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Banking and Finance Dissertation Topics – Selected for Business Students

Published by Owen Ingram at January 2nd, 2023 , Revised On August 16, 2023

Looking for an interesting banking and finance research idea for your dissertation? Your search for the best finance and banking dissertation topics ends right here because, a t ResearchProspect, we help students choose the most authentic and relevant topic for their dissertation projects.

Bank taxes, financial management, financial trading, credit management, market analysis for private investors, economic research methods, the economics of money and banking, international trade and multinational business, the wellbeing of people and society, principles and practices of banking, management and cost accounting, governance and ethics in banking, investment banking, introductory econometrics, and capital investment management are among the many topics covered in banking and finance.

Without further ado, here is our selection of the besting banking and finance thesis topics and ideas.

List of Banking and Finance Dissertation Topics

A Comprehensive Analysis of the Economic Crisis as It Relates to Banking and Finance
A Critical Review of Standard Deviation in Business
The Political and Economic Risks Involving National Bank Transactions
A Study of Corporate Developments in European Countries Regarding Banking and Finance
Security Measures Implemented in Financial Institutions Around the World
Banking and Finance Approaches from Around the World
An in-depth study of the World Trade Organization’s role in banking and finance
A Study of the Relationship Between Corporate Strategy and Capital Structures
Contrasting global, multinational banks with regional businesses
Preventing Repetitive Economic Collapse in National and Global Finances
The Motivations for Becoming International Expats All Over the World
The Difference Between Islamic Banking and Other Religious Denominations in Banking and Financial Habits
How Can Small-Scale Industries Survive the Global Banking Demands?
A Study of the Economic Crisis’s Impact on Banking and Finance
The Impact of the International Stock Exchange on Domestic Bank Transactions
A 2025 Projected Report on World Trade and Banking Statistics
How Can We Address the Issue of the Government’s Financial Deficit in Banking?
A Comparison of Contemporary and Classic Business Models and Companies’ Banking and Financial Habits
Which of the following should be the principal area of money investment that has arrived at the bank in the form of deposits?
How to strike a balance between investing money in various plans to generate a profit and managing depositor trust
What are banks’ responsibilities to their depositors, and how may such liabilities be managed without jeopardising depositor trust?
How the new banking financing laws enacted by governments throughout the world are better protecting depositors’ rights?
What is the terminology related to banking finance, which oversees the investment of deposited funds as well as the banks’ responsibilities to depositors?
Explain the most recent developments in research related to the topic of banking finance
How research in the banking finance industry assists governments and banking authorities in properly managing their finances?
What is the most recent credit rating software that assists in determining the rewards and dangers of investing bank funds in the stock market?
How banking finance assists the world’s top banks in managing consumer expectations and profit?
The negative impact of a manager’s poor management of a bank’s banking financing
Is it feasible to conduct a banking firm without the assistance of banking finance management?
What are the most significant aspects of banking financing that allow businesses to develop without constraints?

The importance of banking finance cannot be overstated. These are only a few of the most extensive subjects on which you may write a banking and finance dissertation. Remember that if you want to succeed in your studies, you must be able to offer reliable numbers and facts on the history and current state of banking and finance throughout the world. Otherwise, you will very certainly be unable to justify your study effectively. We hope you can take some inspiration and ideas from the above banking and finance dissertation topics .

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80 Banking and Finance Research Topics

FacebookXEmailWhatsAppRedditPinterestLinkedInAre you a student embarking on the exciting journey of research and looking for captivating topics in banking and finance to fuel your thesis or dissertation? Look no further! Selecting the proper research topics is pivotal to the success of your academic endeavor. Whether you’re pursuing an undergraduate, master’s, or doctoral degree, the world of […]

Are you a student embarking on the exciting journey of research and looking for captivating topics in banking and finance to fuel your thesis or dissertation? Look no further! Selecting the proper research topics is pivotal to the success of your academic endeavor. Whether you’re pursuing an undergraduate, master’s, or doctoral degree, the world of banking and finance offers a plethora of intriguing avenues to explore.

Banking and finance are often used interchangeably; the keywords “financial research” and “banking study” encompass the intricate mechanisms that drive the global economy, managing the flow of funds, investments, and financial instruments.

This comprehensive guide will delve into diverse research topics that will captivate your interest and contribute to growing knowledge in this dynamic field.

A List Of Potential Research Topics In Banking and Finance:

