting their business strategies and operations to the external environment. IT's penetration of the banking industry falls under this category of business change. Liu et al. [ 22 ] contributed to the conversation by asserting that technological advancements like high-frequency trading systems (HFT) and algorithmic trading systems had altered the financial markets. The point is that information technology (IT) makes it possible to design complex products, improve market infrastructure, apply adequate risk management strategies and aid financial intermediaries in reaching geographically remote and diverse markets. The Internet has considerably impacted the delivery methods used by banks. The Internet has become an essential medium for distributing banking services and goods.

Cluster 5: Response to DBT

This fifth and final cluster considered the attitude of staff and clients toward DBT. If computer systems are not utilized, they cannot increase organizational performance. Unfortunately, managers' and professionals' opposition to end-user technology is a common issue. We need to comprehend why people accept or reject computers in order to better forecast, explain and promote user acceptance. The findings point to the potential for straightforward yet effective models of user acceptance factors, with practical utility for assessing systems and directing managerial actions aimed at addressing the issue of underutilized computer technology. Agarwal and Prasad [ 39 ] assert that a recent lack of user adoption of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and increases in productivity. They continued by saying that the academic and professional sectors had grown concerned about this paradoxical connection between spending on information technology and increases in productivity. The axiom that systems that are not used generate little value is an often proposed explanation for this relationship. Therefore, in order to achieve the expected productivity advantage, it is not enough to simply have the technology available; it must also be accepted and used effectively by its target user group [ 39 ]. The work of DeLone and McLean threw more light on technology acceptance. When [ 32 ] created a thorough taxonomy, they provided a more comprehensive picture of the concept of information system success. Six main characteristics or categories of the success of information systems are proposed by this taxonomy: system quality, information quality, utilization, user satisfaction, individual impact and organizational impact. Meanwhile, further discussions in this cluster have given more insights into customer acceptance or otherwise of IT in banking. Perceived utility, perceived ease of use, trust and perceived enjoyment are discovered to be immediate direct drivers of customers' views toward utilizing Internet banking, according to [ 40 , 41 ] research. This finding is consistent with some of the findings of other studies. The clients' behavioral intentions to utilize Internet banking are determined by attitude, perceived risk, fun, and confidence. Although the perceived website design has a direct impact only on perceived usability, its indirect effects on perceived usefulness, attitude and behavioral intentions are considerable. Perceived enjoyment only has a short-term impact on perceived ease of use, but both a direct and indirect influence on perceived usefulness. Customer experience is at the heart of the digital banking transition. Therefore, banks must continuously innovate products, integrate cutting-edge technology and add value for their clients.

Keywords analysis

The trends in the keywords displayed in multiple studies can be used to determine the main study direction for upcoming investigations [ 42 ]. The VOSviewer r software, which has previously been utilized by other writers, is employed in this study to extract the author's keywords [ 12 , 21 , 43 ]. A co-occurrences network is produced by the VOS viewer program as a dimensional map [ 12 ]. We used bibliographical author keyword analysis to examine our sample and determine whether there was any increasing or declining themes of interest per research question four. We discovered that writers of the 268 publications in our sample employed 829 keywords to indicate their scientific work, meeting the studies' threshold. Only 26 words, or around 3% of the total, were used at least four times. Our findings imply that the literature on DBT is incredibly heterogeneous. Indeed, according to the results of most recent articles, 80 percent of the authors' specified keywords were utilized precisely once. However, there are several keywords that authors frequently utilize to describe their works (Fig.  6 ). FinTech is the most often used keyword, with 25 occurrences and 29 links to other keywords, followed by digitalization, with 18 and 20 links. Reporting on Digital Transformation contains 13 instances and 18 links. The bibliometric map of author keywords is shown in Fig.  6 .

Bibliometric map of author keywords co-occurrence with five minimum occurrences and overlay visualization mode

The theme areas contemporary academics focus on can be seen by closely examining the map. The use of bibliographic coupling is based on the subject the authors are investigating. The digital transformation of financial service delivery was investigated by [ 43 ] from the perspective of Nigeria about chatbot adoption. A moderated mediated model was used by [ 44 ] to examine how blockchain technology was adopted in the financial sector during the fourth industrial revolution. Additionally, Karjaluoto et al. [ 19 ] looked at how users' perceptions of value influence their use of mobile financial services apps. Similarly, Podsakoff et al. [ 16 ] focused on enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Taking the discussion to a different dimension, Teng and Khong [ 45 ] worked on Examining actual consumer usage of E-wallets: A case study of big data analytics. David-West et al. [ 46 ] examined sustainable business models to create mobile financial services in Nigeria. Yip and Bocken [ 23 ] deepened the discussion and, in turn, looked at Sustainable business model archetypes for the banking industry. Finally, Niemand et al. [ 20 ] highlighted digitalization in the financial sector: a backup plan with a strategic focus on digitalization and an entrepreneurial attitude. Future research on financial services provided via e-wallets and mobile banking is the main emphasis of the second cluster. Authors are still studying entrepreneurship and digitalization in the supply of financial services. Future research is required in these areas of study because blockchain technology and digital currency are also gaining traction in the literature. The most popular search terms and the number of times they were used are displayed in Table 6 .

Discussions and future research agenda

The first paper on DBT was published by [ 3 ], and since then, both its audience and popularity have grown. Yet, the rapid rise in total publications across a wide range of specialist areas, notably during the last five years, has made it increasingly difficult for academics to ascertain the intellectual structure of the field. Existing qualitative assessments, which usually only address a small fraction of Digital Transformation in Banking while failing to accurately capture the entire body of work, have in some ways made the problem of theoretical specificity worse. It is rather tricky for a qualitative evaluation to describe more than 260 works over three decades. Thus, our research fills a critical vacuum in the literature by thoroughly (and quantitatively) mapping the digital banking domain, documenting its conceptual structure and suggesting its most likely future orientations. The theoretical underpinnings from which they have been developed, the subtopics and subthemes they have written about, and the notable historical contributors to DBT study (such as scholars, schools, and journals) are all identified in our work over time. Overall, our findings imply a considerable worldwide impact of digitization on banking, making it a truly global study paradigm. Additionally, the high number of citations for recent works shows that there is a great need for more research utilizing the DBT theoretical framework, suggesting that the field of study will continue to advance for a very long period. The study's structure is based on a wide range of goals and inquiries.

The initial research question aimed to characterize the increase in publication (document by year and county) and productivity of journals in terms of citations, top authors and institutions of studies on DBT. According to the data that are currently available, 174 papers, or 72% of all scientific publications, were published in the last six years, from 2016 to 2022. Also, prestigious journals carried out more than 40% of the publications. Therefore, our data imply that the quantity and quality of published research have typically stayed up. Our data also show that the research on the DBT is genuinely global in scope, as seen by the contributions of authors from 65 different countries. China and the UK are split equally, with India coming in second. It is essential to add that the BRIC (Brazil, Russia, India and China) countries perform well with publications. African countries like Ghana and Nigeria are equally showing promising signs of publications in this light. Regarding journal productivity, the study has revealed that articles on the banking industry's digital transformation are published in high-caliber journals in the A and A* classes. In our statistics, three top-five journals fall into the A category. These are the International Journal of Information Management (A*), Journal of Information Technology (A*), and Journal of Cleaner Production (A). We found 598 distinct writers from 224 organizations publishing on the subject of DBT inside our dataset. The descriptive statistics also reveal that Ranti et al. (2020) have the most citations, while the Financial University of the Government of the Russian Federation is the most productive institution in terms of the DBT, with seven publications.

The second research topic analyzes the co-authorship analysis and citation analysis by nation of authorship. Figure  3 shows that the UK has the maximum amount of collaboration, with 16 links and 18 co-authorships. China, Hong Kong and the Netherlands tie for second place with six linkages each. The increase in foreign students seeking second and third degrees in the UK and China may be one factor fostering closer ties between the two countries [ 21 ]. The UK and China are two other critical technological superpowers establishing the foundation for digitization. This might have attracted scholars and prompted them to conduct studies in the area. Future research might study the effects of digitization on banking on enforcing public and private sector regulations in emerging nations like Africa.

The third research question assesses the intellectual structure of the knowledge of DBT. This result was attained through citation analysis. Finding the most important publications in a specific field of study through citation analysis involves looking at the relationships between publications [ 5 ]. The primary point of contact for enabling retail banking and consumer transactions in the past has been actual bank branches. Customers are still transitioning from in-person to digital transactions as technology develops thanks to a complimentary effect brought on by increased access to digital banking services as well as an improved user experience of new digital access products, services and an improved user interface. Further research revealed that the banking sector's transition to digitization had increased competitiveness among service providers. The citation analysis highlighted the impact of FinTech on financial services innovations. According to [ 8 ], FinTech ushers in a new paradigm where information technology drives innovation in the financial sector. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. We discovered that the corporate world is rapidly digitizing, removing industry barriers, opening up new opportunities, and dismantling long-established business structures. The concept of a business model and, to a greater extent, the new banking business model was also included in the analysis. The authors proposed that businesses build the capacity to innovate their business models since it makes good business sense. For instance, it has been seen that social media is significantly influencing the business models of some digitally focused banks. Social media, according to some, has the power to radically alter customer–bank interactions and improve how the two sides communicate in the future. If banks are to have an impact, they must transition from relying on a single, vertically integrated business model to multiple non-linear models and roles in the value chain. As a result of these developments and transformations, financial services will continue to operate in novel and unique ways from those previously observed. The study has proven beneficial for the use of IT in banking. IT-related tools are used in banking to advance a strategic transformational goal. The connection between banks and their customers has altered significantly over the past few decades with the development of contemporary IT. The most prevalent enterprise architecture layers and design items, according to [ 38 ], are the strategic, organizational, integration, software and IT infrastructure. It has been established that information technology (IT) enables the development of complicated products, enhances market infrastructure, implements efficient risk management techniques and enables financial intermediaries to access diverse and geographically dispersed markets. Despite the enormous advantages of digital banking, opinions on the systems are widely divided. Agarwal and Prasad [ 39 ] claim that a recent lack of user acceptance of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and productivity increases. They said that the counterintuitive connection between productivity increases and information technology investments had alarmed academic and professional groups. According to theories advanced by academics, digital technology, in general, and information systems, in particular, must fall under one of the following taxonomies to be accepted and used: system effectiveness, accuracy of the data, usability, user happiness, personal effect and organizational effect. The fourth research question looked at the future directions and emerging research themes and trends in studies of the digital banking transition. Future scholars are still interested in business models, FinTech, and DBT or banking. Additionally, the focus of the conversation is rapidly shifting to emerging and developing economies. Nevertheless, contemporary research areas include blockchain [ 44 ], mobile financial services apps [ 19 ], artificial intelligence and mobile banking service platforms [ 47 ], and sustainable business models [ 46 ]. The importance of highlighting the need for additional research in these fields of study cannot be overstated, given the growing popularity of blockchain technology and digital currency in literature.

Implications for theory

At least four substantial contributions to the body of DBT research, in our opinion, have been made by this study. We contribute primarily by expanding on current DBT reviews. While other reviewers have used qualitative methodologies, we may supplement and expand on such assessments by utilizing a thorough bibliometric study, allowing us to be more explicit about DBT's intellectual progress and structure. This is significant because it gives us a unique opportunity to highlight notable contributors and pinpoint the present and past origins of DBT research. Second, our quantitative analysis of bibliographic data demonstrates how DBT research has developed into its paradigm, which is supported by the original article by Bürk and Pfitzmann [ 3 ]. Third, we make a contribution by detecting rising and negative trends in subtopic areas, so identifying the subjects that are most likely to be studied in the future by academics. Fourth, by conducting a comprehensive assessment of DBT, we pinpoint areas where theory and practice diverge and evaluate the ways in which researchers have aided practitioners by modernizing DBT to comprehend and foresee the difficulties of "real-world" business.

Implications for practice

The banking sector, like other sectors, aspires to embrace contemporary practices and incorporate digital technologies into its operational procedures. This complicated collection of measures necessitates a methodical and considered approach, particularly in financial services where substantial sums of money and severe risks are at stake. DBT in this sense refers to several adjustments made to the banking sector to integrate different FinTech technologies to automate, optimize, and digitize procedures and improve data security. The processes and technologies employed in the financial industry will alter due to several small and significant changes implied by this process. The fundamental tendency of digital transformation, regardless of industry, is the integration of computer technologies, and Statista's analysis indicates that this trend will continue to expand. The challenges posed by introducing new digital innovations must be understood by stakeholders, who must also articulate solutions. Again, embracing digital technologies will involve taking on several tremendous risks; for this reason, bank executives must simultaneously establish and implement a strategy for managing those risks. If regulators utilizing technology to oversee and control the industry want to ensure solid financial stability in the economy, they must constantly be ahead of innovation risk with appropriate countermeasures. Digital banking involves the collection and processing of vast volumes of customer data. This raises the issue of data protection following regulations and international best practices. The DBT's third useful outcome is that it prompts organizational leaders to consider how their personal biases—which are the products of their histories, characteristics and experiences—might influence opinions and, ultimately, bank performance.

Limitations

We know that no study is faultless, and ours has its setbacks. While we made every effort to minimize problems, we nevertheless expect to offer insightful suggestions for future bibliometric and DBT studies. First, we used the Scopus database, a popular database used in bibliometric research, to gather our bibliometric data [ 48 ]. Even though Scopus contains the most data sources, it does not include all research databases on the transformation of digital banking. Furthermore, because this database has so many uses, using Scopus for data collection could likely lead to mistakes that show up when performing bibliometric analysis. To put it another way, errors might have happened if articles were mislabeled, and it is possible that the database completely missed publications important to our study [ 49 ]. To address this potential issue, we followed the best bibliometric analysis methods. For instance, we thoroughly purged duplicates and other forms of incorrect items from our data. Additionally, this research is restricted to English-language publications, and the subject only includes business, management, finance, economics, FinTech and banking digitalization. The data search will be enhanced, and the search restriction will be reduced using several databases.

This article assesses the intellectual landscape and future potential of the field of DBT research, as well as the influence of that research. The approach for this study is based on descriptive analysis, performance analysis and science mapping analysis, and it employs bibliometric analysis. The set was created based on 268 documents from the Scopus database that span the years 1989 to 2022. We demonstrate that DBT has continued to be a hot topic for academic research approximately three decades after its conception. Our findings also indicate that the UK, USA, Germany and China are the countries that have conducted most of the studies on the DBT. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. We further categorize the body of research on DBT into five main clusters, including (1) Digital Banking Innovation, (2) FinTech and RegTech in Banking, (3) The New Digital Business Model of Banks and Other Financial Service Providers, (4) The role of IT in banking, (5) Response to DBT. Due to a significant influx of international students, the UK, China and Hong Kong continue to be the most collaborative countries. Additional research reveals that papers rated with A* and A grades frequently publish studies on DBT. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. FinTech, block chain mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models are currently researched. Given the rising popularity of block chain technology and digital money in the literature, highlighting the need for more research in these areas of study cannot be overstated. This study builds on previous reviews by objectively charting the inception and intellectual growth of the digital banking area and evaluating its future possibilities. In essence, this bibliometric study offers a distinct and original viewpoint on the evolution of DBT by carefully and objectively assessing prior material and concurrently offering a clear road map for future work.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author upon request.

Abbreviations

Digital banking transformation

Financial technology

Regulatory technology

Internet of things

Automatic teller machine

Artificial intelligence

Information technology

Information communication technology

Straight through processing

Electronic banking

Electronic cash

Electronic bill presentment and payment

High-frequency trading system

Electronic wallets

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Acknowledgements

The authors would like to graciously thank the Editor-in-Chief and the editorial team, and the two anonymous reviewers for their feedback in developing this paper. The writers also acknowledge Prof. Alfred Owusu, Dean of KsTU's Business School, for his guidance, inspiration and support. We appreciate his inventiveness and how it enabled us to clearly define the goal and possibilities of this effort. The authors also appreciate the helpful advice provided by Dr. Thomas Adomah Worae and Prof. Abdul-Aziz Iddrisu as we worked on the first versions of the manuscript. Finally, we would like to thank Riya Sureka, a research scholar at the Malaviya National Institute of Technology in Jaipur, India, for his advice on how to analyze bibliometric data using the ‘R’ and VOS viewer software.

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All authors contributed significantly to the development of this article; LK generated the title, wrote the introduction, collection and analysis of the data, interpreted the co-citation analysis and put the manuscript together. YC reviewed the existing to conceptualize the study, reviewed the study and expanded the analysis. KM involved data generation from Scopus data base, software running, data analysis and review of the work. All authors read and approved the final manuscript.

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Lambert Kofi Osei holds a masters of business administration (finance option) degree from the Kwame Nkrumah University of Science and Technology. He is currently a PhD finance and banking student of Siberia Federal University, Russia. He is currently a lecturer at the Department of Banking Technology and Finance—Kumasi Technical University—in Ghana. He also holds an associated charted membership with the Chartered Institute of Securities and Investment—UK. Osei is certified expert in microfinance (CEMF) from the Frankfurt School of Finance—Germany. Osei has had considerable level of industry experience, with over 12 years managerial experience in the banking industry in Ghana including been the chief executive officer of Eman Capital. Prior to joining Kumasi Technical University, he was the National Chairman of Ghana Association of Microfinance Companies (GAMC)—an umbrella body of all microfinance companies in Ghana. Despite joining academia recently, Osei has made two publications of his work and a lot more articles are under completion stage to be sent for review. It is the goal of him to be an authority in the field of digital banking to impact businesses and societies.