Analyzing the effect of Bank of England policies on interest rates and inflation.
Exploring the determinants and consequences of bank liquidity creation.
A critical analysis of Dodd-Frank Wall Street Reform and Consumer Protection Act.
Reviewing the role of systemically important financial institutions (sites) in the 2008 crisis.
Evaluating the effects of mergers and acquisitions on bank performance.
Evaluating the role of credit unions in promoting financial inclusion.
Analyzing the effects of financial market volatility on investor behavior.
Investigating the relationship between financial inclusion and economic growth.
Analyzing the risks and benefits of open banking implementation in the UK.
Exploring the role of insurance in reshaping the insurance industry after COVID-19.
Investigating the implications of negative interest rates on banking profitability.
Evaluating the impact of Brexit on London as a global financial hub.
Analyzing the challenges and opportunities of sustainable finance in the post-covid era.
Analyzing the implications of the London Interbank offered rate (LIBOR) transition in the UK.
Analyzing the resilience of microfinance institutions during and after the pandemic.
Exploring the relationship between UK taxation policies and investment decisions.
Examines Basel iii regulations’ impact on bank capital adequacy.
Examining the effects of political risk on international banking operations.
Reshaping investment strategies: a study of behavioral changes post-pandemic.
The role of credit rating agencies in financial markets: an empirical study.
Reviewing the effects of quantitative easing programs on financial markets .
Investigating the influence of behavioral biases on investment decisions.
Investigating the factors affecting customer loyalty in retail banking.
Impact of the pandemic on credit risk assessment and bank loan defaults.
Examining the determinants of corporate credit ratings and their implications.
Investigating the link between corporate social responsibility and financial performance.
The changing landscape of risk management in banking: a literature review.
The role of central banks in addressing systemic banking crises: a historical perspective.
Evaluating the effectiveness of risk management strategies in the banking sector.
Analyzing the impact of microfinance initiatives on rural economic empowerment.
Assessing the impact of economic uncertainty on investment behavior.
The role of e-commerce and online platforms in shaping post-pandemic retail banking.
Investigating the relationship between macroeconomic indicators and stock market performance.
A review of behavioral finance theories and their practical implications.
Sustainability reporting practices in banking: a global review.
Assessing the adoption and implementation of sustainable finance practices in banking.
Impact of Brexit on UK-EU financial services trade: challenges and opportunities.
Reviewing the effects of high-frequency trading on market liquidity and stability.
Impact of digital currencies on cross-border payments and remittances.
Impact of UK carbon pricing on financial institutions’ risk management strategies.
Analyzing remote work’s impact on financial institutions’ cybersecurity risks.
Analyzing the shift in consumer payment preferences post-COVID-19.
Analyzing the challenges and opportunities of open banking initiatives.
Impact of supply chain disruptions on trade finance and international banking.
Impact of regulatory changes on mortgage market dynamics in the UK.
Evaluating the effects of Brexit on cross-border capital flows and investment.
The shift in consumer behavior: a study of post-covid-19 payment preferences.
Examining the long-term effects of remote work on financial service organizations.
The role of UK Islamic finance in promoting ethical and Sharia-compliant banking.
Exploring the relationship between corporate governance and bank risk management.
The impact of quantitative easing on income inequality: a review of empirical studies.
The role of digital identity verification in ensuring financial security post-covid-19.
Analyzing the dynamics of cryptocurrency markets and their interaction with traditional finance.
Exploring the trends and patterns in the UK peer-to-peer lending market.
Assessing the adoption of contactless payments in the UK retail sector.
The role of data analytics in enhancing financial decision-making post-pandemic.
Reviewing the dynamics of global capital flows and their implications for developing economies.
E explores sovereign wealth funds’ role in global capital markets.
A critical review of financial innovations and their impact on banking services.
Analyzing the shift in UK real estate investment patterns post-Brexit.
Evaluating the effectiveness of central bank digital currencies in crisis management.
The role of venture capital in fostering technological innovation and startups.
Assessing the influence of dividend policies on corporate financial performance.
Impact of pandemic-driven ESG awareness on investment decision-making.
Evaluating the effectiveness of government stimulus packages in supporting financial institutions.
Evaluating the role of financial technology in enhancing financial inclusion in the UK.
A critical review of corporate governance practices in global banking.
Role of UK financial services compensation scheme (fscs) in ensuring consumer confidence.
The role of credit rating agencies in the subprime mortgage crisis: lessons learned.
The dynamics of financial crises: a comparative study of historical cases.
A review of Basel Accords: achievements, criticisms, and implications for banking.
An examination of financial market efficiency theories and empirical evidence.
Analyzing the role of central banks in financial stability.
Exploring central banks’ role in mitigating the pandemic’s economic impact.
Evaluating the impact of supply chain disruptions on trade financing instruments.
The role of corporate governance in preventing financial scandals: a comparative study.
Examining the impact of regulatory changes on bank risk-taking behavior.
Exploring the impact of fintech innovations on traditional banking services.
Investigating the effects of COVID-19 on the UK’s commercial real estate financing.
Analyzing the relationship between financial literacy and retirement planning.

In pursuing academic excellence, these meticulously curated banking and finance research topics across various degree levels provide a launching pad for your thesis or dissertation journey. Whether you’re unraveling the complexities of behavioral finance for your undergraduate thesis, dissecting the impact of digital currencies on traditional banking systems at the master’s level, or delving into the intricacies of international financial regulations for your doctoral dissertation, remember that your chosen topic should align with your passion and research interests. As you embark on this scholarly adventure, these topics offer a stepping stone toward contributing to the ever-evolving landscape of banking and finance research.

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Dissertation - A Study On Financial Performance Of Select Commercial Banks In India

The purpose of the study is to examine the financial performance of SBI and HDFC Bank, public sector and private sector respectively. The research is descriptive and analytical in nature. The data used for the study was secondary in nature.

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The Period between 2005 and 2010 can be Said to be a Time Period in the whole World Economy which is Full of many Ups and Downs. the World Economy was Doing very well and Suddenly there is the Financial Crisis throughout the World Post Layman Brothers' Collapse. now the World Economy is Going through the Recovery Process. Despite all These, Indian Economy is not Aversely Affected to that an Extent. one of the Main Reasons for This was the Strong Financial System of India Led by Sound Banking System. in This Context, the Article Attempts to Roa, Roe, Cd Ratio, Spread Study the Performance of Various Categories of Banks, Namely, Public Sector Banks, Old Generation Private Sector Banks, New Generation Private Sector Banks, Sbi and its Associate Banks, foreign Banks Operating in India, for a Period of Five Years from 2005-06 to 2009-10.

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Utilization of artificial intelligence in the banking sector: a systematic literature review

Original Article
Published: 11 August 2022
Volume 28 , pages 835–852, ( 2023 )

Cite this article

Omar H. Fares ORCID: orcid.org/0000-0003-0950-0661 1 ,
Irfan Butt 1 &
Seung Hwan Mark Lee 1

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This study provides a holistic and systematic review of the literature on the utilization of artificial intelligence (AI) in the banking sector since 2005. In this study, the authors examined 44 articles through a systematic literature review approach and conducted a thematic and content analysis on them. This review identifies research themes demonstrating the utilization of AI in banking, develops and classifies sub-themes of past research, and uses thematic findings coupled with prior research to propose an AI banking service framework that bridges the gap between academic research and industry knowledge. The findings demonstrate how the literature on AI and banking extends to three key areas of research: Strategy, Process, and Customer. These findings may benefit marketers and decision-makers in the banking sector to formulate strategic decisions regarding the utilization and optimization of value from AI technologies in the banking sector. This study also provides opportunities for future research.

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Introduction

Digital innovations in the modern banking landscape are no longer discretionary for financial institutions; instead, they are becoming necessary for financial institutions to cope with an increasingly competitive market and changing customer expectations (De Oliveira Santini, 2018 ; Eren, 2021 ; Hua et al., 2019 ; Rajaobelina and Ricard, 2021 ; Valsamidis et al., 2020 ; Yang, 2009 ). In the era of modern banking, many new digital technologies have been driven by artificial intelligence (AI) as the key engine (Dobrescu and Dobrescu, 2018 ), leading to innovative disruptions of banking channels (e.g., automated teller machines, online banking, mobile banking), services (e.g., imaging of checks, voice recognition, chatbots), and solutions (e.g., AI investment advisors and AI credit selectors).

The application of AI in banking is across the board, with uses in the front office (voice assistants and biometrics), middle office (anti-fraud risk monitoring and complex legal and compliance workflows), and back office (credit underwriting with smart contracts infrastructure). Banks are expected to save $447 billion by 2023, by employing AI applications. Almost 80% of the banks in the USA are cognizant of the potential benefits offered by AI (Digalaki, 2022 ). Indeed, the emergence of AI has generated a wealth of opportunities and challenges (Malali and Gopalakrishnan, 2020 ). In the banking context, the use of AI has led to more seamless sales and has guided the development of effective customer relationship management systems (Tarafdar et al., 2019 ). While the focus in the past was on the automation of credit scoring, analyses, and the grants process (Mehrotra, 2019 ), capabilities evolved to support internal systems and processes as well (Caron, 2019 ).

The term AI was first used in 1956 by John McCarthy (McCarthy et al., 1956 ); it refers to systems that act and think like humans in a rational way (Kok et al., 2009 ). In the aftermath of the dot com bubble in 2000, the field of AI shifted toward Web 2.0. era in 2005, and the growth of data and availability of information encouraged more research in AI and its potential (Larson, 2021 ). More recently, technological advancements have opened the doors for AI to facilitate enterprise cognitive computing, which involves embedding algorithms into applications to support organizational processes (Tarafdar et al., 2019 ). This includes improving the speed of information analysis, obtaining more accurate and reliable data outputs, and allowing employees to perform high-level tasks. In recent years, AI-based technologies have been shown to be effective and practical. However, many corporate executives still lack knowledge regarding the strategic utilization of AI in their organizations. For instance, Ransbotham et al. ( 2017 ) found that 85% of business executives viewed AI as a key tool for providing businesses with a sustainable competitive advantage; however, only 39% had a strategic plan for the use of AI, due to the lack of knowledge regarding implementation of AI for their organizations.

Here, we systematically analyze the past and current state of AI and banking literature to understand how it has been utilized within the banking sector historically, propose a service framework, and provide clear future research opportunities. In the past, a limited number of systematic literature reviews have studied AI within the management discipline (e.g., Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ). However, the current literature lacks either research scope and depth, and/or industry focus. In response, we seek to differentiate our study from prior reviews by providing a specific focus on the banking sector and a more comprehensive analysis involving multiple modes of analysis.

In light of this, we aim to address the following research questions:

What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

How does AI impact the customer's journey process in the banking sector, from customer acquisition to service delivery?

What are the current research deficits and future directions of research in this field?

Methodology

Selection of articles.

Adhering to the best practices for conducting a Systematic Literature Review (SLR) (see Khan et al., 2003 ; Tranfield et al, 2003 ; Xiao and Watson, 2019 ), we began by selecting the appropriate database and identifying keywords, based on an in-depth review of the literature. Research papers were extracted from Web of Science (WoS) and Scopus. These databases were selected to complement one another and provide access to scholarly articles (Mongeon and Paul-Hus, 2016 ); this was also the first step in ensuring the inclusion of high-quality articles (Harzing and Alakangas, 2016 ). The following query was used to search the title, abstract, and keywords: “Artificial intelligence OR machine learning OR deep learning OR neural networks OR Intelligent systems AND Bank AND consumer OR customer OR user.” The keywords were selected, based on prior literature review, with the goal of covering various business functions, especially focusing on the banking sector (Loureiro et al., 2020 ; Verma et al., 2021 ; Borges et al., 2020 ; Bavaresco et al., 2020 ). The initial search criteria yielded 11,684 papers. These papers were then filtered by “English,” “article only” publications, and using the subject area filter of “Management, Business Finance, accounting and Business,” which resulted in 626 papers.

In this study, we used the preferred reporting method for systematic reviews and meta-analyses (PRISMA) to ensure that we follow the systematic approach and track the flow of data across different stages of the SLR (Moher et al., 2009 ). After extracting the articles, each of the 626 papers was given a distinctive ID number to help differentiate the papers; the ID number was maintained throughout the analysis process. The data were then organized using the following columns: “ID number,” “database source,” “Author,” “title,” “Abstract,” “keywords,” “Year,” Australian Business Deans Council (ABDC) Journals, “and keyword validation columns.”

The exclusion of papers was done systematically in the following manner: a) All duplicate papers in the database were eliminated (105 duplicates); b) as a second quality check, papers not published in ABDC journals (163 papers) were omitted to ensure a quality standard for inclusion in the review,Query a practice consistent with other recent SLRs (Goyal and Kumar, 2021 ; Nusair et al., 2019 ; Pahlevan-Sharif et al., 2019 ); c) in order to ensure the relevance of articles included, and following our research objectives, we excluded non-consumer-related papers, searching for consumers (consumer, customer, user) in the title, abstract, and keywords; this resulted in the removal of 314 papers; d) for the remaining 48 papers, a relevance check was manually conducted to determine whether the papers were indeed related to AI and banking. Papers that specifically focused on the technical computational process of AI were removed (4 papers). This process resulted in the selection of 44 articles for subsequent analyses.

Thematic analysis

A thematic analysis classifies the topics and subtopics being researched. It is a method for identifying, analyzing, and reporting patterns within data (Boyatzis, 1998 ). We followed Chatha and Butt ( 2015 ) to classify the articles into themes and sub-themes using manual coding. Second, we employed the Leximancer software to supplement the manual classification process. The use of these two approaches provides additional validity and quality to the research findings.

Leximancer is a text-mining software that provides conceptual and relational information by identifying concept occurrences and co-occurrences (Leximancer, 2019 ). After uploading all the 44 papers onto Leximancer, we added “English” to the stoplist, which removed words such as “or/and/like” that are not relevant to developing themes. We manually removed irrelevant filler words, such as “pp.,” “Figure,” and “re.” Finally, our results consisted of two maps: a) a conceptual map wherein central themes and concepts are identified, and b) a relational cloud map where a network of connections and relationships are drawn among concepts.