Yuliya Cherkasova holds Ph.D. in economics and is a associate professor, School of Economics, Finance and Public Administration, Siberian Federal University. She is the chair of Digital Financial Technologies of Sberbank of Russia. Her research interests include banking prudential regulation of banks, digital economy and public finance. As a researcher, she has published more than 70 articles, 10 textbooks on topics, related finance and banking aria.

Kofi Mintah Oware has a Ph.D. in business administration (sustainability finance and management) from Mangalore University, India, and an MBA degree from Aberdeen Business School (Robert Gordon University—UK). He is currently a senior lecturer in the department of banking technology and finance. He is also a chartered accountant with membership from the Institute of Chartered Accountants (ICA), Ghana, and Institute of Cost Executive & Accountants (ICEA)—UK. Before joining academia, he worked in blue-chip companies for 12 years in various capacities, including chief accountant, head of finance and general manager for finance & administration in Ghana and research consultant to Aberdeen Businesswomen network in the UK. Among his key roles during industry experience include representing management in union negotiations and presenting the firm's financial reports in the corporate board meeting. In academia, he has 34 publications in various journal, including two "A" s under ABDC (Meditari Accountancy Research), three "B" s under ABDC (Social Responsibility Journal & Society and Business Review) and one C (South Asian Journal of Business Studies) all with Emerald publications. Also, he has 10 academic papers in various journals under review.

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Osei, L.K., Cherkasova, Y. & Oware, K.M. Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend. Futur Bus J 9 , 30 (2023). https://doi.org/10.1186/s43093-023-00207-2

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Deep learning in finance and banking: A literature review and classification

• Jian Huang 1 ,
• Junyi Chai   ORCID: orcid.org/0000-0003-1560-845X 2 &
• Stella Cho 2  

Frontiers of Business Research in China volume  14 , Article number:  13 ( 2020 ) Cite this article

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Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Introduction

Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. As a promising branch of artificial intelligence, DL has attracted great attention in recent years. Compared with conventional ML techniques such as support vector machine (SVM) and k-nearest neighbors (kNN), DL possesses advantages of the unsupervised feature learning, a strong capability of generalization, and a robust training power for big data. Currently, DL has been applied comprehensively in classification and prediction tasks, computer visions, image processing, and audio-visual recognition (Chai and Li 2019 ). Although DL was developed in the field of computer science, its applications have penetrated diversified fields such as medicine, neuroscience, physics and astronomy, finance and banking (F&B), and operations management (Chai et al. 2013 ; Chai and Ngai 2020 ). The existing literature lacks a good overview of DL applications in F&B fields. This study attempts to bridge this gap.

While DL is the focus of computer vision (e.g., Elad and Aharon 2006 ; Guo et al. 2016 ) and natural language processing (e.g., Collobert et al. 2011 ) in the mainstream, DL applications in F&B are developing rapidly. Shravan and Vadlamani (2016) investigated the tools of text mining for F&B domains. They examined the representative ML algorithms, including SVM, kNN, genetic algorithm (GA), and AdaBoost. Butaru et al. ( 2016 ) compared performances of DL algorithms, including random forests, decision trees, and regularized logistic regression. They found that random forests gained the highest classification accuracy in the delinquency status.

Cavalcante et al. ( 2016 ) summarized the literature published from 2009 to 2015. They analyzed DL models, including multi-layer perceptron (MLP) (a fast library for approximate nearest neighbors), Chebyshev functional link artificial NN, and adaptive weighting NN. Although the study constructed a prediction framework in financial trading, some notable DL techniques such as long short-term memory (LSTM) and reinforcement learning (RL) models are neglect. Thus, the framework cannot ascertain the optimal model in a specific condition.

The reviews of the existing literature are either incomplete or outdated. However, our study provides a comprehensive and state-of-the-art review that could capture the relationships between typical DL models and various F&B domains. We identified critical conditions to limit our collection of articles. We employed academic databases in Science Direct, Springer-Link Journal, IEEE Xplore, Emerald, JSTOR, ProQuest Database, EBSCOhost Research Databases, Academic Search Premier, World Scientific Net, and Google Scholar to search for articles. We used two groups of keywords for our search. One group is related to the DL, including “deep learning,” “neural network,” “convolutional neural networks” (CNN), “recurrent neural network” (RNN), “LSTM,” and “RL.” The other group is related to finance, including “finance,” “market risk,” “stock risk,” “credit risk,” “stock market,” and “banking.” It is important to conduct cross searches between computer-science-related and finance-related literature. Our survey exclusively focuses on the financial application of DL models rather than other DL models like SVM, kNN, or random forest. The time range of our review was set between 2014 and 2018. In this stage, we collected more than 150 articles after cross-searching. We carefully reviewd each article and considered whether it is worthy of entering our pool of articles for review. We removed the articles if they are not from reputable journals or top professional conferences. Moreover, articles were discarded if the details of financial DL models presented were not clarified. Thus, 40 articles were selected for this review eventually.

This study contributes to the literature in the following ways. First, we systematically review the state-of-the-art applications of DL in F&B fields. Second, we summarize multiple DL models regarding specified F&B domains and identify the optimal DL model of various application scenarios. Our analyses rely on the data processing methods of DL models, including preprocessing, input data, and evaluation rules. Third, our review attempts to bridge the technological and application levels of DL and F&B, respectively. We recognize the features of various DL models and highlight their feasibility toward different F&B domains. The penetration of DL into F&B is an emerging trend. Researchers and financial analysts should know the feasibilities of particular DL models toward a specified financial domain. They usually face difficulties due to the lack of connections between core financial domains and numerous DL models. This study will fill this literature gap and guide financial analysts.

The rest of this paper is organized as follows. Section 2 provides a background of DL techniques. Section 3 introduces our research framework and methodology. Section 4 analyzes the established DL models. Section 5 analyzes key methods of data processing, including data preprocessing and data inputs. Section 6 captures appeared criteria for evaluating the performance of DL models. Section 7 provides a general comparison of DL models against identified F&B domains. Section 8 discusses the influencing factors in the performance of financial DL models. Section 9 concludes and outlines the scope for promising future studies.

Background of deep learning

Regarding DL, the term “deep” presents the multiple layers that exist in the network. The history of DL can be traced back to stochastic gradient descent in 1952, which is employed for an optimization problem. The bottleneck of DL at that time was the limit of computer hardware, as it was very time-consuming for computers to process the data. Today, DL is booming with the developments of graphics processing units (GPUs), dataset storage and processing, distributed systems, and software such as Tensor Flow. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN). All of these models have greatly contributed to the applications in F&B.

The basic structure of NN can be illustrated as Y  =  F ( X T w  +  c ) regarding the independent (input) variables X , the weight terms w , and the constant terms c . Y is the dependent variable and X is formed as an n  ×  m matrix for the number of training sample n and the number of input variables m . To apply this structure in finance, Y can be considered as the price of next term, the credit risk level of clients, or the return rate of a portfolio. F is an activation function that is unique and different from regression models. F is usually formulated as sigmoid functions and tanh functions. Other functions can also be used, including ReLU functions, identity functions, binary step functions, ArcTan functions, ArcSinh functions, ISRU functions, ISRLU functions, and SQNL functions. If we combine several perceptrons in each layer and add a hidden layer from Z 1 to Z 4 in the middle, we term a single layer as a neural network, where the input layers are the X s , and the output layers are the Y s . In finance, Y can be considered as the stock price. Moreover, multiple Y s are also applicable; for instance, fund managers often care about future prices and fluctuations. Figure  1 illustrates the basic structure.

The structure of NN

Based on the basic structure of NN shown in Fig.  1 , traditional networks include DNN, backpropagation (BP), MLP, and feedforward neural network (FNN). Using these models can ignore the order of data and the significance of time. As shown in Fig.  2 , RNN has a new NN structure that can address the issues of long-term dependence and the order between input variables. As financial data in time series are very common, uncovering hidden correlations is critical in the real world. RNN can be better at solving this problem, as compared to other moving average (MA) methods that have been frequently adopted before. A detailed structure of RNN for a sequence over time is shown in Part B of the Appendix (see Fig. 7 in Appendix ).

The abstract structure of RNN

Although RNN can resolve the issue of time-series order, the issue of long-term dependencies remains. It is difficult to find the optimal weight for long-term data. LSTM, as a type of RNN, added a gated cell to overcome long-term dependencies by combining different activation functions (e.g., sigmoid or tanh). Given that LSTM is frequently used for forecasting in the finance literature, we extract LSTM from RNN models and name other structures of standard RNN as RNN(O).

As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including CNN and RL, as well as Latent variable models such as variational autoencoders and generative adversarial network. Table 6 in Appendix shows a legend note to explain the abbreviations used in this paper. We summarize the relationship between commonly used DL models in Fig.  3 .

Relationships of reviewed DL models for F&B domains

Research framework and methodology

Our research framework is illustrated in Fig.  4 . We combine qualitative and quantitative analyses of the articles in this study. Based on our review, we recognize and identify seven core F&B domains, as shown in Fig.  5 . To connect the DL side and the F&B side, we present our review on the application of the DL model in seven F&B domains in Section 4. It is crucial to analyze the feasibility of a DL model toward particular domains. To do so, we provide summarizations in three key aspects, including data preprocessing, data inputs, and evaluation rules, according to our collection of articles. Finally, we determine optimal DL models regarding the identified domains. We further discuss two common issues in using DL models for F&B: overfitting and sustainability.

The research framework of this study

The identified domains of F&B for DL applications

Figure  5 shows that the application domains can be divided into two major areas: (1) banking and credit risk and (2) financial market investment. The former contains two domains: credit risk prediction and macroeconomic prediction. The latter contains financial prediction, trading, and portfolio management. Prediction tasks are crucial, as emphasized by Cavalcante et al. ( 2016 ). We study this domain from three aspects of prediction, including exchange rate, stock market, and oil price. We illustrate this structure of application domains in F&B.

Figure  6 shows a statistic in the listed F&B domains. We illustrate the domains of financial applications on the X-axis and count the number of articles on the Y-axis. Note that a reviewed article could cover more than one domain in this figure; thus, the sum of the counts (45) is larger than the size of our review pool (40 articles). As shown in Fig.  6 , stock marketing prediction and trading dominate the listed domains, followed by exchange rate prediction. Moreover, we found two articles on banking credit risk and two articles on portfolio management. Price prediction and macroeconomic prediction are two potential topics that deserve more studies.

A count of articles over seven identified F&B domains

Application of DL models in F&B domains

Based on our review, six types of DL models are reported. They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Regarding FNN, several papers use the alternative terms of backpropagation artificial neural network (ANN), FNN, MLP, and DNN. They have an identical structure. Regarding RNN, one of its well-known models in the time-series analysis is called LSTM. Nearly half of the reviewed articles apply FNN as the primary DL technique. Nine articles apply LSTM, followed by eight articles for RL, and six articles for RNN. Minor ones that are applied in F&B include CNN, DBM, and RBM. We count the number of articles that use various DL models in seven F&B domains, as shown in Table  1 . FNN is the principal model used in exchange rate, price, and macroeconomic predictions, as well as banking default risk and credit. LSTM and FNN are two kinds of popular models for stock market prediction. Differently, RL and FNN are frequently used regarding stock trading. FNN, RL, and simple RNN can be conducted in portfolio management. FNN is the primary model in macroeconomic and banking risk prediction. CNN, LSTM, and RL are emerging research approaches in banking risk prediction. The detailed statistics that contain specific articles can be found in Table 5 in Appendix .

Exchange rate prediction

Shen et al. ( 2015 ) construct an improved DBN model by including RBM and find that their model outperforms the random walk algorithm, auto-regressive-moving-average (ARMA), and FNN with fewer errors. Zheng et al. ( 2017 ) examine the performance of DBN and find that the DBN model estimates the exchange rate better than FNN model does. They find that a small number of layer nodes engender a more significant effect on DBN.

Several scholars believe that a hybrid model should have better performance. Ravi et al. ( 2017 ) contribute a hybrid model by using MLP (FNN), chaos theory, and multi-objective evolutionary algorithms. Their Chaos+MLP + NSGA-II model Footnote 1 has a mean squared error (MSE) with 2.16E-08 that is very low. Several articles point out that only a complicated neural network like CNN can gain higher accuracy. For example, Galeshchuk and Mukherjee ( 2017 ) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA). However, they find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.

Stock market prediction

In stock market prediction, some studies suggest that market news may influence the stock price and DL model, such as using a magic filter to extract useful information for price prediction. Matsubara et al. ( 2018 ) extract information from the news and propose a deep neural generative model to predict the movement of the stock price. This model combines DNN and a generative model. It suggests that this hybrid approach outperforms SVM and MLP.

Minh et al. ( 2017 ) develop a novel framework with two streams combining the gated recurrent unit network and the Stock2vec. It employs a word embedding and sentiment training system on financial news and the Harvard IV-4 dataset. They use the historical price and news-based signals from the model to predict the S&P500 and VN-index price directions. Their model shows that the two-stream gated recurrent unit is better than the gated recurrent unit or the LSTM. Jiang et al. ( 2018 ) establish a recurrent NN that extracts the interaction between the inner-domain and cross-domain of financial information. They prove that their model outperforms the simple RNN and MLP in the currency and stock market. Krausa and Feuerriegel ( 2017 ) propose that they can transform financial disclosure into a decision through the DL model. After training and testing, they point out that LSTM works better than the RNN and conventional ML methods such as ridge regression, Lasso, elastic net, random forest, SVR, AdaBoost, and gradient boosting. They further pre-train words embeddings with transfer learning (Krausa and Feuerriegel 2017 ). They conclude that better performance comes from LSTM with word embeddings. In the sentiment analysis, Sohangir et al. ( 2018 ) compares LSTM, doc2vec, and CNN to evaluate the stock opinions on the StockTwits. They conclude that CNN is the optimal model to predict the sentiment of authors. This result may be further applied to predict the stock market trend.

Data preprocessing is conducted to input data into the NN. Researchers may apply numeric unsupervised methods of feature extraction, including principal component analysis, autoencoder, RBM, and kNN. These methods can reduce the computational complexity and prevent overfitting. After the input of high-frequency transaction data, Chen et al. ( 2018b ) establish a DL model with an autoencoder and an RBM. They compare their model with backpropagation FNN, extreme learning machine, and radial basis FNN. They claim that their model can better predict the Chinese stock market. Chong et al. ( 2017 ) apply the principal component analysis (PCA) and RBM with high-frequency data of the South Korean market. They find that their model can explain the residual of the autoregressive model. The DL model can thus extract additional information and improve prediction performance. More so, Singh and Srivastava ( 2017 ) describe a model involving 2-directional and 2-dimensional (2D 2 ) PCA and DNN. Their model outperforms 2D 2 with radial basis FNN and RNN.

For time-series data, sometimes it is difficult to judge the weight of long-term and short-term data. The LSTM model is just for resolving this problem in financial prediction. The literature has attempted to prove that LSTM models are applicable and outperform conventional FNN models. Yan and Ouyang ( 2017 ) apply LSTM to challenge the MLP, SVM, and kNN in predicting a static and dynamic trend. After a wavelet decomposition and a reconstruction of the financial time series, their model can be used to predict a long-term dynamic trend. Baek and Kim ( 2018 ) apply LSTM not only in predicting the price of S&P500 and KOSPI200 but also in preventing overfitting. Kim and Won ( 2018 ) apply LSTM in the prediction of stock price volatility. They propose a hybrid model that combines LSTM with three generalized autoregressive conditional heteroscedasticity (GARCH)-type models. Hernandez and Abad ( 2018 ) argue that RBM is inappropriate for dynamic data modeling in the time-series analysis because it cannot retain memory. They apply a modified RBM model called p -RBM that can retain the memory of p past states. This model is used in predicting market directions of the NASDAQ-100 index. Compared with vector autoregression (VAR) and LSTM, notwithstanding, they find that LSTM is better because it can uncover the hidden structure within the non-linear data while VAR and p -RBM cannot capture the non-linearity in data.

CNN was established to predict the price with a complicated structure. Making the best use of historical price, Dingli and Fournier ( 2017 ) develop a new CNN model. This model can predict next month’s price. Their results cannot surpass other comparable models, such as logistic regression (LR) and SVM. Tadaaki ( 2018 ) applies the financial ratio and converts them into a “grayscale image” in the CNN model. The results reveal that CNN is more efficient than decision trees (DT), SVM, linear discriminant analysis, MLP, and AdaBoost. To predict the stock direction, Gunduz et al. ( 2017 ) establish a CNN model with a so-called specially ordered feature set whose classifier outperforms either CNN or LR.