Thematic map

RQ 1: What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

We began with a deductive approach to categorize articles into predetermined themes for the theme identification process. We then employed an inductive approach to identify the sub-themes and provide context for the primary themes (See Fig. 1 ). The procedure for determining the primary themes included, a) reviewing previous related systematic literature reviews (Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ), b) identifying keywords and developing codes (themes) from selected papers; and c) reviewing titles, abstracts, and full papers, if needed, to identify appropriate allocation within these themes. Three primary themes were curated from the process: Strategy, Processes, and Customers (see Fig. 2 ).

Themes by timeline

In the Strategy theme (21 papers), early research shows the potential uses and adoption of AI from an organizational perspective (e.g., Akkoç, 2012 ; Olson et al., 2012 ; Smeureanu et al., 2013 ). Data mining (an essential part of AI) has been used to predict bankruptcy (Olson et al., 2012 ) and to optimize risk models (Akkoç, 2012 ). The increasing use of AI-driven tools to drive organizational effectiveness creates greater business efficiency opportunities for financial institutions, as compared to traditional modes of strategizing and risk model development. The sub-theme Organizational use of AI (14 papers) covers a range of current activities wherein banks use AI to drive organizational value. These organizational uses include the use of AI to drive business strategies and internal business activities. Medhi and Mondal ( 2016 ) highlighted the use of an AI-driven model to predict outsourcing success. Our findings indicate the effectiveness of AI tools in driving efficient organizational strategies; however, there remain several challenges in implementing AI technologies, including the human resources aspect and the organizational culture to allow for such efficiencies (Fountain et al., 2019 ). More recently, there has been a noticeable focus on discussing some of the challenges associated with AI implementation in banking institutions (e.g., Jakšič and Marinč, 2019 ; Mohapatra, 2020 ). The sub-theme Challenges with AI (three papers) covers a range of challenges that organizations face, including the integration of AI in their organizations. Mohapatra ( 2020 ) characterizes some of the key challenges related to human–machine interactions to allow for the sustainable implementation of AI in banking. While much of the current research has focused on technology, our findings indicate that one of the main areas of opportunity in the future is related to adoption and integration. The sub-theme AI and adoption in financial institutions (six papers) covered a range of topics regarding motivation, and barriers to the adoption of AI technology from an organizational standpoint. Fountain et al. ( 2019 ) conceptually highlighted some barriers to organizational adoption, including workers’ fear, company culture, and budget constraints. Overall, in the Strategy theme, organizational uses of AI seemed to be the most prominent, which highlights the consistent focus on technology development compared with technology implementation. However, the literature remains limited in terms of discussions related to the organizational challenges associated with AI implementation.

In the Processes theme (34 papers), after the dot com bubble and with the emergence of Web 2.0, research on AI in the banking sector started to emerge. This could have been triggered by the suggested use of AI to predict stock market movements and stock selection (Kim and Lee, 2004 ; Tseng, 2003 ). At this stage, the literature on AI in the banking sector was related to its use in credit and loan analysis (Baesens et al., 2005 ; Ince and Aktan, 2009 ; Kao et al., 2012 ; Khandani et al., 2010 ). In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021 ). Baesens et al. ( 2005 ) utilized a neural network approach to better predict loan defaults and early repayments. Ince and Aktan ( 2009 ) used a data mining technique to analyze credit scores and found that the AI-driven data mining approach was more effective than traditional methods. Similarly, Khandani et al. ( 2010 ) found machine-learning-driven models to be effective in analyzing consumer credit risk. The sub-theme, AI and credit (15 papers), covers the use of AI technology, such as machine learning and data mining, to improve credit scoring, analysis, and granting processes. For instance, Alborzi and Khanbabaei ( 2016 ) examined the use of data mining neural network techniques to develop a customer credit scoring model. Post-2013, there has been a noticeable increase in investigating how AI improves processes that go beyond credit analysis. The sub-theme AI and services (20 papers) covers the uses of AI for process improvement and enhancement. These process-related uses of technology include institutional uses of technology to improve internal service processes. For example, Soltani et al. ( 2019 ) examined the use of machine learning to optimize appointment scheduling time, and reduce service time. Overall, regarding the process theme, our findings highlight the usefulness of AI in improving banking processes; however, there remains a gap in practical research regarding the applied integration of technology in the banking system. In addition, while there is an abundance of research on credit risk, the exploration of other financial products remains limited.

In the Customer theme (26 papers), we uncovered the increasing use of AI as a methodological tool to better understand customer adoption of digital banking services. The sub-theme AI and Customer adoption (11 papers) covers the use of AI as a methodological tool to investigate customers’ adoption of digital banking technologies, including both barriers and motivational factors. For example, Arif et al. ( 2020 ) used a neural network approach to investigate barriers to internet-banking adoption by customers. Belanche et al. ( 2019 ) investigate factors related to AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examine the drivers of the usage of AI-enabled mobile banking services. In addition, bank marketers have found an opportunity to use AI to better segment, target, and position their banking products and services. The sub-theme, AI and marketing (nine papers), covers the use of AI for different marketing activities, including customer segmentation, development of marketing models, and delivery of more effective marketing campaigns. For example, Smeureanu et al. ( 2013 ) proposed a machine learning technique to segment banking customers. Schwartz et al. ( 2017 ) utilized an AI-based method to examine the resource allocation in targeted advertisements. In recent years, there has been a noticeable trend in investigating how AI shapes customer experience (Soltani et al., 2019 ; Trivedi, 2019 ). The sub-theme of AI and customer experience (Papers 11) covers the use of AI to enhance banking experience and services for customers. For example, Trivedi ( 2019 ) investigated the use of chatbots in banking and their impact on customer experience.

Table 1 highlights the number of papers included in the themes and sub-themes. Overall, the papers related to Processes (77%) were the most frequently occurring, followed by Customer (59%) and Strategy-based (48%) papers. From 2013 onward, there was an increase in the inter-relation between all three areas of Strategy, Processes, and Customers. Since 2016, there has been a surge in research linking the themes of Processes and Customers. More recently, since 2017, papers combining Customers with Strategy have become more frequent.

Leximancer analysis

A Leximancer analysis was conducted on all the papers included in the study. This resulted in two major classifications and 56 distinct concepts. Here, a “concept” refers to a combination of closely related words. When referring to “concept co-occurrence,” we refer to the total number of times two concepts appear together. In comparison, the word association percentage refers to the conditional probability that two concepts will appear side-by-side.

Conceptual and relational analyses

Conceptual analysis refers to the analysis of data based on word frequency and word occurrence, whereas relational analysis refers to the analysis that draws connections between concepts and captures the co-occurrences between words (Leximancer, 2019 ). As Fig. 3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme. The concept “customer” appeared 2,231 times across all papers. For the concept “customer,” some of the key concept associations include satisfaction (324 co-occurrences and 64% word association), service (185 co-occurrences and 43% word association), and marketing (86 co-occurrences and 42% word association). This may imply the importance of utilizing AI in improving customer service and satisfaction, and in marketing to retain and grow the customer base. For instance, Trivedi ( 2019 ) examined the factors affecting chatbot satisfaction and found that information, system, and service quality, all have a significant positive association with it. Ekinci et al. ( 2014 ) proposed a customer lifetime value model, supported by a deep learning approach, to highlight key indicators in the banking sector. Xu et al. ( 2020 ) examined the effects of AI versus human customer service, and found that customers are more likely to use AI for low-complexity tasks, whereas a human agent is preferred for high-complexity tasks. It is worth noting that most of the research related to the customer theme has utilized a quantitative approach, with limited qualitative papers (i.e., four papers) in recent years.

Concept map of content of all papers included in the study

Not surprisingly, the second most prominent concept is “banking,” which is expected as it is the sector that we are examining. The concept “banking” appeared 1,033 times across all the papers. In the “banking” concept, some of the key concept associations include mobile (248 co-occurrences and 88% word association), internet (152 co-occurrences and 82% word association), adoption (220 co-occurrences and 50% word association), and acceptance (71 co-occurrences and 42% word association). This implies the importance of utilizing AI in mobile- and internet-banking research, along with inquiries related to the adoption and acceptance of AI for such uses. Belanche et al. ( 2019 ) proposed a research framework to provide a deeper understanding of the factors driving AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities and found that the need for services, attitude toward AI, relative advantage, and trust had a significant positive association with the usage of AI-enabled mobile banking services.

Figure 4 highlights the concept associations and draws connections between concepts. The identification and classification of themes and sub-themes using the deductive method in thematic analysis, and the automated approach using Leximancer, provide a reliable and detailed overview of the prior literature.

Cloud map of content of all papers included in the study

Customer credit solution application-service blueprint

RQ 2: How does AI impact the banking customer’s journey?

A service blueprint is a method that conceptualizes the customer journey while providing a framework for the front/back-end and support processes (Shostack, 1982 ). For a service blueprint to be effective, the core focus should be on the customer, and steps should be developed based on data and expertise (Bitner et al., 2008 ). As previously discussed, one of the key research areas, AI and banking, relates to credit applications and granting decisions; these are processes that directly impact customer accessibility and acquisition. Here, we develop and propose a Customer Credit Solution Application-Service Blueprint (CCSA) based on our earlier analyses.

Not only was the proposed design developed but the future research direction was also extracted from the articles included in this study. We also validated the framework through direct consultation with banking industry professionals. The CCSA model allows marketers, researchers, and banking professionals to gain a deeper understanding of the customer journey, understand the role of AI, provide an overview of future research directions, and highlight the potential for future growth in this field. As seen in Fig. 5 , we divided the service blueprint into four distinct segments: customer journey, front-stage, back-stage, and support processes. The customer journey is the first step in building a customer-centric blueprint, wherein we highlight the steps taken by customers to apply for a credit solution. The front-stage refers to how the customer interacts with a banking touchpoint (e.g., chatbots). Back-stage actions provide support to customer-facing front-stage actions. Support processes aid in internal organizational interactions and back-stage actions. This section lays out the steps for applying for credit solutions online and showcases the integration and use of AI in the process, with examples from the literature.

Customer credit solution application journey

Acquire customer

We begin from the initial step of customer acquisition, and proceed to credit decision, and post-decision (Broby, 2021 ). In the acquisition step, customers are targeted with the goal of landing them on the website and converting them to active customers. The front-stage includes targeted ads , where customers are exposed to ads that are tailored for them. For instance, Schwartz et al. ( 2017 ) utilized a multi-armed bandit approach for a large retail bank to improve customer acquisition, and proposed a method that allows bank marketers to maintain the balance between learning from advertisement data and optimizing advertisement investment. At this stage, the support processes focus on integrating AI as a methodological tool to better understand customers' banking adoption behaviors, in combination with utilizing machine learning to evaluate and update segmentation activities. The building block at this stage, is understanding the factors of online adoption. Sharma et al. ( 2017 ) used the neural network approach to investigate the factors influencing mobile banking adoption. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities. Markinos and Daskalaki ( 2017 ) used machine learning to classify bank customers based on their behavior toward advertisements.

Visit bank’s website & apply for a credit solution

At this stage, banking institutions aim to convert website traffic to credit solution applicants. The integration of robo-advisors will help customers select a credit solution that they can best qualify for, and which meets their banking needs. The availability of a robo-advisor can enhance the service offering, as it can help customers with the appropriate solution after gathering basic personal financial data and validating it instantly with credit reporting agencies. Trivedi ( 2019 ) found that information, system, and service quality are key to ensuring a seamless customer experience with the chatbot, with personalization moderating the constructs. Robo-advisors have task-oriented features (e.g., checking bank accounts) coupled with problem-solving features (e.g., processing credit applications). Following this, the data collected will be consistently examined through the use of machine learning to improve the offering and enhance customer experience. Jagtiani and Lemieux ( 2019 ) used machine learning to optimize data collected through different channels, which helps arrive at appropriate and inclusive credit recommendations. It is important to note that while the proposed process provides immense value to customers and banking institutions, many customers are hesitant to share their information; thus, trust in the banking institution is key to enhancing customer experience.

Receive a decision

After the data have been collected through the online channel, data mining and machine learning will aid in the analysis and provide optimal credit decisions. At this stage, the customer receives a credit decision through the robo-advisor. The traditional approaches for credit decisions usually take up to two weeks, as the application goes to the advisory network, then to the underwriting stage, and finally back to the customer. However, with the integration of AI, the customer can save time and be better informed by receiving an instant credit decision, allowing an increased sense of empowerment and control. The process of arriving at such decisions should provide a balance between managing organizational risk, maximizing profit, and increasing financial inclusion. For instance, Khandani et al. ( 2010 ) utilized machine learning techniques to build a model predicting customers' credit risk. Koutanaei et al. ( 2015 ) proposed a data mining model to provide more confidence in credit scoring systems. From an organizational risk standpoint, Mall ( 2018 ) used a neural network approach to examine the behavior of defaulting customers, so as to minimize credit risk, and increase profitability for credit-providing institutions.