Stock trading

Many studies adopt the conventional FNN model and try to set up a profitable trading system. Sezer et al. ( 2017 ) combine GA with MLP. Chen et al. ( 2017 ) adopt a double-layer NN and discover that its accuracy is better than ARMA-GARCH and single-layer NN. Hsu et al. ( 2018 ) equip the Black-Scholes model and a three-layer fully-connected feedforward network to estimate the bid-ask spread of option price. They argue that this novel model is better than the conventional Black-Scholes model with lower RMSE. Krauss et al. ( 2017 ) apply DNN, gradient-boosted-trees, and random forests in statistical arbitrage. They argue that their returns outperform the market index S&P500.

Several studies report that RNN and its derivate models are potential. Deng et al. ( 2017 ) extend the fuzzy learning into the RNN model. After comparing their model to different DL models like CNN, RNN, and LSTM, they claim that their model is the optimal one. Fischer and Krauss ( 2017 ) and Bao et al. ( 2017 ) argue that LSTM can create an optimal trading system. Fischer and Krauss ( 2017 ) claim that their model has a daily return of 0.46 and a sharp ratio of 5.8 prior to the transaction cost. Given the transaction cost, however, LSTM’s profitability fluctuated around zero after 2010. Bao et al. ( 2017 ) advance Fischer and Krauss’s ( 2017 ) work and propose a novel DL model (i.e., WSAEs-LSTM model). It uses wavelet transforms to eliminate noise, stacked autoencoders (SAEs) to predict stock price, and LSTM to predict the close price. The result shows that their model outperforms other models such as WLSTM, Footnote 2 LSTM, and RNN in predictive accuracy and profitability.

RL is popular recently despite its complexity. We find that five studies apply this model. Chen et al. ( 2018a ) propose an agent-based RL system to mimic 80% professional trading strategies. Feuerriegel and Prendinger ( 2016 ) convert the news sentiment into the signal in the trading system, although their daily returns and abnormal returns are nearly zero. Chakraborty ( 2019 ) cast the general financial market fluctuation into a stochastic control problem and explore the power of two RL models, including Q-learning Footnote 3 and state-action-reward-state-action (SARSA) algorithm. Both models can enhance profitability (e.g., 9.76% for Q-learning and 8.52% for SARSA). They outperform the buy-and-hold strategy. Footnote 4 Zhang and Maringer ( 2015 ) conduct a hybrid model called GA, with recurrent RL. GA is used to select an optimal combination of technical indicators, fundamental indicators, and volatility indicators. The out-of-sample trading performance is improved due to a significantly positive Sharpe ratio. Martinez-Miranda et al. ( 2016 ) create a new topic of trading. It uses a market manipulation scanner model rather than a trading system. They use RL to model spoofing-and-pinging trading. This study reveals that their model just works on the bull market. Jeong and Kim ( 2018 ) propose a model called deep Q-network that is constructed by RL, DNN, and transfer learning. They use transfer learning to solve the overfitting issue incurred as a result of insufficient data. They argue that the profit yields in this system increase by four times the amount in S&P500, five times in KOSPI, six times in EuroStoxx50, and 12 times in HIS.

Banking default risk and credit

Most articles in this domain focus on FNN applications. Rönnqvist and Sarlin ( 2017 ) propose a model for detecting relevant discussions in texting and extracting natural language descriptions of events. They convert the news into a signal of the bank-distress report. In their back-test, their model reflects the distressing financial event of the 2007–2008 period.

Zhu et al. ( 2018 ) propose a hybrid CNN model with a feature selection algorithm. Their model outperforms LR and random forest in consumer credit scoring. Wang et al. ( 2019 ) consider that online operation data can be used to predict consumer credit scores. They thus convert each kind of event into a word and apply the Event2vec model to transform the word into a vector in the LSTM network. The probability of default yields higher accuracy than other models. Jurgovsky et al. ( 2018 ) employs the LSTM to detect credit card fraud and find that LSTM can enhance detection accuracy.

Han et al. ( 2018 ) report a method that adopts RL to assess the credit risk. They claim that high-dimensional partial differential equations (PDEs) can be reformulated by using backward stochastic differential equations. NN approximates the gradient of the unknown solution. This model can be applied to F&B risk evaluation after considering all elements such as participating agents, assets, and resources, simultaneously.

Portfolio management

Song et al. ( 2017 ) establish a model after combining ListNet and RankNet to make a portfolio. They take a long position for the top 25% stocks and hold the short position for the bottom 25% stocks weekly. The ListNetlong-short model is the optimal one, which can achieve a return of 9.56%. Almahdi and Yang ( 2017 ) establish a better portfolio with a combination of RNN and RL. The result shows that the proposed trading system respond to transaction cost effects efficiently and outperform hedge fund benchmarks consistently.

Macroeconomic prediction

Sevim et al. ( 2014 ) develops a model with a back-propagation learning algorithm to predict the financial crises up to a year before it happened. This model contains three-layer perceptrons (i.e., MLP) and can achieve an accuracy rate of approximately 95%, which is superior to DT and LR. Chatzis et al. ( 2018 ) examine multiple models such as classification tree, SVM, random forests, DNN, and extreme gradient boosting to predict the market crisis. The results show that crises encourage persistence. Furthermore, using DNN increases the classification accuracy that makes global warning systems more efficient.

Price prediction

For price prediction, Sehgal and Pandey ( 2015 ) review ANN, SVM, wavelet, GA, and hybrid systems. They separate the time-series models into stochastic models, AI-based models, and regression models to predict oil prices. They reveal that researchers prevalently use MLP for price prediction.

Data preprocessing and data input

Data preprocessing.

Data preprocessing is conducted to denoise before data training of DL. This section summarizes the methods of data preprocessing. Multiple preprocessing techniques discussed in Part 4 include the principal component analysis (Chong et al. 2017 ), SVM (Gunduz et al. 2017 ), autoencoder, and RBM (Chen et al. 2018b ). There are several additional techniques of feature selection as follows.

Relief: The relief algorithm (Zhu et al. 2018 ) is a simple approach to weigh the importance of the feature. Based on NN algorithms, relief repeats the process for n times and divides each final weight vector by n . Thus, the weight vectors are the relevance vectors, and features are selected if their relevance is larger than the threshold τ .

Wavelet transforms: Wavelet transforms are used to fix the noise feature of the financial time series before feeding into a DL network. It is a widely used technique for filtering and mining single-dimensional signals (Bao et al. 2017 ).

Chi-square: Chi-square selection is commonly used in ML to measure the dependence between a feature and a class label. The representative usage is by Gunduz et al. ( 2017 ).

Random forest: Random forest algorithm is a two-stage process that contains random feature selection and bagging. The representative usage is by Fischer and Krauss ( 2017 ).

Data inputs

Data inputs are an important criterion for judging whether a DL model is feasible for particular F&B domains. This section summarizes the method of data inputs that have been adopted in the literature. Based on our review, five types of input data in the F&B domain can be presented. Table  2 provides a detailed summary of the input variable in F&B domains.

History price: The daily exchange rate can be considered as history price. The price can be the high, low, open, and close price of the stock. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Technical index: Technical indexes include MA, exponential MA, MA convergence divergence, and relative strength index. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Gunduz et al. ( 2017 ), Sezer et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Financial news: Financial news covers financial message, sentiment shock score, and sentiment trend score. Related articles include Feuerriegel and Prendinger ( 2016 ), Krausa and Feuerriegel ( 2017 ), Minh et al. ( 2017 ), and Song et al. ( 2017 ).

Financial report data: Financial report data can account for items in the financial balance sheet or the financial report data (e.g., return on equity, return on assets, price to earnings ratio, and debt to equity ratio). Zhang and Maringer ( 2015 ) is a representative study on the subject.

Macroeconomic data: This kind of data includes macroeconomic variables. It may affect elements of the financial market, such as exchange rate, interest rate, overnight interest rate, and gross foreign exchange reserves of the central bank. Representative articles include Bao et al. ( 2017 ), Kim and Won ( 2018 ), and Sevim et al. ( 2014 ).

Stochastic data: Chakraborty ( 2019 ) provides a representative implementation.

Evaluation rules

It is critical to judge whether an adopted DL model works well in a particular financial domain. We, thus, need to consider evaluation systems of criteria for gauging the performance of a DL model. This section summarizes the evaluation rules of F&B-oriented DL models. Based on our review, three evaluation rules dominate: the error term, the accuracy index, and the financial index. Table  3 provides a detailed summary. The evaluation rules can be boiled down to the following categories.

Error term: Suppose Y t  +  i and F t  +  i are the real data and the prediction data, respectively, where m is the total number. The following is a summary of the functional formula commonly employed for evaluating DL models.

Mean Absolute Error (MAE): \( {\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{m} \) ;

Mean Absolute Percent Error (MAPE): \( \frac{100}{m}{\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{Y_{t+i}} \) ;

Mean Squared Error (MSE): \( {\sum}_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m} \) ;

Root Mean Squared Error (RMSE): \( \sqrt{\sum_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m}} \) ;

Normalized Mean Square Error (NMSE): \( \frac{1}{m}\frac{\sum {\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{\mathit{\operatorname{var}}\left({Y}_{t+i}\right)} \) .

Accuracy index: According to Matsubara et al. ( 2018 ), we use TP, TN, FP, and FN to represent the number of true positives, true negatives, false positives, and false negatives, respectively, in a confusion matrix for classification evaluation. Based on our review, we summarize the accuracy indexes as follows.

Directional Predictive Accuracy (DPA): \( \frac{1}{N}{\sum}_{t=1}^N{D}_t \) , if ( Y t  + 1  −  Y t ) × ( F t  + 1  −  Y t ) ≥ 0, D t  = 1, otherwise, D t  = 0;

Actual Correlation Coefficient (ACC): \( \frac{TP+ TN}{TP+ FP+ FN+ TN} \) ;

Matthews Correlation Coefficient (MCC): \( \frac{TP\times TN- FP\times FN}{\sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} \) .

Financial index: Financial indexes involve total return, Sharp ratio, abnormal return, annualized return, annualized number of transaction, percentage of success, average profit percent per transaction, average transaction length, maximum profit percentage in the transaction, maximum loss percentage in the transaction, maximum capital, and minimum capital.

For the prediction by regressing the numeric dependent variables (e.g., exchange rate prediction or stock market prediction), evaluation rules are mostly error terms. For the prediction by classification in the category data (e.g., direction prediction on oil price), the accuracy indexes are widely conducted. For stock trading and portfolio management, financial indexes are the final evaluation rules.

General comparisons of DL models

This study identifies the most efficient DL model in each identified F&B domain. Table  4 illustrates our comparisons of the error terms in the pool of reviewed articles. Note that “A > B” means that the performance of model A is better than that of model B. “A + B” indicates the hybridization of multiple DL models.

At this point, we have summarized three methods of data processing in DL models against seven specified F&B domains, including data preprocessing, data inputs, and evaluation rules. Apart from the technical level of DL, we find the following:

NN has advantages in handling cross-sectional data;

RNN and LSTM are more feasible in handling time series data;

CNN has advantages in handling the data with multicollinearity.

Apart from application domains, we can induce the following viewpoints. Cross-sectional data usually appear in exchange rate prediction, price prediction, and macroeconomic prediction, for which NN could be the most feasible model. Time series data usually appear in stock market prediction, for which LSTM and RNN are the best options. Regarding stock trading, a feasible DL model requires the capabilities of decision and self-learning, for which RL can be the best. Moreover, CNN is more suitable for the multivariable environment of any F&B domains. As shown in the statistics of the Appendix , the frequency of using corresponding DL models corresponds to our analysis above. Selecting proper DL models according to the particular needs of financial analysis is usually challenging and crucial. This study provides several recommendations.

We summarize emerging DL models in F&B domains. Nevertheless, can these models refuse the efficient market hypothesis (EMH)? Footnote 5 According to the EMH, the financial market has its own discipline. There is no long-term technical tool that could outperform an efficient market. If so, using DL models may not be practical in long-term trading as it requires further experimental tests. However, why do most of the reviewed articles argue that their DL models of trading outperform the market returns? This argument has challenged the EMH. A possible explanation is that many DL algorithms are still challenging to apply in the real-world market. The DL models may raise trading opportunities to gain abnormal returns in the short-term. In the long run, however, many algorithms may lose their superiority, whereas EMH still works as more traders recognize the arbitrage gap offered by these DL models.

This section discusses three aspects that could affect the outcomes of DL models in finance.

Training and validation of data processing

The size of the training set.

The optimal way to improve the performance of models is by enhancing the size of the training data. Bootstrap can be used for data resampling, and generative adversarial network (GAN) can extend the data features. However, both can recognize numerical parts of features. Sometimes, the sample set is not diverse enough; thus, it loses its representativeness. Expanding the data size could make the model more unstable. The current literature reported diversified sizes of training sets. The requirements of data size in the training stage could vary by different F&B tasks.

The number of input factors

Input variables are independent variables. Based on our review, multi-factor models normally perform better than single-factor models in the case that the additional input factors are effective. In the time-series data model, long-term data have less prediction errors than that for a short period. The number of input factors depends on the employment of the DL structure and the specific environment of F&B tasks.

The quality of data

Several methods can be used to improve the data quality, including data cleaning (e.g., dealing with missing data), data normalization (e.g., taking the logarithm, calculating the changes of variables, and calculating the t -value of variables), feature selection (e.g., Chi-square test), and dimensionality reduction (e.g., PCA). Financial DL models require that the input variables should be interpretable in economics. When inputting the data, researchers should clarify the effective variables and noise. Several financial features, such as technical indexes, are likely to be created and added into the model.

Selection on structures of DL models

DL model selection should depend on problem domains and cases in finance. NN is suitable for processing cross-sectional data. LSTM and other RNNs are optimal choices for time-series data in prediction tasks. CNN can settle the multicollinearity issue through data compression. Latent variable models like GAN can be better for dimension reduction and clustering. RL is applicable in the cases with judgments like portfolio management and trading. The return levels and outcomes on RL can be affected significantly by environment (observation) definitions, situation probability transfer matrix, and actions.

The setting of objective functions and the convexity of evaluation rules

Objective function selection affects training processes and expected outcomes. For predictions on stock price, low MAE merely reflects the effectiveness of applied models in training; however, it may fail in predicting future directions. Therefore, it is vital for additional evaluation rules for F&B. Moreover, it can be more convenient to resolve the objective functions if they are convex.

The influence of overfitting (underfitting)

Overfitting (underfitting) commonly happens in using DL models, which is clearly unfavorable. A generated model performs perfectly in one case but usually cannot replicate good performance with the same model and identical coefficients. To solve this problem, we have to trade off the bias against variances. Bias posits that researchers prefer to keep it small to illustrate the superiority of their models. Generally, a deeper (i.e., more layered) NN model or neurons can reduce errors. However, it is more time-consuming and could reduce the feasibility of applied DL models.

One solution is to establish validation sets and testing sets for deciding the numbers of layers and neurons. After setting optimal coefficients in the validation set (Chong et al. 2017 ; Sevim et al. 2014 ), the result in the testing sets reveals the level of errors that could mitigate the effect of overfitting. One can input more samples of financial data to check the stability of the model’s performance. This method is known as the early stopping. It stops training more layers in the network once the testing result has achieved an optimal level.

Moreover, regularization is another approach to conquer the overfitting. Chong et al. ( 2017 ) introduces a constant term for the objective function and eventually reduces the variates of the result. Dropout is also a simple method to address overfitting. It reduces the dimensions and layers of the network (Minh et al. 2017 ; Wang et al. 2019 ). Finally, the data cleaning process (Baek and Kim 2018 ; Bao et al. 2017 ), to an extent, could mitigate the impact of overfitting.

Financial models

The sustainability of the model.

According to our reviews, the literature focus on evaluating the performance of historical data. However, crucial problems remain. Given that prediction is always complicated, the problem of how to justify the robustness of the used DL models in the future remains. More so, whether a DL model could survive in dynamic environments must be considered.

The following solutions could be considered. First, one can divide the data into two groups according to the time range; performance can subsequently be checked (e.g., using the data for the first 3 years to predict the performance of the fourth year). Second, the feature selection can be used in the data preprocessing, which could improve the sustainability of models in the long run. Third, stochastic data can be generated for each input variable by fixing them with a confidence interval, after which a simulation to examine the robustness of all possible future situations is conducted.

The popularity of the model

Whether a DL model is effective for trading is subject to the popularity of the model in the financial market. If traders in the same market conduct an identical model with limited information, they may run identical results and adopt the same trading strategy accordingly. Thus, they may lose money because their strategy could sell at a lower price after buying at a higher.

Conclusion and future works

Concluding remarks.

This paper provides a comprehensive survey of the literature on the application of DL in F&B. We carefully review 40 articles refined from a collection of 150 articles published between 2014 and 2018. The review and refinement are based on a scientific selection of academic databases. This paper first recognizes seven core F&B domains and establish the relationships between the domains and their frequently-used DL models. We review the details of each article under our framework. Importantly, we analyze the optimal models toward particular domains and make recommendations according to the feasibility of various DL models. Thus, we summarize three important aspects, including data preprocessing, data inputs, and evaluation rules. We further analyze the unfavorable impacts of overfitting and sustainability when applying DL models and provide several possible solutions. This study contributes to the literature by presenting a valuable accumulation of knowledge on related studies and providing useful recommendations for financial analysts and researchers.

Future works

Future studies can be conducted from the DL technical and F&B application perspectives. Regarding the perspective of DL techniques, training DL model for F&B is usually time-consuming. However, effective training could greatly enhance accuracy by reducing errors. Most of the functions can be simulated with considerable weights in complicated networks. First, one of the future works should focus on data preprocessing, such as data cleaning, to reduce the negative effect of data noise in the subsequent stage of data training. Second, further studies on how to construct layers of networks in the DL model are required, particularly when considering a reduction of the unfavorable effects of overfitting and underfitting. According to our review, the comparisons between the discussed DL models do not hinge on an identical source of input data, which renders these comparisons useless. Third, more testing regarding F&B-oriented DL models would be beneficial.

In addition to the penetration of DL techniques in F&B fields, more structures of DL models should be explored. From the perspective of F&B applications, the following problems need further research to investigate desirable solutions. In the case of financial planning, can a DL algorithm transfer asset recommendations to clients according to risk preferences? In the case of corporate finance, how can a DL algorithm benefit capital structure management and, thus, maximize the values of corporations? How can managers utilize DL technical tools to gauge the investment environment and financial data? How can they use such tools to optimize cash balances and cash inflow and outflow? Until recently, DL models like RL and generative adversarial networks are rarely used. More investigations on constructing DL structures for F&B regarding preferences would be beneficial. Finally, the developments of professional F&B software and system platforms that implement DL techniques are highly desirable.

Availability of data and materials

Not applicable.

In the model, NSGA stands for non-dominated sorting genetic algorithm.

A combination of Wavelet transforms (WT) and long-short term memory (LSTM) is called WLSTM in Bao et al. ( 2017 ).

Q-learning is a model-free reinforcement learning algorithm.

Buy-and-hold is a passive investment strategy in which an investor buys stocks (or ETFs) and holds them for a long period regardless of fluctuations in the market.

EMH was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s. It says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. It is impossible to consistently choose stocks that will beat the returns of the overall stock market. Therefore, this hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

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Acknowledgments

The constructive comments of the editor and three anonymous reviewers on an earlier version of this paper are greatly appreciated. The authors are indebted to seminar participants at 2019 China Accounting and Financial Innovation Form at Zhuhai for insightful discussions. The corresponding author thanks the financial supports from BNU-HKBU United International College Research Grant under Grant R202026.

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JH carried out the collections and analyses of the literature, participated in the design of this study and preliminarily drafted the manuscript. JC initiated the idea and research project, identified the research gap and motivations, carried out the collections and analyses of the literature, participated in the design of this study, helped to draft the manuscript and proofread the manuscript. SC participated in the design of the study and the analysis of the literature, helped to draft the manuscript and proofread the manuscript. The authors read and approved the final manuscript.

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Part b. detailed structure of standard rnn.

The abstract structure of RNN for a sequence cross over time can be extended, as shown in Fig. 7 in Appendix , which presents the inputs as X , the outputs as Y , the weights as w , and the Tanh functions.

The detailed structure of RNN

Part C. List of abbreviations

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Huang, J., Chai, J. & Cho, S. Deep learning in finance and banking: A literature review and classification. Front. Bus. Res. China 14 , 13 (2020). https://doi.org/10.1186/s11782-020-00082-6

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Green banking and sustainability – a review

Arab Gulf Journal of Scientific Research

ISSN : 1985-9899

Article publication date: 7 September 2022

Issue publication date: 15 September 2022

The purpose of this article is to study green banking practices, its methods of adoption and importance of practicing green banking. This study also includes the role and contribution of banks in environmental sustainability and UN Sustainable Development Goals.

Design/methodology/approach

The current research paper is conceptual in nature, based on a thorough literature review, websites of financial institutions and literature evaluations among other sources. This study has been supplemented by a variety of research journal articles. The websites of many banks including SBI (State Bank of India) and MayBank (Malaysia) were used and reviewed to know about various green banking practices both nationally and internationally and their contribution toward sustainability.

The devastating effects of recent flooding, droughts and extreme temperatures that several people all over the world have experienced compelled everyone to begin thinking about global warming and its consequences, and to do everything that can be done to address this problem. Governments, businesses and individuals all play a part in preventing global warming and creating a more sustainable world. People have to deal with financial institutions, particularly banks, which play a vital role in this environment by assisting in the development of a robust and successful low-carbon economics. They should make more use of environmental data when extending credit and making investment decisions. The project will assist them in proactively improving their environmental performance while also adding long-term value to their company. Businesses having a bigger carbon output may be viewed as riskier in the future, and banks may shy away from funding such businesses in favor of innovative technology solutions that absorb or reduce carbon emissions. As a result, green banking is the order of the day, a source for sustainable development and it will undoubtedly benefit banks, industries as well as the environment at large.

Research limitations/implications

The theoretical implications can be summed in the following points: (1) there is no universally accepted framework for green or sustainable banking so far. However, green banking practices are at different stages of development across countries. As per the case of India, green banking practices are at a development phase in India, and green processes have a significant impact on sustainable development. (2) The study is one of the first of its kind in the academic literature as it links green banking practices with sustainability besides discussing green banking practices of the top public sector Bank of India and top commercial bank of Malaysia. Despite the significant contributions made by this study, many disadvantages should be addressed for future research. The present work was chosen for comfort, it was restricted to green banking practices of two banks only, which limits conclusion and interpretation of outcome to some extent Future research can be conducted by a comparative study with the top green banks or with the cleanest country of the world or green banking practices by those banks toward sustainability in that country can also be a good area for research

Practical implications

Managerial implication: The study is extremely helpful to the banking industry in determining the scope of green banking initiatives in sustainable development. This study is a prime study in India to interrelate banking industry towards sustainability and two UN SDGs besides green banking practices of banks. This paper has noted the areas where the banks can make progress for the greener, sustainable economics. It has also aided the banking industry in identifying areas for development so that it may focus on improving social satisfaction and satisfaction of stakeholders across its operating areas. The study is also very helpful for banks to comprehend how vital these green initiatives, especially green processes, are to improve sustainability.

Social implications

The study will serve as a gauge for banking actions toward greener nations and a greener world since these are the efforts toward Carbon Free World, Efforts for controlling global warming, efforts for the greener planet in general which undoubtedly is a significant long-term service to society a reason for better climate and better tomorrow.

Originality/value

This paper identifies the need for green banking in sustainability. This article also summarizes the notion of green banking besides outlining some methods and analyzing green banking initiative by SBI (State bank of India) of India, MayBank of Malaysia & UNSDG .

• Sustainable development
• Environment-friendly
• Green banking
• UN Sustaianble development goals

Mir, A.A. and Bhat, A.A. (2022), "Green banking and sustainability – a review", Arab Gulf Journal of Scientific Research , Vol. 40 No. 3, pp. 247-263. https://doi.org/10.1108/AGJSR-04-2022-0017

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Copyright © 2022, Ajaz Akbar Mir and Aijaz Ahmad Bhat

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Introduction

Green banking is becoming a global standard speedily for adopting socially and environmentally acceptable business operations. This banking is environmentally benign by preventing environmental deterioration and making the earth more habitable. In the last few decades, green banking has become a catchphrase in the area of sustainable banking. In reality, green banking is recognized as sustainable banking, which plays a part in protecting the world from environmental damage with the goal of guaranteeing long-term economic prosperity ( Islam, Roy, Miah, & Das, 2020 ). To protect and make our environment greener, we must take some practical initiatives, which should focus at the business level and appropriate center to focus on environmental factors and implement greening efforts at the corporate level ( Islam, 2020 ). Imbalanced industrialization has harmed the environment and resulted in natural and industrial calamities ( Rehman et al. , 2021 ). As per ( Bangladesh Banladesh Bank, 2020 , www.bb.org.bd ), green banking is a type of banking with the main goal of protecting the environment and sustainable development (SD)while taking into account all social and environmental aspects. Therefore, the term “sustainable development” has spread throughout the development community and is now used by international agencies, development planners, academics and advocates for environmental and SD ( Ukaga, Maser, & Reichenbach, 2011 ). SD has developed as a new growth model in accomplishing the underlying future goal since 1992. In a larger sense, SD is defined as “long-term cultural, socioeconomic, and environmental wellness,” with the focus on “long-term,” “together with the necessity of integrating our social, economical, and environmental well-being.” ( Rahman & Rahman, 2020 ) interpreted that SD is based on enlightened self-interest, and it frequently involves the triple bottom line of economic, social and environmental concerns. Environmental sustainability, SD and climate change are the crucial parts of comprehensive socioeconomic development in developing nations and can be managed by green banking to a large extent ( Monirul Alam, Alam, & Mushtaq, 2018 ). Green banking has been characterized in a variety of ways by academics, but the overall focus has been on complete banking systems that ensure significant economic growth while simultaneously improving environmental-friendly practices ( Lalon, 2015 ). Banks must take a more significant role related to climate change through green banking and it is effective ( Sarker, Khatun, & Alam, 2019; Stephens & Skinner, 2013 ). Bai (2011) defines green banking as environmentally friendly baking and a set of practices and responsibilities that establishes a business being ecologically friendly. Green banking, often known as moral financial services, is a broad term that refers to environmental-friendly and socially responsible banking activities ( Goyal & Joshi, 2011; Sarker, Peng, Yiran, & Shouse, 2020 ). Companies are becoming more interested in environmental integrity issues as a result of increased external pressure from a variety of stakeholders, including government banking firms, socially conscious investors and society lobby groups (i.e. members of host communities), among others, in accordance with this trend, the recent surge in environmental costs has prompted businesses to include environmental considerations into all levels of management ( Shuvro, Saha, & Alam, 2020 ). Khawaspatil & More (2013), Ajaz & Aijaz (2021) concluded that Indian banks remain further behind in green banking services execution, despite a lot of opportunities in green banking and RBI notifications, hence strict measures are required to be taken regarding implementation besides awareness and training to customers and bankers must be provided. Only a few banks have taken the initiative in this area. All banks have a lot of potential, and they cannot only preserve our planet but also convert the entire globe to be more energy conscious. Banks must educate their consumers about green banking and implement all techniques to help save the environment while also improving the bank's reputation. After studying research conducted both in India and overseas, it is clear that the majority of banks are gearing up their efforts to acquire SD through the use of green practices.

Literature review

Green banking is a type of banking activity where banks make the effort to carry out their everyday operations as conscientious members of society by taking internal and external environmental sustainability into consideration and these banks are termed as green or sustainable banks Hossain, Rahman, Hossain, and Karim (2020) . Choudhury, Salim, Bashir, and Saha (2013) highlighted that in today's banking competition every bank should step up to the plate to produce a new green product with higher stakeholder involvement and SD. However, Dharwal and Agarwal (2013) while outlining risks found that green banking is a key to mitigate many types of risks such as legal risk, credit risk and reputation risk. They also recommended several green practices, such as carbon credit businesses, green financial goods, green mortgages, carbon emissions mitigation, energy awareness, green construction and social responsibility contributions to society. Ahmad, Zayed, and Harun (2013) investigated that one of the most important aspects of green banking in Bangladesh is to maintain financial viability, which is mandated by the Bangladesh Bank. Meena (2013) identified four benefits of green banking: it reduces deforestation, raises environmental consciousness among staff and consumers, provides advantage of the lower rate and changes corporate activities in an environmentally beneficial manner. Ullah (2013) , Bangladesh, arguably the least developed country, is the worst victim of global environmental pollution caused by Western countries' industrialization. Jaggi (2014) investigated SBI and ICICI bank's green banking program and strategies. SBI has implemented a number of initiatives in this area, including the Green Channel Counter, increased commitment to reaching carbon neutrality, online money transfer, wind farms, and so on. ICICI Bank's Green products and services strategy comprises internet banking for anytime, anywhere banking, auto finance and home finance. Furthermore, these banking institutions have taken initiatives to conserve energy, such as depletion (two-sided printing), recycling and using compact fluorescent lighting (CFLs), among other things. Chaurasia (2014) found that there haven't been many green banking services initiatives in India, according to investigators, who recommend that banks should practice greener financing and consider economic and environmental elements as a part of their financing principles, forcing industries to make mandated investments in SD for the greater good of society. ( Ortiz-de-Mandojana, Aguilera-Caracuel, & Morales-Raya, 2016 ) examined that due to institutional pressures, banking institutions are implementing green rules and using green transparency to become more respectable in society. Managers are encouraged to achieve environmental sustainability via institutional forces. Zhixia, Hossen, Muzafary, and Begum (2018) claimed that the Bangladesh bank's enforcement of precise criteria would lead to the successful development of sustainable lending in Bangladeshi banks. The study also showed that a barrier to green growth could be the slower rate of technology advancement, financial innovative products and widespread lack of social and ecological conscience among banking firms. ( Volz, 2018 ) found sustainable banking where investment and lending decisions are made based on environmental monitoring and risk assessment to fulfill sustainability criteria along with insurance services that address environmental and climatic risk which are significant components of green finance. Bukhari, Hashim, and Amran (2020) while pivoting on green banking adoptions model based on environmental social and governance considered where affinity of variables impacted environmental sustainability. This study found that the process is influenced by a variety of environment factors and banks can wangle the adoption by applying certain operations in a consecutive and analogous manner. Alsayegh, Abdul Rahman, and Homayoun (2020) claimed that the idea of sustainable banking entails using green banking techniques to take ethical, social and environmental concerns into account. Khairunnessa, Vazquez-Brust, and Yakovleva (2021) described that the Bangladeshi banks through their investments in numerous environmental-friendly projects, lessen the negative consequences of climate change and play a vital part in the nation's economic sustainability. Additionally, banking institutions play a significant role in financing numerous industrial projects that could have significant detrimental social or environmental effects. Zheng, Siddik, Masukujjaman, Fatema, and Alam (2021) outlined that the Green Financing is seen as a crucial component of sustainable banking, having a significant influence on the growth of a eco-friendly economy and industry generally. Therefore, it can be said that in enhancing the sustainability practices of the financial sector, the banking sector should focus on ensuring the funding for environment-conscious projects through financially viable banking in order to enhance the competitive edge of banks, generate more earnings, improve existing assets and save on invested capital and other costs. Until recently, green banking appeared to be merely an idea, and environmental concerns did not appear to be particularly relevant to a bank's operations. Initially, a bank evaluating a client's environmental suitability would have been regarded as intruding into their private affairs. However, the current view is that this poses a risk to their business. Although financial organizations are not directly impacted by environmental degradation, they, nevertheless, incur indirect expenses. Unless such measures are adopted, credit, legal and reputation problems will continue to hound these banks. The growing economies are yet to embrace the conceptualization. Amir (2021) argues that the number of studies on the green banking is scarce in developing countries; hence there is a compelling need to unlock the concept in totality. Similarly, Sharma and Choubey (2022) shared the concern for the dearth of studies in green banking space. Moreover, Chandran and Sathiyabama (2020) described that green banking practices have not gained currency in developing countries in general and Indian banks in particular. However, green banking has drawn a lot of attention in developed countries but underdeveloped countries have mostly neglected it ( Weber, 2016; Jeucken, 2010; Khan et al. , 2015; Roca & Searcy, 2012 ) and in nations like India research on green banking is virtually non-existent ( Prakash, Kumar, & Srivastava, 2018 ). Research has also shown that Indian banks are not ideally suited to carry out green banking practices ( Rajput, Kaura, & Khanna, 2013 ). The Reserve Bank of India plays a significant role in advancing environmental standards. A developing nation such as India needs to put more emphasis on the social aspect of banking and link it to economic development ( UNEP FI, 2016 ). On the other hand, in India, the majority of research focuses on corporate social responsibility and environmental management ( Narwal, 2007; Biswas, 2016; Rajput et al. , 2013; Sharma & Mani, 2013 ; Sahoo & Nayak, 2007 ), green banking strategies ( Bahl, 2012; Tara & Singh, 2014 ) and green practices adopted by private and public sector banks ( Bahl, 2012; Bihari, 2010 ). There is a significant gap between what banks seek to promote and what the public perceives them to be doing in terms of green banking ( Jayadatta & Nitin, 2017 ). Resultantly, there is a dearth of literature pertaining to green banking in India ( Sharma & Choubey, 2022 ) not much research has been conducted on role of green banking in SD overall, green banking practices by SBI (India), Maybank (Malaysia) and the contribution in achieving UN SDGs for the country. MayBank of Malaysia has been first-lined for the study due to its top commercial activities in Malaysia and State bank of India preferred for this review owing to the first bank to focus on green banking initiatives. Kaur and Sandhu (2019) since most of the studies conducted on green banking predominantly concentrated on green banking practices or on perception of customers or bankers. This gap justifies the need to investigate the problem stated. Therefore, the present study is an endeavor and an attempt to fill the research gap in this regard.

Online bill payment: Paying bills online is a significant lifestyle change, but it is possible. Payments for telephone, television and utility payments, as well as credit card and mortgage, could all be made electronically. In fact, several clients have completely abandoned their paper cheque books in favor of online payments. Not only is bookkeeping a lot easier, but a lot of paper is saved as well.