Customer contact call center

At this stage, we outline the relationship between humans and AI. As Xu et al. ( 2020 ) found that customers prefer humans for high-complexity tasks, the integration of human employees for cases that require manual review is vital, as AI can make errors or misevaluate one of the C's of credit (Baiden, 2011 ). While AI provides a wealth of benefits for customers and organizations, we refer to Jakšič and Marinč's ( 2019 ) discussion that relationship banking still plays a key role in providing a competitive advantage for financial institutions. The integration of AI at this stage can be achieved by optimizing banking channels. For instance, banking institutions can optimize appointment scheduling time and reduce service time through the use of machine learning, as proposed by Soltani et al. ( 2019 ).

General discussion

Researchers have recognized the viable use of AI to provide enhanced customer service. As discussed in the CCSA service advice, facilities, such as robo-advisors, can aid in product selection, application for banking solutions, and time-saving in low-complexity tasks. As AI has been shown to be an effective tool for automating banking processes, improving customer satisfaction, and increasing profitability, the field has further evolved to examine issues pertaining to strategic insights. Recent research has been focused on investigating the use of AI to drive business strategies. For instance, researchers have examined the use of AI to simplify internal audit reports and evaluate strategic initiatives (Jindal, 2020 ; Muñoz-Izquierdo et al., 2019 ). The latest research also highlights the challenges associated with AI, whether from the perspective of implementation, culture, or organizational resistance (Fountain et al., 2019 ). Moreover, one of the key challenges uncovered in the CCSA is privacy and security concerns of customers in sharing their information. As AI technologies continue to grow in the banking sector, the privacy-personalization paradox has become a key research area that needs to be examined.

In addition, the COVID-19 pandemic has brought on a plethora of challenges in the implementation of AI in the banking sector. Although banks' interest in AI technologies remains high, the reduction in revenue has resulted in a decrease in short-term investment in AI technologies (Anderson et al., 2021 ). Wu and Olson ( 2020 ) highlight the need for banking institutions to continue investing in AI technologies to reduce future risks and enhance the integration between online and offline channels. From a customer perspective, COVID-19 has led to an uptick in the adoption of AI-driven services such as chatbots, E-KYC (Know your client), and robo-advisors (Agarwal et al., 2022 ).

Future research directions

RQ 3: What are the current research deficits and the future directions of research in this field?

Tables 2 , 3 , and 4 provide a complete list of recommendations for future research. These recommendations were developed by reviewing all the future research directions included in the 44 papers. We followed Watkins' ( 2017 ) rigorous and accelerated data reduction (RADaR) technique, which allows for an effective and systematic way to analyze and synthesize calls for future research (Watkins, 2017 ).

Regarding strategy, as AI continues to grow in the banking industry, financial institutions need to examine how internal stakeholders perceive the value of embracing AI, the role of leadership, and multiple other variables that impact the organizational adoption of AI. Therefore, we recommend that future research investigate the different factors (e.g., leadership role) that impact the organizational adoption of AI technologies. In addition, as more organizations use and accept AI, internal challenges emerge (Jöhnk et al., 2021 ). Thus, we recommend examining the different organizational challenges (e.g., organizational culture) associated with AI adoption.

Regarding processes, AI and credit is one of the areas that has been extensively explored since 2005 (Bhatore et al., 2020 ). We recommend expanding beyond the currently proposed models and challenging the underlying assumptions by exploring new aspects of risks presented with the introduction of AI technologies. In addition, we recommend the use of more practical case studies to validate new and existing models. Additionally, the growth of AI has evoked further exploration of how internal processes can be improved (Akerkar, 2019 ). For instance, we suggest investigating AI-driven models with other financial products/solutions (e.g., investments, deposit accounts, etc.).

Regarding customers, the key theories mentioned in the research papers included in the study are the Technology Acceptance Model (TAM) and diffusion of innovation theories (Anouze and Alamro, 2019 ; Azad, 2016 ; Belanche et al., 2019 ; Payne et al., 2018 ; Sharma et al., 2015 , 2017 ). However, as customers continue to become accustomed to AI, it may be imperative to develop theories that go beyond its acceptance and adoption. Thus, we recommend investigating different variables (e.g., social influence and user trends) and methods (e.g., cross-cultural studies) that impact customers' relationship with AI. The gradual shift toward its customer-centric utilization has prompted the exploration of new dimensions of AI that influence customer experience. Going forward, it is important to understand the impact of AI on customers and how it can be used to improve customer experience.

Limitations and implications

This study had several limitations. During our inclusion/exclusion criteria, it is plausible that some AI/banking papers may have been missed because of the specific keywords used to curate our dataset. In addition, articles may have been missed due to the time when the data were collected, such as Manrai and Gupta ( 2022 ), who examined investors' perceptions of robo-advisors. Second, regarding theme identification, there may be a potential bias toward selecting themes, which may lead to misclassification. In addition, we acknowledge that the papers were extracted only from the WoS and Scopus databases, which may limit our access to certain peer-reviewed outlets.

This research provides insights for practitioners and marketers in the North American banking sector. To assist in the implementation of AI-based decision-making, we encourage banking professionals to consider further refining their use of AI in the credit scoring, analysis, and granting processes to minimize risk, reduce costs, and improve customer experience. However, in doing so, we recommend using AI not only to improve internal processes but also as a tool (e.g., chatbots) to improve customer service for low-complexity tasks, thereby directing employees' efforts to other business-impacting activities. Moreover, we recommend using AI as a marketing segmentation tool to target customers for optimal solutions.

This study systematically reviewed the literature (44 papers) on AI and banking from 2005 to 2020. We believe that our findings may benefit industry professionals and decision-makers in formulating strategic decisions regarding the different uses of AI in the banking sector, and optimizing the value derived from AI technologies. We advance the field by providing a more comprehensive outlook specific to the area of AI and banking, reflecting the history and future opportunities for AI in shaping business strategies, improving logistics processes, and enhancing customer value.