Net banking: Customers who use online banking do the majority of basic banking duties without having to enter the bank physically. For online banking clients must have a unique internet banking ID and password issued by the concerned bank.

Online saving accounts: The simplest method of using green banking services and protecting the environment is to open an online savings account and use mobile banking. Opening up direct deposit for your pay cheques, obtaining electronic statements via your bank, and making payments online are all examples of green banking. All these techniques can help your bank cut down on the amount of paper it produces. Internet banking and mobile banking are also excellent tools for staying on top of your finances and avoiding late fees. Other banking action you may take is to advise that your employer subscribe for a “Remote Deposit” program. To make a deposit, remote consumers must physically present each check to their bank. Banks can also clear payments digitally via remote deposits.

Paperless banking: All banks are moving to the CBS or ATM platform, and they are also offering online financial products and services. As a result, there is a lot of room for banks to become paperless banking. Private and foreign banks use electronic communication in their offices, but PSU banks continue to rely heavily on paper. Paper fewer statements, on the other hand, are those statements sent by mail to the concerned stakeholders of the bank to avoid huge wastage of paper. Most banks offer people the option of receiving their statements digitally through a protected log-in when they sign up for internet banking. Electronic copies of financial records and statements can therefore be stored rather than paper files. The risk of identity theft is further reduced when statements are received online.

Green deposits: The majority of firms will allow employees to get their pay cheques electronically. This mostly expedites the accessibility of your funds and saves customers a trip to the bank, but it also saves paper, a bunch of paperwork involved and so on.

Green finance or green loan: Banks can create creative green-based products or offer low-interest green loans. A green credit loan or green finance is being issued for projects that benefit nature and the environment i.e. finance provided for Renewable Energy and Clean Energy projects, Resource Recycling projects, Waste Disposal, Pollution Prevention and Control projects, Green Agriculture Development projects, Industrial Energy Conservation, Water Conservation and Environmental Protection projects, Green Transport projects, Energy Conservation and Environmental Protection Services projects, etc.

Green building and CSR: Banks have residential dwellings, branches and ATMs, they may choose to develop green buildings to protect the environment. Indian banks should launch numerous social responsibility activities as part of their green banking program, such as tree planting camps, park upkeep, and pollutant check-up camps, etc.

Importance of green banking: Green building and CSR: Banks have residential dwellings, branches and ATMs, they may choose to develop green buildings to protect the environment. Indian banks should launch numerous social responsibility activities as part of their green banking program, such as tree planting camps, park upkeep and pollutant check-up camps, etc.

Importance of green banking

Green banking is regarded as one of the processes for guaranteeing sustainability in which business operations have no adverse impact on the environment. Moreover, environment management is identical to risk management. It is extremely important for both banks and the economy since it avoids numerous hazards in the banking sector. Banks play an intermediary part in the economy because banks have the capacity to contribute significantly to SD. Green banking not only secures the greening of sectors, but it also helps banks improve their asset quality in the future. Green banking enhances the image of the bank by demonstrating and serving its environmental commitment; reduces operational costs due to less utilization of office stationery, energy and water; increases employee productivity and efficiency through skilled and optimum use of technology; and reduces dangers by installing eco-friendly equipment. It saves a lot of forestry by minimizing paper usage; reduces greenhouse gas emissions by teleconferencing and arranging a transportation pool for employees; assists in developing customers' environmental consciousness by organizing awareness program; and reduces the extent of non-performing assets by investing in less risky projects. Green banking involves technical advancements, operational improvements and a shift in client behavior in the banking industry. Green banking program typically include energy efficiency, recycling, ride sharing and environmentally responsible lending. Due to severe environmental regulations enforced by competent authorities throughout countries, industries would be required to observe particular standards in order to conduct business. It improves the mental capabilities of officials and customers to reflect green sensibilities. Green banking saves money and energy by lowering costs and raising the country's GDP.

Green banking initiatives by MayBank Malaysia

Conservation alliance for tigers (MYCAT): MayBank worked with the Malaysian Conservation Alliance for Tigers (MYCAT) in 2011. Over the course of two years, donation of RM 1 million has been made to support research programs aimed at ensuring the effective preservation of wild tiger environment in Malaysia.

Carbon disclosure project (CDP): The CDP is a non-profit organization that gives stakeholders with an in-depth look into how the planet's major corporations are tackling climate change. Companies that participate in the CDP gain better understanding of how to protect ourselves from the effects of climate change and thus become more environmental friendly. Maybank is one of merely two Malaysian banks to partake in the CDP since 2010. The CDP rating indicates a company's degree of loyalty and experience with emissions disclosure. They achieved a total number of 58 for the 2012 CDP assessment, an increase over the past year's score of 37.

Green financing and environmental-friendly projects: Maybank ventures announced a US$500 million renewable energy fund in November 2011, with the goal of capitalizing on the increased interest in clean and renewable energy. Its 10-year private equity fund consists of a series of diversified renewable energy project holdings across the Asia-Pacific region, with a concentration on China, India, Indonesia, Malaysia, Thailand, the Philippines, Vietnam, Cambodia and Laos. They also support the Malaysian government's Green Technology Financing Scheme (GTFS), which was created to encourage environmental friendly funding. MayBank participated in four major photovoltaic (PV) power plant projects in 2020 as part of a sustainable financing drive, with a total funding of around RM 1.3 billion. These projects, which will be installed in West Malaysia, will have total output of 390 MW and are expected to be commercially active by the end of 2021 or early 2022, allowing them to replace traditional coal or gas-powered stations in the local energy system and reduce carbon emissions.

Maybank is also contributing RM 70 million to the Sun Lease project to build 30 MW rooftop solar systems for the creation of cheaper power. The bank has increased its environmental concentration in recent years to help the development of a sustainable financial sector, launching its first green fund in 2020 and assisting in the issuance of several green securities across the area. In keeping with its mission to facilitate sustainable economic activity, Maybank has continued to offer funding for sustainable energy and other green infrastructure projects ( MayBank Sustainability Report, 2021 ).

Supporting innovation through financing Waste-to-Energy Projects: In 2020 Maybank refinanced the creation of a waste-to-energy project in Negeri Sembilan with about RM 374 million in financing waste-to-energy initiatives which enhance waste management attempts by blowing up municipal solid refuse to generate steam for power production. This project is expected to convert 600 tons of garbage per day and generate up to 25MW of electricity, with the capacity to expand in the future. Besides, from waste-to-energy capabilities, this plant will also include waste segregation and reusing materials, leachate treatment and a safe disposal.

Paper consumption and disposal: Paperless strategy is implemented throughout marketplaces by digitalizing all its internal procedures. Almost 136,570 kg of sensitive papers were securely recycled throughout Malaysian and Singaporean locations.

Raising awareness and reducing carbon emission: Maybank runs awareness programs and campaigns through posters, banners, e-bulletins and other regular communications regarding environmental clean consciousness. In 2020 a campaign was lunched ‘Don't be a Plastic Addict: Bring your Own Container'. The bank also encourages people to switch off their computers, water coolers and other electrical appliances when not in use through different platforms. MayBank reduced electricity consumption by 8.5% to 50,102,311 kWh compared to 2019 as an initiative to reduce carbon emission across its seven strategic buildings in West Malaysia. In 2020, water consumption was reduced by 54,786 m 3 or 11.7% compared to 466,769 in 2019.

Waste management: All garbage is responsibly disposed of, with efficient collection, recycling and disposal systems in place, all of which are overseen by licensed contractors to ensure compliance with government regulations. They also hire professionals to properly dispose of outdated electrical gadgets.

Green banking initiatives by State Bank of India

Green financing and environmental-friendly projects: SBI is investing in Eco environmental Projects and Renewable energy projects like Solar Roof (INR 91.37 Billion), Wind Energy (INR 20.93 Billion), biomass (INR 940 Million) and on some Hydro Projects (INR 1.65 Billion) as part of sustainable Banking as on 2021 Financial year SBI, India's first bank to create green electricity, has constructed ten windmills with a combined capacity of 15 MW across Tamil Nadu, Gujarat and Maharashtra. Not only that, but the programs would be advanced further with the installation of 20 MW additional capacity in Gujarat. The initiative's primary goal is not just to be economically advantageous but also to protect the environment by reducing reliance on non-renewable resources. Although the installation cost of a 1.5 MW windmill is projected to be over 10 crores, the running cost for the windmills will be almost nothing, proving to be cost efficient. Suzlon Energy has taken up the installation of these windmills with the goal of encouraging Indian banks to go green. Banks will also assist the firms by funding these initiatives at cheaper interest rates. These loans will be dubbed “Carbon Credit Plus” ( ET Bureau, 2010 ).

Green housing: Green housing is another key program launched by the State Bank of India to promote a low-carbon society. The bank has taken on the obligation of providing financing to people interested in green projects. Green buildings, via improved design and operation, are reducing the negative influence on the environment. Leadership in Energy and Environmental Design (LEED) INDIA, Indian Green Building Council (IGBC) and TERIGRIHA from TERI-BCSD India are the agencies in India with the ability to certify green buildings. SBI, the first bank in India to enter this market, has introduced the “SBI Green Home Loan” as a new product. The bank offers a home loan with a 5% margin concession and a 0.25% interest rate with no processing charge. Natural lighting and reclaimed water were used as conservation strategies ( Jaggi, 2014 ).

Rooftop solar project financing: SBI has begun to promote the green movement by giving huge amounts of credit to projects involving the installation of solar roofs. It would fund 100 MW of solar panels for Rs. 400 crores. The State Bank of India will carry out this development initiative with the assistance of the World Bank ( moneycontrol.com , 2017).

Counter for the green channel: On July 1, 2010, SBI took another step toward green banking by transitioning from paper-based banking operations to green channel counters in a number of its locations ( SBI, 2010 ). GCC is presently present at 7,052 SBI branches, with a daily transaction average of more than 100,000. This is the most essential action conducted by the bank because the majority of the bank's disposal is generally paper, which generates tremendous waste. As a result, trees are being chopped down at a rapid pace, causing environmental devastation.

Long term loans: The bank makes loans at low interest rates to project executors who have environmental goals and considerations, particularly in the case of manufacturing operations ( Vadrale & Katti, 2016 ). SBI's key endeavor is a cooperative arrangement between SBI and the Export and Import Bank of India (EXIM) to grant a long-term credit to the Aston field Renewable Resources and Group T-Solar Global SA, a Spanish enterprise. Banks have issued a 14-year loan with the aim of constructing a solar power plant in India ( Yadav & Pathak, 2013 ). In addition, the bank has introduced a new loan product called “Carbon Financing Plus”, with the express objective of providing credit to Clean Development Mechanism (CDM) projects ( Janakiraman & Karthikeyan, 2016 ).

Green marathon: SBI has recently proposed hosting marathons every year to promote environmental goals. In February and March 2019, the marathon was conducted in six cities: Delhi, Bangalore, Chennai, Ahmedabad and Chandigarh. The marathon was themed “Race for Green,” and each participant received a sapling to plant after finishing the run for a green city. In addition, the majority of the materials utilized in the marathon's organization were environmentally friendly. The primary goal of this marathon was to raise awareness about the critical need to embrace green practices ( India CSR Network, 2018 , https://indiacsr.in ).

Green bonds: The introduction of green bonds onto the market is a significant step on the part of the bank. The issue was created in order to collect funding for environmentally beneficial projects. In September 2018, the bank raised a total of $ 650 million. Furthermore, a subscription three times the real value was obtained ( Das, 2018 ). The bank intends to invest the funds in projects involving renewable energy, low-carbon buildings, energy-efficient goods, projects involving sustainable mobility and projects involving pollution and waste disposal. To regulate the goal, a dedicated green bond committee was constituted with qualified people.

Retail and digital banking development initiatives: SBI has developed YONO as a source to distribute awareness about the bank's omni-channel banking and lifestyle platform activities in order to promote green banking. SBI recognized much digital advancement in Bhopal in September 2018, including SBI “YONO” an omni-channel and lifestyle platform, State Bank Buddy wallet, anywhere banking comprising mobile banking, “SBI INTOUCH” a digital branch and more (the pioneer, 2018).

Solar ATMS: SBI claims to be the country's largest employer of solar ATMs. Since its inception in 2008, this initiative has been effective in reducing CO 2 emissions by 2000 tons per year. Until September 2018, the bank has built 1200 solar-powered ATMs and has built over 250 ATMs with lenders covering the roofs of 150 buildings with solar panels. It has a projection to construct approximately 10,000 ATMs in the next two years (businesstoday.in). The main goal of this initiative is to significantly reduce carbon footprints and ameliorate carbon emissions. In order to save energy, the bank has implemented efficient time management systems, automated systems and an effective lighting system in addition to ATMs.

Annual reports in electronic format: Another component that contributes to environmental preservation is the distribution of electronic annual reports to shareholders. Paper waste was enormous as a result of mailing annual statements on paper. It was started at the request of the shareholders and for a little fee that is donated to a charity. In 2014, the bank generated Rs. 3.09 Cr. By charging Rs. 100 for every report, it contributed to the SBI Children's Welfare Fund.

Project on carbon disclosure: SBI joined the CDP along with 550 other institutions with the goal of developing and enforcing stringent policies to reduce carbon footprints and encourage green banking practices.

Comparison of green banking initiatives of Maybank and SBI

It is clear that SBI has 1200 solar-powered ATMs and has built 250 ATM covering the roofs of 150 buildings with solar panels. However, enough solar-powered ATM by Maybank of Malaysia was not found under study and such kind of ATMs with more quantity should be installed by this Malaysian bank towards saving energy to control carbon emission. It is pertinent to mention that both these banks are signatories of CDP that helps companies and cities to disclose their impact on the environment. SBI is doing well by investing in eco-environmental projects and Renewable energy projects like solar roof (INR 91.37 billion), wind energy (INR 20.93 Billion), biomass (INR 940 million) and on some hydro projects (INR 1.65 billion) as part of sustainable banking as on 2021 financial year. Maybank is not that much behind in green finance and has announced a venture of US$500 million toward clean/renewable energy and RM70 million towards Sun Lease project for solar power generation. Moreover, it supports Malaysian Governments' GTFS. Maybank refinanced to create a waste-to-energy project in Negeri Sembilan with an amount of about RM374 million under waste-to-energy initiatives, but we could not find any such scheme by SBI in India. MayBank decided to go paperless by way of digitalization, and it has also adopted paper recycling process across Malaysian and Singaporean locations. SBI is also in the process of paperless, but the paper recycling process has not been started yet. As per Maybank website and sustainability report, it was found that a well-placed recycling system has been upkept across all branches for collection and recycling of wastage of its branches though such facilities was not found discussed for any SBI branches.

The UN Sustainable Development Goals

The 2030 agenda is an action plan with a commitment to leave nothing behind to carry out the revolutionary actions required to move the world toward an egalitarian, sustainable and resilient path. It identifies 17 goals, which include 169 targets and followed by 232 indicators to gauge how well they are being implemented. However, the Millennium Declaration, which preceded the 2030 Agenda having eight Millennium Development Goals (MDGs), the framework for global development that was finalized in 2015, is built upon by tackling complex and pressing issues of poverty, rising inequality, climate change, instability and fragility. The SDGs have greatly outpaced the MDGs. The SDGs are committed to ensure everyone is treated with dignity and are based on human rights. The SDGs apply worldwide: The 17 goals have to be carried out across the world whether they are developing or developed nations. The idea of universality also expresses a dedication to regional and international collaboration as well as understanding how to deal with similar problems. Given the disparate degrees of development in the region, this is especially crucial for Asia and the Pacific. The motto “leave no one behind” is a distinctive aspect of the 2030 Agenda; it is a call to action that no goal will be regarded to have been accomplished if it is not attained for all members of society; improvement in national averages is not sufficient. Each goal requires targets that pledge to progress on important enabling variables and methods for achieving, such as new data, technology and resource mobilization, which are required to produce favorable results, in addition to outlining time-bound commitments. Two important goals which the researcher found very much interrelated to green banking have been discussed along with achievement, steps, process of India and Malaysia toward these two goals.

Toward achieving seven and eleven UN Sustainable Development Goals

UN Sustainable Development Goal 7 (Affordable and Clean Energy): (SDG) 7 emphasizes a global effort to guarantee that everyone has access to affordable, efficient, renewable and modern energy. Availability of safe, modern, long-term energy is vital for enhancing the health and lives of billions of people worldwide.

Over the last decade, the percentage of people globally who have access to power and clean fuel have climbed significantly. In 2018, 90% of the global population had access to power, up from 82% in 2008, and 63% had access to clean fuel and cooking technology, up from 55% previously. At the current rate of progress, the world is not yet on schedule to meet the SDG 7 objective of guaranteeing affordable, dependable, sustainable and modern energy access by 2030. Globally, 733 million people still do not have access to electricity and 2.4 billion people continue to cook using those fuels which are harmful to their wellbeing and the environment ( World Bank Tracking SDG 7 – The Energy Progress Report, 2022 ).