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Fares, O.H., Butt, I. & Lee, S.H.M. Utilization of artificial intelligence in the banking sector: a systematic literature review. J Financ Serv Mark 28 , 835–852 (2023). https://doi.org/10.1057/s41264-022-00176-7

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Dissertation Topic in Finance

Updated on
Jan 11, 2024

Dissertation Topics in Finance- MBA, Banking, Accounting Projects-04 (1)

Also known as the study of investments, Finance is a combination of two interrelated subjects – how money is handled and the process of obtaining money. One of the reasons why postgraduate students struggle with their Finance dissertation topics is that they do not spend enough time planning it. It is important for students to be extremely careful while writing a finance dissertation as it contributes a lot to their respective degrees. This blog provides you with the best topics, a dissertation structure, and more.

This Blog Includes:

What is a finance dissertation, why finance dissertation topics are important, tips to find excellent dissertation topics on finance, writing tips for finance dissertation, how to plan your work on a finance dissertation, how to structure a finance dissertation, finance dissertation general topics , topics related to india, mba dissertation topics, banking dissertation topics , accounting dissertation topics, research project example, final consideration and conclusion.

Finance dissertations, as the name implies, are pieces of writing that study a certain finance topic chosen by the student. The subjects covered include anything from the stock market to banking and risk management to healthcare finance. This dissertation gives the student academic self-assurance and personal happiness in the subject of finance. Finance writing necessitates substantial research in order to produce a compelling report.

The majority of students have no idea why finance dissertation themes are so crucial. However, put yourself in the shoes of your lecturer. You’ve already read hundreds of theses. The majority of them covered the same ground — issues that you’re already tired of hearing about. Then there’s a topic with a distinct, intriguing theme. Something that piques your interest and entices you to read more. Wouldn’t you give those pupils some extra credit? You’d do it! This is why there are so many fantastic finance dissertation topics. You can get extra points for your efforts. The topic of your paper might mean the difference between a good and a terrific grade.

It’s difficult to come up with anything unique and interesting. There are, nevertheless, ways to come up with interesting ideas. Here are a few pointers on how to locate them:

Read a fantastic finance dissertation and find for areas where further study is needed.
Go to the library and read a couple theses to get some ideas.
Inquire with a writing agency about some ideas from one of their professional dissertation writers.
In writing forums and blogs, ask for assistance. If you ask gently, people will give you some excellent suggestions.
Look for ideas on the internet, but don’t use them exactly as they are. Make them distinctive by changing them.
Talk to other students who are working on their dissertations and find out what other ideas they had before settling on the present topic.
Narrow down your topic : Your financial topic should be narrowed down to a certain niche. It should concentrate on a single area, such as microfinance, microfinance, or online banking.
Verify your facts: Finance is a topic that requires a great deal of logical analysis of statistical data. As a result, double-check facts and statistics using credible sources before using them in your paper.
Write concisely: You should condense a financial paper into a tight, succinct work, unlike other papers with extended narrative narratives. At this length, the adage of ‘short is sweet’ theoretically applies.
Arrange your data neatly: A report that is crammed with numbers and graphs may turn off a reader at first glance. Know how and when to utilise your data for a great financial thesis.
Write simply: Avoid using jargon that might be confusing to a non-technical reader. When technical terminology are required, utilise accessible examples to convey them. In a finance dissertation, simplicity is king. So make good use of it.

Dissertation submission is very important to obtain a PG Degree. You are supposed to submit the work by the end of your study course, so by the last year of your degree, you may have got enough ideas and problems dealing with finance. While starting with a finance dissertation topic you should always remember that the purpose of a Finance Dissertation is to demonstrate your research ability, how you analyze specific data and come up with a conclusion. Mentioned below is a step to step guide for you to start working with:

Step 1 : Choose a relevant and interesting topic for your research

Step 2 : Discuss and receive feedback from your supervisor

Step 3 : Finalise the research methods to prove the significance of the selected topic

Step 4 : Gather the required data from relevant sources

Step 5 : Conduct the research and analyse the acquired results

Step 6 : Work on the outline of your dissertation

Step 7 : Make a draft and proofread it. Discuss with your advisors if any changes are to be made

Step 8 : Make the required corrections.

Step 9 : Draft the final dissertation

Also Read: Check out the Top Course in Finance

There are so many different ways you can structure your dissertation. But the most common and universally accepted way is as follows:

Introduction
Literature review
Methodology
Analysis of the data and Significance/Implications of the acquired results

Finance Dissertation Topics

Finance is an extensive field, you can explore a lot of areas related to finance to choose a dissertation topic. Here we’ve mentioned the best finance dissertation topics to make it easier for you:

Mentioned below are some of the topics related to the recent issues in the world:

The negative impact of microfinance in developing countries.
The effects of population growth on economic growth in China
Cryptocurrency: Are we ready to digitalise the monetary world?
Analyzing the financial statements of VISA and MasterCard
Why do banks oppose digital currency?
Risks and benefits associated with digital money transferring technology

Best MBA Dissertation Topics

Be careful while choosing an MBA Dissertation Topic as it involves more intense study. Make sure the topic you’ve chosen remains within your field of study. We’ve listed some of the best topics you can choose for an MBA Dissertation:

Management skills an entrepreneur need
The place of communication for effective management in the workplace
How technology took over management
The impact of good leadership in an organization
How does a strong social media presence affect a company’s marketing strategies?
Human resource management in non-profit organizations
The importance of employee motivation programs on productivity
Management’s socio-cultural background and how it influences leadership relationships
How do employment benefits impact employee and company’s productivity?
Business team performance in multinational corporations

Best Finance Universities in the USA

Study on Future Options in Markets in India
Gold as an Investable Commodity in India
Study on Impact Of Corruption On FDI Inflows In India
The Impact Of The Money Supply On Economic Growth In India
Capital Structure Of The Business Enterprises In Delhi NCR
GST And Its Effect on MNC Manufacturing Companies
Analysis of the Insurance Industry in India
Analysis of HDFC Bank Finance
Comparative analysis of HDFC Bank with ICICI bank
Comparison of Market Share in Public Sector Banks VS Private Sector Banks
The impact of online banking on the world.
Risk factors and security issues that are inherent in online banking.
Fraud and identity theft is accomplished via internet banking.
Advantages and disadvantages of internet banking for consumers.
Risk management in investment banking
The rise of growing banking sectors in developing nations.
Issues surrounding banking in China’s growing economy.
The impact of the Federal Reserve on the United States and global economy
Banking and asset-liability in management.
The strategies to use online banking technology to attract customers.