Indian contribution: India has demonstrated a strong commitment to and achievement in household electrification and it is on track to meet the aim of providing universal access to electricity to every household. According to the Saubhagya dashboard of the Ministry of Power, over 99.99% of houses were lighted at the end of March 2019. Except for Chhattisgarh, all states have achieved 100% electricity. Clean cooking fuel: As of July 2020, the country had 2,824 lakh liquefied petroleum gas (LPG) connections and 72 lakh piped natural gas (PNG) connections; with Gujarat leading in PNG connections (26 lakh), while West Bengal has no PNG connection yet. Among the UTs, Delhi leads in both LPG with 49.8 lakh and PNG with 9 lakh connections.

UN Sustainable Development Goal 13: climate change

Floods, storms, droughts and extreme weather are all indicators of a changing climate. The global climate is changing. The reason is well-recognized: greenhouse gas emissions caused by human activities such as the use of fossil fuel for heat, electricity and transportation; industrial operations and land use changes. Climate change has the potential to stymie progress in practically every aspect of human existence. It puts agricultural production, water resources, ecosystems, energy supplies and infrastructures at risk. More than 216 million people may be pushed to migrate within national borders by 2050 in time to prevent the worst consequences of climate change ( World Bank, 2021 ). To fight with climate change, India has committed to an Intended Nationally Determined Contribution (INDC) goal of 40% non-fossil fuel energy generation by 2030, with an objective of 450 GW from renewable sources. India has also launched Unnat Jyoti, a project of affordable LEDs for All (UJALA) to control carbon emission low-cost LED lamps are being distributed under this project. By converting to these LED bulbs, India has saved around 38.6 MtCO 2 (Mt metric ton Carbon di oxide) by December 2020. Niti Ayog (Niti Ayog.in).

The State Bank of India also recognizes these risks of human pain or economic loss caused by climate change and is working to make renewable energy more widely available in order to lessen its impact. The SBI Group is also working on efforts to assist rejuvenate regional economies by incorporating and managing natural energy resources using area resources, increasing energy self-sufficiency, and developing sustainable communities with local production for local consumption. SBI ENERGY, in particular, is developing and pushing the spread of solar sharing (farming-type solar power production), which produces energy on farmland without interrupting farming, in addition to solar power generation, micro hydropower production and biomass (biogas) power generation ( www.sbigroup.com ).

MayBank of Malaysia is also doing better towards achieving UN SDG 7 and 13. MayBank has almost financed RM 3.45 billion to green energy projects to increase resilience and adaptability to climate-related danger and to provide sustainable and modern energy services.

Green banking has significant contribution toward sustainability

Green banking is a new idea that integrates environmental management and banking operations with the goal of changing the financial industry and creating new, sustainable business models ( Yadav & Pathak, 2013 ). Mumtaz and Smith (2019) analyzed the process of green finance for SD in Pakistan found that scope of green finance is immense as it helps to curb environment issues in addition to making firms more accountable and competitive in reducing carbon emission. ( Jeucken & Bouma, 1999 ) studied relationship between banks and sustainability found that banks are taking more dynamic approach since last 20 years toward attaining sustainability across the world ( Bhardwaj & Malhotra, 2013 ) explained green banking, an initiative by the banks to encourage the development of environmental-friendly enterprises and also helps in the process of natural environment restoration However, green banking, according to the Indian Banks Association, is standard banking system that takes into account all social and environmental elements in order to promote ecological sustainability and the best possible use of natural resources ( Sahi & Pahuja, 2020 ). ( Masukujjaman & Aktar, 2013 ) refers to green banking to environmentally friendly banking system that helps to prevent environmental deterioration in order to make this world more livable. ( Khatun, Mitra, & Sarker, 2021 ) recounted those institutions as banks can make a significantly positive contribution to sustainable economic. When banks engage in sustainable banking programs, it adds to its value creation and is also part of the strategy for banks ( Tyl, Vallet, Nancy, Bocken, & Marion, 2015 ). Green banking being strikingly important toward achieving the UN set sustainable goals; therefore, the implementation of green banking is the main focus of the Central Bank of Bangladesh as found by ( Akter, Siddik, & Mondal, 2018 ). In the Indian city of Coimbatore, GB practices (environmental training of workers, energy-efficient operations, green policy and overall green initiatives) had a favorable impact on banks' environmental performance found in the study done by ( Vidyakala, 2020 ) The goal of green banking is to achieve sustainability, growth and the development that is sustainable through eco-friendly and sustainable banking system ( Brundtland, 1987 ) and sustainability can also be achieved using online banking, e-banking and adhering to the 3D strategy (de-materialization, de-carbonization and de-mobilization) in the bank's daily operations ( Hossen, Uddin, & Hossain, 2014 ). After reviewing the above literature, it can easily be assessed that green banking and sustainability has a positive and significant relationship towards sustainable growth and development across the world. Therefore, previous literature supports the idea that despite having substantial risk exposure, the financial sector has responded to the sustainability issue very perfectly as being the intermediate function in the economy; hence, it is concluded that green banking can never be ignored to achieve sustainability.

We are currently confronted with big difficulties such as global warming, heavy electrical waste, and pollutants. Green banking can help in fighting all these challenges. It is one of the most important pillars toward achieving sustainability. Banks are one of the most significant professional bodies that engage with the general public and have potential for the country's long-term development by raising public awareness and offering education. The influence of banks on the environment could be seen in two different ways: internally and externally. Internal activities such as the use of electricity, water, paper and the quantity of various waste produced during banking operations. It is, however, low in comparison to other areas, but it should not be overlooked. External activities, on either hand, do not directly destroy the environment, but they do implicitly impact the environment by being included in other activities such as investing, lending, risk management and so on. Some internally and externally driving forces can help banks support greening. Employees, investors and directors are internal driving factors who can be driven to greenery and create green products and services, as well as make environmentally friendly policies, and, therefore, contribute to sustainability. External factors include competitors and customers, and sustainable growth can be achieved by raising awareness and demonstrating positive attitudes toward green financing. The green marketing plan is now being used by banks to indicate a responsible corporate marketing activity that views environmental concerns as possibilities for development and growth and verifies them throughout all operations.

The devastating effects of recent flooding, droughts and extreme temperatures that several people all over the world have experienced compelled us to begin thinking about global warming and its consequences, and to do everything we can do to address this problem. Governments, businesses and individuals all play a part in preventing global warming and creating a more sustainable world. We have to deal with financial institutions, particularly banks, which play a vital role in this environment by assisting in the development of a robust and successful low-carbon economics. They should make more use of environmental data when extending credit and making investment decisions. The project will assist them in proactively improving their environmental performance while also adding long-term value to their company. Businesses having a bigger carbon output may be viewed as riskier in the future, and banks may shy away from funding such businesses in favor of innovative technology solutions that absorb or reduce carbon emissions. As a result, green banking is the order of the day, a source for SD and it will undoubtedly benefit banks, industries, as well as the environment at large.

Theoretical implications

The theoretical implications can be summed in the following points: (1) there is no universally accepted framework for green or sustainable banking so far. However, green banking practices are at different stages of development across countries. As per the case of India, green banking practices are at a development phase in India, and green processes have a significant impact on SD. (2) The study is one of the first of its kind in the academic literature as it links green banking practices with sustainability besides discussing green banking practices of the top public sector Bank of India and top commercial bank of Malaysia. Despite the significant contributions made by this study, many disadvantages should be addressed for future research. The present work was chosen for comfort, it was restricted to green banking practices of two banks only, which limits conclusion and interpretation of outcome to some extent Future research can be conducted by a comparative study with the top green banks or with the cleanest country of the world or green banking practices by those banks toward sustainability in that country can also be a good area for research.

Managerial implication

The study is extremely helpful to the banking industry in determining the scope of green banking initiatives in SD. This study is a prime study in India to interrelate banking industry toward sustainability, and two UN SDGs besides green banking practices of banks. This paper has noted the areas where the banks can make progress for the greener, sustainable economics. It has also aided the banking industry in identifying areas for development so that it may focus on improving social satisfaction and satisfaction of stakeholders across its operating areas. The study is also very helpful for banks to comprehend how vital these green initiatives, especially green processes, are to improve sustainability. To the society: The study will serve as a gauge for banking actions toward greener nations and a greener world, since these are the efforts toward carbon free world, efforts toward controlling global warming which undoubtedly is a significant long-term service to society and a reason for better climate and better tomorrow.

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Green banking initiatives: a qualitative study on Indian banking sector

• Published: 02 May 2021
• Volume 24 , pages 293–319, ( 2022 )

Cite this article

• Meenakshi Sharma   ORCID: orcid.org/0000-0002-6841-052X 1 &
• Akanksha Choubey 1  

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The environmental concern is on rise in all types of business; however, banking assumes a special niche due to its ability to influence the economic growth and development of the country. The present study proposes conceptual model of Green banking initiatives and studies the impact of three Green banking initiatives, viz. green products development, green corporate social responsibility and green internal process on two possible outcomes, viz. Green brand image and Green trust. The study is qualitative in nature comprising of semistructured in-depth interviews conducted with 36 middle- to senior-level managers of twelve public and private Indian banks. Banking sector can play a crucial role in greening the banking system by enhancing the availability of finance and serve the needs of a “green economy”. The findings of the study revealed that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services, 53% of the bankers said that their bank incorporates green internal processes in their daily activities, and 78% respondents said that their bank undertakes several green corporate social responsibility initiatives. This investigation further highlights that more than 60% respondents believed that Green banking initiatives have positive role in restoring customer trust through enhanced Green brand image. With dearth of studies on green banking in India, the present qualitative study contributes to the body of knowledge and paves way for future research in green banking for sustainable development.

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1 Introduction

Sustainability today is an “emerging mega-trend” (Lubin & Esty, 2020 ) and a very important business objective to drive green business innovation (Raska & Shaw, 2012 ; Royne et al., 2011 ). Companies like Cisco, HP and Walmart have successfully integrated it into their business practices (Sheth et al., 2010 ). The relevance of green marketing in existent scenario is conspicuous because of environmental concerns amongst marketing researchers and practitioners (Chamorro et al., 2009 ; Peattie & Crane, 2005 ; Ottman et al., 2006 ; Lee, 2008 ; Polonsky, 2011 ; Sharma, 2018 ). Industrialization has resulted in ecological inequality, and corporates are blooming at the expense of local community (Porter & Kramer, 2014 ). Uneven industrialization has disturbed ecological balance and has resulted in natural and industrial disasters (Rehman et al., 2021 ). High levels of environmental pollution have raised social concern over environmental issues (Chen, 2010 ). This environmental concern is surging in divergent businesses. Manufacturing, technology, electronics and IT industries (Bae, 2011 ) all are willingly accepting environmental dedication as a paramount business responsibility (Chen et al., 2006 ).

Banks play a pivotal role in sustainable development of a country, and green banking today has become a phraseology. Due to financial, economic and environmental changes, financial services market is re-shaping and an all-inclusive engagement of ethical proposal and values into banking practices is taking place (Lymperopoulos et al., 2012 ; San-Jose et al., 2009 ). Banking sector facilitates adaptation of environment friendly strategies, mitigates climate risks and supports recovery by diverting funds to climate-sensitive sectors (Part & Kim, 2020 ). Today, environmental and green banking has become synonym with sustainability (Kärnä et al., 2003 ), so banks are broadcasting corporate social responsibility (CSR) activities (Scholtens, 2011 ). Banks globally are investing substantially in green strategies (Evangelinos et al., 2009 ) to create green image. Greening of bank is further reducing carbon footprints from banking activities, and this is mutually beneficial to the banks, industries and the economy (Bihari & Pandey, 2015 ).

Many relevant studies have been conducted on green banking before. Scholtens assigns green bank marketing as a component of larger CSR concept. Economic agents banks play an important role in financing environment-friendly projects (Nizam et al., 2019 ) and thus contribute towards society (Rehman et al., 2021 ). Kärnä et al. ( 2003 ) and Grove et al. ( 1996 ) explained association between green marketing and CSR, in non-banking sector. Lymperopoulos et al. ( 2012 ) tested the favorable impact of green bank marketing and green image ; for Evangelinos et al. ( 2009 ) development of green services was the prime focus. Kumar and Prakash ( 2018 ) also opine that implementing sustainable banking practices can be a strong stimulus to sustainable development and points towards scarcity of studies related to sustainable banking in Indian banks. Nizam et al. ( 2019 ) emphasized the need for implementing Green banking initiatives in routine operations, whereas Masukujjaman et al. ( 2017 ) talked about pivotal role played by green banking in developing economies at social, corporate and environmental level.

Developed nations have attracted major research on green banking though developing nations have ignored them (Khan et al., 2015 ; Jeucken, 2015 ; Amacanin, 2005 ; Scholtens, 2011 ; Roca & Searcy, 2012 ; Weber, 2016 ), and in countries like India research on green banking is relatively undiscovered (Prakash et al., 2018 ). Majority of research in India is on corporate social responsibility and management of environment (Biswas, 2011 ; Narwal, 2007 ; Rajput et al., 2013 ; Sahoo & Nayak, 2007 ; Sharma & Mani, 2013 ), green banking strategies (Bihari, 2010 ; Bahl, 2012 ; Jha & Bhoome, 2013 ; Tara & Singh, 2014 ) and green practices adopted by public and private sector banks.

Equator Principles (EPs) and United National Environmental Protection Finance initiative (UNEPF1) and Equator Principles (EPs) promote sustainable development through financial institutions. It has been embraced by more than 200 member nations, and India also being a member nation is following the guidelines of RBI (Reserve Bank of India, 2017). However, despite taking vigorous steps by Indian government, sustainability is yet to dribble down to ordinary people.

Communication gap between the various stakeholders, lack of awareness, lack of green image of the banks and lack of trust are amongst the various reasons why the outcome of the green outreach by the banks is not as expected. Lymperopoulos et al. ( 2012 ) empirically validated that green bank marketing positively influences green image of the bank. However, no such study has been conducted in Indian scenario. The impact of Green banking initiatives to enhance the Green trust and further Green brand image has not been studied so far in Indian scenario.

Henceforth, there is a need to develop a framework that will fill the research gaps by asking following research questions:

What are the Green banking initiatives of leading Indian public and private banks?

What are the major challenges for Indian banks towards “going green”?

How the Green banking initiatives contribute towards creation of Green trust?

How the Green banking initiatives contribute towards creation of Green brand image?

The remaining of this paper is organized as follows: the next section discusses literature review which throws light on green banking, Green banking initiatives in India and challenges of implementing Green banking initiatives in India. Thereafter, the outcomes of Green banking initiatives, viz. Green brand image and Green trust, are discussed as subsections. Afterwards, the research methodology is explained with the help of techniques used for data collection and data analysis. Thereby, findings are discussed which elucidate how research questions are answered. The study is concluded by highlighting the implications and limitations of the research.

2 Literature review

2.1 green banking.

Green banking was initially introduced in the year 2009 in State of Florida. In India, SBI (state bank of India) being the largest commercial bank took a lead towards setting higher standards of sustainability and undertook foremost step towards “green banking” initiative. SBI was the first bank to inaugurate wind farm project in Coimbatore.

Green banking is a form of banking activity where the banks take initiative to do its daily activates as a conscious entity in the society by considering in-house and external environmental sustainability. The banks who do such type of banking activities are termed as socially responsible and a sustainable bank or green bank or ethical bank (Hossain et al., 2020 ; Zhixia et al., 2018 ).

A green bank is a bank that promotes and enacts green technologies in bank operations both internally and externally to minimize carbon footprints and facilitates environment management (Bose et al., 2017 ). It is an influencer for holistic growth of economy in the nation (Jeucken & Bouma, 1999 ; UNEP FI, 2016 ). Green banks adopt social and economic aspect into their strategies and progress towards sustainable practices (UNEP FI, 2011 , 2017 ).

According to Indian Banks Association, green banking refers to a normal banking system which involves all environmental as well as social factors with an aim to ensure ecological sustainability and optimum use of natural resources (Scholtens, 2009 ; Lymperopoulos et al., 2012 ; Kumar & Prakash, 2018 ; Sahi & Pahuja, 2020 ). Hermes et al. ( 2005 ) said that banks involve a shift from traditional towards sustainable practices and social, governance and environment criteria are being integrated into their core strategy. Scholtens ( 2009 ) has explained the concept of green corporate social responsibility in banking and pronounces that a green bank offers savings accounts to stakeholders, ensuring that the savings will finance sustainable projects. He developed a framework to assess the social responsibility of global banks and further tested it on 30 institutions and concluded that there is a positive and significant association between a bank’s CSR score and its financial size and quality. As per Evangelinos et al. ( 2009 ), development of green products like green financial products, loans for renewable energies, greener technologies, green lending and environmental management strategies is green marketing in bank. This improves banks’ reputation and contribute towards sustainability. This has motivated several banks implementing green strategies to invest in developing environmental image to better prepare for future challenges.

Lymperopoulos et al. ( 2012 ) verified empirically that banking initiatives that are green result in a favorable, green image. His green bank marketing construct is comprised of green corporate social responsibility (GCSR), green internal process (GIP) and green product development (GPD).

According to (Dewi & Dewi, 2017 ), green banking promotes environment-friendly practices in banking sector. He further postulated that green banking guides the bank’s core operation towards sustainability. Kumar and Prakash ( 2018 ) have studied the adoption level of sustainable banking tools and categorized 40 criteria into five heads. They further used content analysis to evaluate the sustainable practices of Indian banks and concluded that green banking adoption is still at the nascent stage in Indian banking.

As a part of Green banking initiatives, several banks throughout the globe and NBFIs have adopted eco-friendly mechanisms for financing as well as green transformation of internal operations. For instance, banks in nations like Bangladesh, Brazil, Columbia and Indonesia have started practicing green banking relatively along the lines of the policy framework (Bahl, 2012 ; Rahman & Akhtar, 2016 ). Bank of Ceylon in its annual report of 2015 stated that all their services and goods are driven towards more technology-oriented platforms which helps in reduction of carbon footprints. Also, peoples bank has initiated a paradigm shift to its old model of banking (Oyegunle & Weber, 2015 ). Banks in China, Turkey, Mongolia, Vietnam, Indonesia, Kenya and Peru have also introduced green banking concepts like SmartGen with mobile and internet-oriented passbook free application, fortune branches being installed and initiation of smart zones (Scholtens, 2009 ; Bank of Ceylon, 2015 ; Herath & Herath, 2019 ).

Currently, Indian banks are seen being desirable towards entering global markets (Laskowska, 2018 ; Nuryakin & Maryati, 2020 ; Paramesswari, 2018 ), and it has become important that they recognize their environmental and social responsibilities (Prasanth et al., 2018 ; Sahi & Pahuja, 2020 ; Zhixia et al., 2018 ). As a result, green strategies have become prevalent, not only amongst smaller alternative and cooperative banks, but also amongst diversified financial service providers, asset management firms and insurance companies (Allen & Craig, 2016 ; Gopalakrishnan & Priya, 2020 ; Hossain et al., 2020 ; Kapoor et al., 2016 ).

2.2 Green banking initiatives in India

Green initiatives may be referred to as developing green products which consume less energy, and accordingly distribution, pricing and communication strategies follow. Peattie and Charter ( 1994 ) have defined green marketing as a comprehensive process of management which identify, anticipate and satisfy the needs of customers and society, in a fruitful and sustainable way.

Banking defines green marketing in a similar manner as other industries do. Evangelinos et al. ( 2009 ) defined green bank marketing as developing an innovative environment-friendly financial product like green loans that finance clean technology, and green strategies, like waste management programs and energy efficiency to augment banks’ green reputation and performance.

Green marketing by banks or green initiatives forms a favorable eco-friendly image that satisfies the customer’s green needs and green desires (Chang & Fong, 2010 ) and contributes towards sustainable development (Portney, 2008 ). Several banks are already implementing green banking, green strategies and building their green image to handle existing confrontation. Such green actions can help banks to procure environmental reputation and inculcate their environmental concern (Evangelinos et al., 2009 ; Lymperopoulos et al., 2012 ; Portney, 2008 ).

Green marketing in banks should address green methods and process (Kärnä et al., 2003 ) that suggests green communication also be a part of green initiatives. Evangelinos et al. ( 2009 ) suggest three aspects of green bank marketing in banking literature: lending decisions of banks should be based on environmental criteria; bank environmental management strategies; and developing environmental financial products. He suggested that “green” marketing refers to development of new green financial products that improves banks reputation and performance.

Lymperopoulos et al. ( 2012 ) empirically validated that green bank marketing which comprises of green product development (GPD), green corporate social responsibility (GCSR) and green internal processing (GIP) is a complex concept, is crucial for the bank’s green image (Hartmann et al., 2005 ) and critically contributes in developing customer loyalty and satisfaction (Chang & Fong, 2010 ).

Role of CSR in banks in creating Green brand image has not been explored much (Lymperopoulos et al., 2012 ). CSR is decision making in business, and it has ethical values, compliance with law and regards for environment, communities and people, communities attached to it. Banking relates to CSR with reference to cause-related marketing, ethical issues concerning minority and environment and quality of life (Donaldson & Dunfee, 2002 ). GCSR in banking has been emphasized by Scholtens ( 2009 ), as a socially responsible bank that safeguard savings that are financing environmental projects.

In the contemporary circumstances in market, financial service sector has been reshaped, demanding fresh marketing insight with an aim to provide instructions for successful practice. Going ecological has become a massive trend in the banking industry worldwide. The idea of green banking has encouraged banks to familiarize with paperless, technology driven goods and services while curtailing ecological impacts and performing their role as a corporate citizen on country’s development. The need of the hour is to understand the demand for green initiatives because the eventual success or even failures of these investments are influenced by apparent satisfaction of green consumers. They also assist banks to develop environmental reputation and concern, which is has become imperative today.

Several issues of green marketing like green corporate social responsibility, green product development and green internal processing are addressed by previous studies (Scholtens, 2009 ; Evangelinos et al., 2009 ; Lymperopoulos et al., 2012 ; Herath & Herath, 2019 ) and are long established by several experts, as measurements of Green banking initiatives. Additionally, the outcomes of several qualitative research underline the major contribution of GCSR as an accomplishment for green banks, thereby backing up several former studies (Grove et al., 1996 ; Kärnä et al., 2003 ). Green communications also form an important part of green initiatives as the success of implementation depends upon how well they are communicated to the masses. Lymperopoulos et al. ( 2012 ) also pointed out that environmental awareness can be included in green banking.

India lags other market economies that are in emerging stage in terms of distinctive sustainability policy for their banking practices. Ministry of Finance and RBI together are focusing on developing a policy framework specifically for Indian green banking sector (Roy, 2017 ; Kumar & Prakash, 2018 ). The present study has clubbed the Green banking initiatives of leading Indian private and public sector banks in Indian banking into three categories, viz. green product development, green corporate social responsibility and green internal process (Scholtens, 2009 ; Evangelinos et al., 2009 ; Lymperopoulos et al., 2012 ) as presented in Table 1 . Table 1 explains the three categories of Green banking initiatives, viz. GPD, GCSR and GIP, and different products introduced by different banks under each category.

2.2.1 Green product development

Green product development has actually become the major strategic consideration for several firms throughout the globe because of the ecological regulations and public awareness of eco-friendly practices (Nuryakin & Maryati, 2020 ; Paramesswari, 2018 ). Green product development can be defined as development of business loans for green logistics and waste management, renewable energy sources, loans granted to produce organic products, green mutual funds, stimulating purchase of hybrid cars and other green products, installing photovoltaic systems and investing in production of eco-friendly products (Lymperopoulos et al., 2012 ), green mortgages and green bonds (Campiglio, 2016 ; Kumar & Prakash, 2018 ) and climate fund (Jeucken, 2001 ; Scholtens, 2009 ; Islam et al., 2016 ; GRI G4-FSS1,8, EN6). GPD emphasizes on “end of pipe technology” where organizations are well aware of environmental issues via procedure of production and product design. As per Chen (2001), the product designed to minimize the use of non-renewable resource and avoid toxic materials and renewable resource during its whole life-cycle would be the most effective to display green technological development (Driessen et al., 2013 ; Fraccascia et al., 2018 ; Gopalakrishnan & Priya, 2020 ; Nuryakin & Maryati, 2020 ; Prasanth et al., 2018 ; Yan & Yazdanifard, 2014 ).

2.2.2 Green corporate social responsibility

Green corporate social responsibility (GCSR) can be described as the environmental aspect of CSR—the duty to cover the environmental implications of the company’s operations and the minimization of practices that might adversely affect the enjoyment of the country’s resources by future generations (Laskowska, 2018 ; Nuryakin & Maryati, 2020 ). It can be defined as development of community involvement program (GRI G4-26; Mitra & Schmidpeter, 2017 ; Hossain & Reaz2 007), charity and sponsoring (Jeucken, 2001 ; Scholtens, 2009 ; GRI G4-EC1; Islam et al., 2016 ; Shukla & Donovan, 2014 ) and health care and sanitation program (Hossain & Reaz, 2007 ; Narwal, 2007 ). Access points for financial services in low populated or remote areas of the country (GRI FSS 13; Kumar et al., 2015 ) improve access to financial services for disadvantaged people (GRI FSS 14; Hossain & Reaz, 2007 ; Sarma & Pais, 2011 ). GCSR can decrease business risk, rally reputation as well as afford opportunities for cost savings .  Thus, GCSR is no longer a luxury but a requirement . While much of the drive for sustainability has come from regulatory directives, research has shown that if implemented constructively, GCSR can drive business performance improvements in many areas ( Allen & Craig, 2016 ; Nuryakin & Maryati, 2020 ) .

2.2.3 Green internal process

Green internal process can be defined as relevant strategies for maximizing the utilization of bank’s resources and preserving energy such as saving paper and water, recycling and providing eco-friendly equipment; appropriate curriculum for personnel training to safeguard environment; and upgraded internal functions in to insulate the environment.

2.2.4 Challenges of implementing Green banking initiatives

Implementing Green banking initiatives in India involves a lot of problems. There is a lack of awareness amongst the customers and the bank employees about the concept of “green banking” and even if they are aware, the information they have is inaccurate (The Boston Consulting Group, 2009 ; Jayadatta & Nitin, 2017 ; Sharma et al., 2014 ; Maheshwari, 2014 ; Rastogi & Khan, 2015 ; Sindhu, 2015 ). A huge gap has been found in what banks want or try to spread and what people think of banks to be doing regarding green banking (Jayadatta & Nitin, 2017 ; The Boston Consulting Group, 2009 ). Green washing has led consumers to doubt towards environmental advertising and has led to increase in skepticism that has negative influence on green brand equity (Alniacik & Yilmaz, 2012 ; Shrum et al., 1995 ). It was found that almost three-fourth of people using online facilities provided by their banks were unaware of the term green banking or misunderstood it with digital banking (Sharma et al., 2014 ; Maheshwari, 2014 ; Rastogi & Khan, 2015 ; Sindhu, 2015 ). Awareness of green banking is especially less within middle and senior age groups (Sahoo et al., 2016 ). Henceforth, significant gap in terms of studying the impact of demographic exists.

Inclusive growth of economy requires a robust and healthy banking practices (Kumar & Prakash, 2018 ) Most of the activities of a green bank in India are focused on ATMs, internet banking, paperless banking, etc. (Biswas, 2011 ). It is also researched that Indian banks are not so well equipped to implement Green banking initiatives (Rajput et al., 2013 ), and they still have a long way to go (Kumar & Prakash, 2018 ). Reserve Bank of India is a major contributor in facilitating environmental policies. A developing country like India requires more thrust on the social dimension of banking and couples it with economic growth (UNEP FI, 2017 ). Limited Indian banks have advocated the green banking principles as per international standard. There is a need to improve regulatory framework (UNEP FI, 2011 ).

3 Outcomes of Green banking initiatives

3.1 green brand image.

Chang and Fong ( 2010 ) defined green corporate image as “the perceptions developed from the interaction among the institute, personnel, customers and the community that are linked to environmental commitments and environmental concerns”. If the green products of a company are reliable and stable, they converge with the environmental needs of consumer, enjoy excellent environmental performance and have green reputation that company will relish green image. According to Chen ( 2010 ), Green brand image is when a product is perceived by the customers as having green commitment and green concerns. It is accepted via its competence in green reputation, success in sustainable achievement and trustworthiness of environmental promises. Chen ( 2010 ) also endorsed that green marketing positively influences a company to obtain competitive advantages, enhance corporate image and product value and hunt for innovative opportunities in market and augment the product value with reference to information technology products. Hartmann et al. ( 2005 ) posit that an efficiently chalked out green positioning strategy can provide direction towards more appreciative brand perceptions.

In the banking studies, green bank image is related to bank superiority substantially and reputation in their environmental endeavor vis-a-vis competition (Lewis & Soureli, 2006 ). This clubbed with the impression of the customers plays an important role in describing Green brand image (Nguyen & LeBlanc, 2001 ). Further, green bank image can help in retaining the customers, winning back the lost and attracting new ones, thus leading to banks’ prosperity and future sustainability. Thus, it can be presumed that corporate image has a substantial impact on customer loyalty and achieving the fundamentals of green marketing (Chaudhuri, 1997 ; Chen & Chang, 2013 ; Lewis & Weigert, 1985 ; Mitchell et al., 1997 ).

3.2 Green trust

Rotter ( 1971 ) defined trust as the extent to which a party can entrust on another party’s word, statement or promise. Hart and Saunders ( 1997 ) believe that trust is the assurance that others would behave as is conventional based on integrity, ability and benevolence (Schurr & Ozanne, 1985 ), a degree of willingness to believe another party based on ability, reliability and benevolence (Ganesan, 1994 ). Green trust is a willingness to rely on a product, brand or service or expectation arising out of its ability and credibility because of its environmental performance (Chen, 2010 ). Prior research has shown a positive relationship between trust and long-term consumer behavior (Lee et al., 2011 ) and purchase intentions (Harris & Goode, 2010 ; Schlosser et al., 2006 ) and is an antecedent of the same (Van der Heijden et al., 2003 ). Chen and Chang ( 2013 ) endorse that green initiatives can enhance customer trust and their willingness to purchase a product or service (Gefen & Straub, 2004 ).

Henceforth, it can be concluded that Green banking initiatives will have positive influence on Green trust and customers’ green expectations. However, exaggerating the green performance can also lead to reluctance of customers to trust (Kalafatis et al., 1999 ). For a bank to gain Green trust of its customer, its environmental performance, expectations and promises should be reliable, dependable and trustworthy (Chen, 2010 ); more information about the “greenness” of product should be shared with stakeholders (Chen & Chang, 2013 ); else it can give rise to mistrust (Jain & Kaur, 2004 ). Table 2 summarizes the various items of the major constructs of the study, viz. Green banking initiatives, Green brand image and Green trust.

4 Proposed framework

This research develops a conceptual framework (Fig.  1 ) that illustrates the impact of Green banking initiatives on Green brand image and Green trust. Green banking initiatives consist of three items, viz. green product development, green corporate social responsibility and green internal process (Lymperopoulos et al., 2012 ). The outcomes of Green banking initiatives are Green brand image measured by four items in the scale by Chen ( 2010 ) and Green trust measured by five items in the scale given by (Chen & Chang, 2013 ) (Table 2 ).

Conceptual model of Green banking initiatives

Green banking initiatives positively influence Green brand image (Lymperopoulos et al., 2012 ), and Green banking initiatives enhance customer trust and their willingness to purchase a product or service (Gefen & Straub, 2004 ).

5 Methodology and case study

As mentioned before, there is dearth of extensive study on green banking in India. Henceforth, the need for exploratory research is realized and chosen for the present study. Qualitative research provides a deep-seated understanding of the experience or case under observation and study by illuminating uncovering loosely connected insights and taking forward the casual relationship. Use of qualitative research is more apt for formulization and theory dissemination in the background when not much is public about the elemental variance. According to Eisenhardt ( 1989 ), developing a case study method which is based on theory is the favored investigation technique which assist not only in testing but also provoke innovative policy in new arenas.

The present analysis is based on multiple case study where the same phenomenon is investigated in multiple situations. However, the multiple cases shall be selected in such a careful manner so that it either anticipates analogous outcome or anticipates contradictory outcome for anticipated inference (Yin, 2003 ). The above-mentioned twin conditions are addressed in the present study by taking into consideration more than one branch of the same bank and branches from different banks. Henceforth, the findings obtained from analysis of each case from contrasting groups (between State bank of India (SBI), Punjab National Bank (PNB), Bank of Baroda (BOB), Canara Bank, ICICI Bank Ltd, HDFC Bank Ltd, Axis Bank Ltd, Kotak Mahindra Bank, IndusInd Bank, YES Bank, IDFC Group, IDBI) were regarded as object of comparison and the results from each case from similar group (amongst three branches of SBI or three branches of PNB) are findings which further exaggerate the understanding of Green banking initiatives of Indian banks.

In comparison to a single case study, multiple case study provides more sturdy, persuasive and conclusive results. Furthermore, the findings from multiple case study can be hypothesized to a larger extent and collaborate in theory building. Henceforth, a study based on multiple case study is more accurate, logical, and sound (Ray & Sharma, 2019 ). The findings accomplished from multiple case study method are more robust and trustworthy (Baxter & Jack, 2008 ). They allow for a comprehensive development of research questions and academic transformation. The results validate the described complementary and comparative findings to enrich the knowledge base of green banking.

5.1 Exploratory interviews

As the study is exploratory in nature, the research questions focused on what (do you […], e.g., believe?), how (do you […], e.g., feel?), why (do you […], e.g., believe?), in contrast to how much and how many and other quantifiable question. Exploratory interviews were found to be more fruitful technique of providing relevant information deemed necessary for developing a new theory (Amaratunga et al., 2002 ; De Ruyter & Scholl, 1998 ). Several probing questions like “what are the Green banking initiatives used by your bank?”, “what are the problems faced in communicating Green banking initiatives?” …….” Were asked to reveal as much information as possible. The benefit of asking such practical questions was that they provided a structure for reference and conceded the researcher to explore deeper and get analytical. Laddering and funneling techniques were used (Eisenhardt & Graebner, 2007 ; Kvale & Brinkmann, 2009 ) to discover the hidden meaning. The questions were semistructured so had flexibility of words and sequence guided by interviewee’s response. Divergent themes and subthemes were explored dictated by interviewee’s interest and expertise. The focus of the conversation was on green initiatives, their impact on Green brand image and Green trust. This directed the study to conduct interviews in the form of conversation, which were deemed apt for the study’s exploratory nature. It was also considered relevant to conduct detailed analysis (Flick, 2009 ).