All you need to know about a Banking Course

Case study of the impact of industry and public knowledge on the market share index’s fluctuation
Significance of auditing for large corporations
Examining India’s country’s tax scheme
What to consider when investing in financial markets?
From an accounting perspective, risk-taking in companies and its effects
Evaluate the differences and similarities between external and internal auditors
Can taxation be considered a human rights policy? Analyse the problem
What are the consequences of India’s current tax structure on individuals with a lower income?

Accounting courses

We’ve included a Finance Dissertation Research Example with reference to a Finance Dissertation Structure:

The Indian Economic Crisis of 1991 – The title of your Finance Dissertation must focus on your research objective.
Abstract – The 1991 Indian economic crisis was…………….. imports and other external factors. The abstract part must include a summary of the research problem or objective of the research, the research design and a summary of the results.
Introduction – The introduction must reflect your research on the Indian Economic Crisis of 1991 in a way that the audience already gets to know what the research is going to include.

3.1 Background (background of the study)

3.2 Problem Statement (significance of the problem in context)

3.3 Purpose/Research Questions (What caused the Crisis, how was the crisis revived etc.)

Review of Literature – The Review of Literature Section must include a theoretical rationale of the problem, the importance of the study, and the significance of the results.
Methodology – The Methodology Section must include the description of the subjects, research methods used in the data collection and any limitations issues involved.
Significance/Implications (Results of the Discussion)

*Please note that the above-mentioned structure is only for your reference to get an idea of writing a Finance Dissertation.

Choosing the right topic for your Finance dissertation to plan the work, all the above-mentioned aspects must be given equal importance. This blog has included the best dissertation topic in finance in MBA, accounting, and banking you can choose while writing a dissertation.

Finance research papers and dissertations should be prepared in a way that answers the core question while also being relevant to the remainder of the study. For example, if the dissertation’s major question is “what is the link between foreign exchange rates and the interest rates of a specific country,” the dissertation should provide suitable illustrations to help illustrate the topic. It should also go through the major and minor concerns that are relevant to this topic. Furthermore, utilise proper language to ensure that the article is readily understood by readers. The overall purpose of the project is to produce a well-written, well-researched, and well-supported dissertation.

It takes around 2 years to complete an MBA in India while 1 year to complete a full-time MBA in other countries.

A finance dissertation must be 100-300 pages long.

It takes around 5 years to obtain a Doctorate in Finance.

Hopefully, this blog assisted you in finding out your finance dissertation topics and structure for your course. If you require any assistance regarding your application process while enrolling for your further studies, our experts at Leverage Edu are just one click away. Call us anytime at 1800 572 000 for a free counselling session!

Damanpreet Kaur Vohra

Daman is an author with profound expertise in writing engaging and informative content focused on EdTech and Study Abroad. With a keen understanding of these domains, Daman excels at creating complex concepts into accessible, reader-friendly material. With a proven track record of insightful articles, Daman stands as a reliable source for providing content for EdTech and Study Abroad.

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38 Banking and Finance Dissertation Topics Ideas

Banking and Finance Dissertation Topics: Dissertation topics in banking and finance focus on how monetary exchanges and programs function in the banking sector, both nationally and internationally. This is also a very famous academic discipline throughout the world. Banking and Finance explore the dynamic, fast-paced world of money, shares, credit, and investments. Finance is an essential […]

Banking and Finance explore the dynamic, fast-paced world of money, shares, credit, and investments . Finance is an essential part of our economy as it provides liquidity in terms of money or assets required for individuals and businesses to invest in the future.

If you are looking for banking and finance dissertation topics, your search end here. Below is the list of best-selected banking and finance dissertation topics. Also, you can check our related posts for accounting & finance dissertation topics and financial accounting dissertation topics .

Best Banking and Finance Dissertation Topics ideas for college students

Banking is the business of protecting money for others . Banks lend this money, generating interest that creates profits for the bank and its customers. A bank is a financial institution licensed to accept deposits and make loans.

Research topics in banking and finance have been collected together and presented in the form of an extensive list as below:

Implementing blockchain applications in the field of banking and finance: a descriptive approach.
Banking and finance post-COVID-19 pandemic: a review of the literature.
Studying the effects of monetary policy on banking and finance: a systematic analysis.
Islamic banking and finance: a quantitative study.
Effects of BREXIT on banking and finance contracts: a descriptive approach.
Financial fragility in the domain of banking and finance: a review of the literature.
Development and governance of Islamic banking and finance in X country.
Evaluating risk assessment in the area of banking and finance in X country.
Islamic banking and finance in present-day financial crisis: focus on current and future issues.
Sustainability in Islamic banking and finance: a systematic analysis.
Correlational analysis of financial stability, bank competition, and fire sales.
Development of a hypothetical model for internalization of banking and finance institutions.
The role played by information technology in the field of banking and finance: a systematic review.
Historical analysis of banking and finance in the UK: a quantitative study.
Credit risk versus financial risk in Islamic banking and finance.
Comparative analysis of banking and finance in UK and Asia.
The role played by intuitive decision-making in the field of banking and finance.
Working on mergers and acquisitions in the field of banking and finance.
Investigating illicit cyber activity in the field of banking and finance.
Interest-free banking and finance in the developing countries of the world.
Importance of compensation and risk incentives in the field of banking and finance.
Quality control in banking and finance: an exploratory study.
How e-commerce can be applied in the field of banking and finance.
Studying the big data and analytics as a customer loyalty tool in banking and finance: a descriptive study.
Data mining and banking and finance: a systematic analysis.
Studying the effects of enterprise risk management on the performance of firms in X country: focus on banking and finance sector.
Operations of Islamic banking and finance in the West: a systematic analysis.
Ethics in banking and finance programs: a review of the literature.
Relationship between risk and reputational capital in the banking and finance sector.
Highlighting the social and ethical issues in the banking and finance sector.
The current dilemma of financial instability in the structure of the banking and finance sector.
Comparative analysis of islands and small states within the banking and finance sector.
Studying the operational efficiency in the banking and finance sector: a review of the literature.
Social banking and social finance: a descriptive approach.
Finance employment: focus on UK banks.

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