5.2 Data collection

Exploratory research design has been used in the present study, and data have been collected by interviewing 36 middle to senior level bank employees from 12 public and private sector banks. Twelve banks that were targeted were State Bank of India (SBI), Punjab National Bank (PNB), Bank of Baroda (BOB), Canara Bank, ICICI Bank Ltd, HDFC Bank Ltd, Axis Bank Ltd, Kotak Mahindra Bank, IndusInd Bank, YES Bank, IDFC Group, IDBI from Delhi NCR region. From each bank three middle-level managers were selected using purposive sampling and were interviewed using semistructured questionnaire method. The chosen respondents with their knowledge and expertise answered the semistructured questionnaire, and this helped in gathering critical points and in-depth knowledge of different aspects of green banking. The theoretical insights that emerged increased the likelihood to expand based on emergent theories (Baker, 2002 ; Eisenhardt & Graebner, 2007 ). As not much research has been done in green banking in India, if analysis had considered a sample up to twelve for conducting in-depth interviews it was considered sufficient (Carson et al., 2001 ). However, this investigation conducted in-depth semistructured interviews with 36 banking sector employees. The detailed profile of the respondents is provided in Table 3 .

The details of the interview were duly recorded and were written on paper. The interview lasted for 50 min on an average, varying from 30 to 90 min (total number of hours exceeding 30 h). Interviews were conducted face to face, and each interview was classified into tables encompassing the most relevant headings under research (as explained earlier), to organize the data. This phenomenon focused attention on distinctive opinions and segregated those from customary perspective shared. Repetitive and interpreted logic produced strong hypothesis development. With the help of in-text, entwined with germane literature, the liaison between factual documentation and emerging theory was established (Amaratunga et al., 2002 ; Eisenhardt & Graebner, 2007 ).

5.3 Data analysis

After reaching the point of exhaustion when no contribution was done by new interviews, data analysis was done. The data were analyzed based on conceptual framework (Fig.  1 ) once interview material was transcripted. Thereafter, process of data analysis was initiated wherein for each item in the interview detailed content analysis was performed (Flick, 2009 ) to remove the crucial facets. It was followed by an interesting exercise of highlighting the cut-outs and freezing the nucleus statements in association with the conceptual framework in Fig.  1 (Saldaña, 2012 ). Characterization was performed for each interview, cut-outs found intriguing were underlined, and further important statements were frozen in association with the conceptual framework (Saldaña, 2012 ). A characterization emerged out of each interview. Then intensity analysis was performed wherein excellent responses were analyzed further to compare the phenomenon under study.

This step involved following robust quality criteria. Every phase was documented, memos were critically written, and motivation for each interpretation was worked upon. Coding of the interview took place in two cycles, and the crucial facets of the findings were assigned to the major categories of Green banking initiatives, Green brand image and Green trust. Next, the extensive interview material was immersed as it was private investigation of an exclusive interview. The objective of such technique was to point at the trends emerging and to reinforce them all with fitting justifications. In summation, demonstration was for the main constructs explained at the time of interviews in a pattern (i.e., putting together coding cycles) (Fig.  1 ). Consequently, the indicators were categorized. Content analysis and topic-based analysis together justified and verified the authenticity of the analysis.

The qualitative data for the study were collected with the help of in-depth personal interviews conducted with the bank employees. The data developed thereafter provided relevant insight. Numerous green marketing issues, such as GPD and GIP already addressed before by previous research (Evangelinos et al., 2009 ; Lymperopoulos et al., 2012 ), were confirmed as components of green marketing by the practitioners. The findings of qualitative analysis conducted in the study highlighted the role of GCSR as a crucial factor for success of green bank marketing (Grove et al., 1996 ; Kärnä et al., 2003 ; Lymperopoulos et al., 2012 ).

6 Findings and discussion

The present study aims to provide answer to the following research questions:

The process adopted in the paper is depicted with the help of flowchart in Fig.  2 . The study begins with the introduction and now has moved to the findings and discussions by answering the research questions identified in the beginning of the study.

Workflow of the research paper

The Green banking initiatives in the paper are divided into three major categories: green products development, green corporate social responsibility and green internal process. They are further summarized in detail in Table 1 along with different products introduced under different heads by different banks under consideration. All the 36 respondents agreed that the twelve public sector and private sector banks are using these Green banking initiatives.

One branch manager of a leading public sector bank stated: The bank has come up with several green products and services like green loans/green financing of energy efficient projects, promote renewable energy, green vehicle finance, loans for constructing green buildings etc .

Another branch manager stated: My bank is involved in several green corporate social responsibility activities as a part of green initiatives like tree plantation campaigns, maintenance of parks, promoting environmental literacy etc .

One of the regional managers commented: Bank is implementing responsible waste management disposal systems, rainwater harvesting, use of more daylight, using emails and internal network communication instead of paper-based documentation.

Another AGM said: Implementing green banking has always been a major issue but it plays an important role in the development of a developing nation like India .

Majority of the bank employees agreed that now both public and private sector banks are taking steps to implement Green banking initiatives. They also commented about the reputation risks involved from financing environmentally objectionable projects (Sahi & Pahuja, 2020 ; Zhixia et al., 2018 ).

In a country like India with literacy rate of 70% on an average, green banking is still at a nascent stage and desired results have not been achieved (Kumar & Prakash, 2018 ). The analysis revealed that there were multifold reasons attributed to it. The bank employees provided very valuable and honest insight during the semistructured interviews.

One of the regional managers commented: People have trust issue with green goods and services. Most of the customers are uncomfortable adopting new tools and technologies.

Branch manager said: Many customers are not aware of several green tools and technologies resulting in no use or less use of them .

Another commented: Elderly and uneducated people are less adaptable towards green products and services.

There were several other comments as mentioned below:

Staff training is a major task as few older staff are reluctant towards the change.

Green goods and services increase bank’s cost at least initially though reduces administrative cost in the long run.

The major problem bank faces in this process is of customers not accepting the online transactions happily.

Customers are skeptical towards safety in transactions undertaken online; however, educated people easily adopt green technologies.

Majority of the bank employees agreed that a proactive way of future sustainability is Green banking, but banks in India are running far behind their counterparts from developed nations because of lack of education, lack of awareness and lack of preparedness of Indian banks to implement green initiatives (Jayadatta & Nitin, 2017 ; The Boston Consulting Group, 2009 ). However, there was a consensus that a lot needs be done till green banking percolate to grassroots level and this was not possible till all stakeholders, i.e., government, bankers and customers work in union to achieve it (Kumar & Prakash, 2018 ).

Green trust is a willingness to rely on a product, brand or service or expectation arising out of its environmental performance. The Green banking initiatives if successfully explained and implemented will enhance customer’s trust in bank and will positively influence their purchasing decisions.

One of the bank managers stated: Bank’s priority must be to make customers do everything themselves digitally without being dependent on bank. This will increase their confidence and enhance their trust on the bank.

Another bank employee stated: My bank undertakes several green corporate social responsibility activities like tree plantation, maintenance of parks etc . They enhance our reputation and reliability.

Regional manager said: One of our customers told me that he participated in the marathon sponsored by our bank. He very proudly told other participants that he has account in our bank, and we are very committed to the environmental cause.

Another employee stated: One customer came to me and said that he read in the newspaper that our bank is a signatory to UNEP F1 and adhere to UN Global Compact Principles. He said that he was very much impressed that our bank keeps promises and commitments for environmental protection.

Hence, based on comments received it can be affirmatively concluded that Green banking initiatives in the form of green products and services, green corporate social responsibility and green internal process can go a long way in creating Green trust of all stakeholders (Chen, 2010 ).

Researchers studied the relation amid green banking and Green brand image resulting to the conclusion that a positive relation actually exists amid the banks undertaking Green banking initiatives and the development that takes place in terms of improving the banks brand image (Chang & Fong, 2010 ; Hartmann et al., 2005 ; Lymperopoulos et al., 2012 ).

One manager stated: Green initiatives have influenced all our eco-friendly and environmentally concerned consumers and they through positive word of mouth have augmented bank’s green image within the society.

Regional manager Commented: Steps taken to create environmental awareness has created Green brand image amongst our ecofriendly customers. This in future will be a driver of satisfaction and loyalty. Bank green corporate social responsibility initiatives like sponsorship for protection of wildlife, development of school fees collection modules etc . augment the banks green image.

One bank employee said: One of the customers told me that he saw two ambulances donated by this bank outside an eye hospital. A slogan on environmental protection was painted on the ambulance. He was very touched. His impression of our bank’s reputation got enhanced.

The above reviews guide the researchers to conclude that Green banking initiatives in the form of green products and services, green corporate social responsibility and green internal process contribute towards creation of Green brand image of the bank (Chaudhuri, 1997 ; Chen & Chang, 2013 ; Lewis & Weigert, 1985 ; Mitchell et al., 1997 ).

On the basis of content analysis in Table 4 , it can be concluded that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services; 53% of the bankers said that their bank incorporates green internal processes in their daily activities; 78% respondents said that their bank undertakes several GCSR actions like marathon for promoting sustainability, reduction of carbon footprints, green loans, green mortgages etc.; and 22% of respondents were of view that their bank still has a long way to go for fulfilling its green corporate social responsibilities. Though 84% of bankers believe that their bank is concerned as a benchmark for ecological commitment, 16% bankers said that their bank is far away from setting a standard for Green banking initiatives. Majority, i.e., 70%, of bankers feel that their bank is very professional when it comes to environmental protection, but 30% said that their bank is still an amateur in undertaking green initiatives and thereby fails to embark on environmental protection. Though when it comes to fulfillment of ecological performance and success in the same, half of the respondents agree and half disagree to this fact. Majority of respondents, i.e., 63%, said that their bank undertakes many actions to build its establishment towards environmental concern and approximately same, i.e., 65% bankers also said that their bank seems to be trustworthy when it comes to environmental argument that it puts amongst its customers. A widely held belief observed amongst bankers was regarding reliability of bank’s environmental commitments, to which 84% agreed and, merely 16% denied. Same results were attained when it came to dependability on the bank’s environmental performance. Even though bankers feel that their banks try and perform as much as possible towards ecological concerns, and even 63% felt that their bank keeps its promises for environmental performance, yet majority of them feel that expectations are yet not fulfilled and almost all the bankers were of view that banks face a lot of challenges like difficulty in gaining trust, lack of ease with digital forms, cybercrimes, hacking risks, etc., while implementing green initiatives. Results of content analysis are also depicted using bar graphs in Fig.  3 .

Results of the content analysis using bar graphs

7 Conclusions

To facilitate the market transformation demanded in Paris agreement, green banks play a critical role to meet the goal of restricting global warming (Ihlen, 2009 ; Kolk & Pinkse, 2005 ; Miah et al., 2020 ). Banks needs to apply morality of sustainability and responsibility to their business model. By adopting the environmental factors in their lending activities, banks can gain public trust and also fulfill their responsibility towards the society. Green banking, if implemented sincerely, will act as an effective measure for attaining people’s trust and building bank’s brand image (Chen, 2010 ).

Countries like USA, UK, Australia, Japan and Malaysia have embedded Green banking initiatives, guidelines and principles in their banking system (Meng et al., 2019 ; Thompson & Cowton, 2004 ); however, India has a long way to go vis-a-vis their developed counterparts (Scholtens, 2009 ; Bank of Ceylon, 2015 ; Herath & Herath, 2019 ) and require strong motivation and reinforcement to do so. In such a backdrop, the present study has relevant theoretical, social and managerial implications.

The present study proposed conceptual model of Green banking initiatives in Fig.  1 with three antecedents of Green banking initiatives, viz. green products development, green corporate social responsibility and green internal process with two green banking outcomes: Green brand image and Green trust with themes and dimensions as described in Table 2 . Based on the findings of semistructured interviews and discussions; thereafter, the proposed relationship in the conceptual model was appropriately concluded. This investigation highlights the role of Green banking initiatives in restoring customer trust through enhanced green image. The study has successfully answered all the four research questions posed in the beginning of the study.

In response to RQ1, the study suggests that majority of public and private sector banks are implementing Green banking initiatives in the form of Green product development like Green loans, green financing, green mortgages, loans for green construction, etc.; Green corporate social responsibility like green credit cards, internet banking, green savings account, payment of school fees through ATM, solar ATM, green CDs, green awareness programs; and green internal process like use of more daylight, employee training on green initiatives, conducting energy audits, using internal network communication (Herath & Herath, 2019 ; Lymperopoulos et al., 2012 ; Sudhalaksmi & Chinnadorai, 2014 ).Quantitative analysis revealed that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services; 53% of the bankers said that their bank incorporates green internal processes in their daily activities; and 78% respondents said that their bank undertakes several GCSR.

The study revealed very valid information regarding the major challenges for Indian banks towards “going green”. It was found that there are lack of awareness, lack of education and presence of green washing (The Boston Consulting Group, 2009 ; Jayadatta & Nitin, 2017 ; Shrum et al., 1995 ; Alniacik & Yilmaz, 2012 ; Sharma et al., 2014 ; Maheshwari, 2014 ; Rastogi & Khan, 2015 ; Sindhu, 2015 ) because of which Indian banks were not able to meet international standard. Need for improved regulatory framework and collaborated efforts of all stakeholders was also found imperative in achieving the required goals (Miah et al., 2020 ). Previous studies clearly point out towards multi-stakeholder involvement in facilitating green building adoption (Bukhari et al., 2020 ).

Bank employees revealed that engaging in green corporate social responsibility activities like tree plantation, organizing marathons, undertaking green internal processes like reducing paper usage, using digital banking safely, launching green counters and green credit cards all enhance consumer’s trust in green activities of banks and create Green trust (Chen, 2010 ; Lymperopoulos et al., 2012 ; Hossain et al., 2020 ).

The study revealed a positive relationship between Green banking initiatives and Green brand image. The bank employees confirmed that eco-friendly consumers were very proud of Green banking initiatives and also created positive word of mouth that helped in creation of Green brand image that helps in achieving customer loyalty and the fundamentals of green marketing (Chaudhuri, 1997 ; Chen & Chang, 2013 ; Lewis & Weigert, 1985 ; Mitchell et al., 1997 ).

On the basis of in-depth interviews, the study further concludes that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services; 53% of the bankers said that their bank incorporates green internal processes in their daily activities; and 78% respondents said that their bank undertakes several green corporate social responsibility initiatives. This investigation further highlights that more than 60% respondents believed that Green banking initiatives have positive role in restoring customer trust through enhanced green image.

8 Suggestions and implications of the study

The theoretical implication of the present research is to validate using qualitative research the positive relationship between Green banking initiatives, Green trust and Green brand image of the Indian banks. The semistructured interview of thirty-six middle- to senior-level bank managers of twelve banks has very lucidly thrown light on the challenges and the proposed conceptual framework comprising of three constructs, viz. Green banking initiatives, Green trust and Green brand image. With dearth of studies on green banking in India, the present qualitative study makes valuable contribution to the body of knowledge and paves way for future research in green banking for sustainable development.

The present study’s managerial implications are wide ranging. The investigation clearly states that if Green banking initiatives are implemented effectively, augmenting environmental reputation and reinforcing environmental concern will no longer be a utopia. So, through efficient resource planning of green activities, new and interesting opportunities can be created by the bank which can boost their prominence and help to win trust of current and prospective customers. The study will motivate the banking sector to be engaged in green corporate social responsibility as “social banking” is an important aspect of “green banking” and use green internal process to create awareness amongst the divergent stakeholders. The study has great relevance for environmentalist, policy makers and all stakeholders in developing effective and efficient green banking strategies.

9 The limitations and directions for future studies

The proposed relationship in this qualitative study can be further validated quantitatively, and the impact of demographics on it can also be investigated. The study has been conducted in Delhi NCR region in India, and an exhaustive study in different countries at different stages of development can provide valuable insight. The proposed framework can also be studied from the point of view of other stakeholders apart from bank employees.

The study has very placidly explained how use of green initiatives by banks can enhance Green brand image and solidify trust with stakeholders. The research results provide relevant and divergent insights into government, strategist and academician to chalk out effective green banking strategies for “green economy”. The State of Green Bank Report ( 2020 ) also declares that for a sustainable economic recovery during the global COVID 19 crises as well as for reducing emission before 2050 “climate-resilient green banks” are the need of the hour.

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The authors acknowledge the supports provided by Indian Council of Social Science Research (ICSSR) India for funding this research.

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Sharma, M., Choubey, A. Green banking initiatives: a qualitative study on Indian banking sector. Environ Dev Sustain 24 , 293–319 (2022). https://doi.org/10.1007/s10668-021-01426-9

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Received : 03 August 2020

Accepted : 10 April 2021

Published : 02 May 2021

Issue Date : January 2022

DOI : https://doi.org/10.1007/s10668-021-01426-9

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