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• Published: 24 January 2019

Financial literacy and the need for financial education: evidence and implications

• Annamaria Lusardi 1  

Swiss Journal of Economics and Statistics volume  155 , Article number:  1 ( 2019 ) Cite this article

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1 Introduction

Throughout their lifetime, individuals today are more responsible for their personal finances than ever before. With life expectancies rising, pension and social welfare systems are being strained. In many countries, employer-sponsored defined benefit (DB) pension plans are swiftly giving way to private defined contribution (DC) plans, shifting the responsibility for retirement saving and investing from employers to employees. Individuals have also experienced changes in labor markets. Skills are becoming more critical, leading to divergence in wages between those with a college education, or higher, and those with lower levels of education. Simultaneously, financial markets are rapidly changing, with developments in technology and new and more complex financial products. From student loans to mortgages, credit cards, mutual funds, and annuities, the range of financial products people have to choose from is very different from what it was in the past, and decisions relating to these financial products have implications for individual well-being. Moreover, the exponential growth in financial technology (fintech) is revolutionizing the way people make payments, decide about their financial investments, and seek financial advice. In this context, it is important to understand how financially knowledgeable people are and to what extent their knowledge of finance affects their financial decision-making.

An essential indicator of people’s ability to make financial decisions is their level of financial literacy. The Organisation for Economic Co-operation and Development (OECD) aptly defines financial literacy as not only the knowledge and understanding of financial concepts and risks but also the skills, motivation, and confidence to apply such knowledge and understanding in order to make effective decisions across a range of financial contexts, to improve the financial well-being of individuals and society, and to enable participation in economic life. Thus, financial literacy refers to both knowledge and financial behavior, and this paper will analyze research on both topics.

As I describe in more detail below, findings around the world are sobering. Financial literacy is low even in advanced economies with well-developed financial markets. On average, about one third of the global population has familiarity with the basic concepts that underlie everyday financial decisions (Lusardi and Mitchell, 2011c ). The average hides gaping vulnerabilities of certain population subgroups and even lower knowledge of specific financial topics. Furthermore, there is evidence of a lack of confidence, particularly among women, and this has implications for how people approach and make financial decisions. In the following sections, I describe how we measure financial literacy, the levels of literacy we find around the world, the implications of those findings for financial decision-making, and how we can improve financial literacy.

2 How financially literate are people?

2.1 measuring financial literacy: the big three.

In the context of rapid changes and constant developments in the financial sector and the broader economy, it is important to understand whether people are equipped to effectively navigate the maze of financial decisions that they face every day. To provide the tools for better financial decision-making, one must assess not only what people know but also what they need to know, and then evaluate the gap between those things. There are a few fundamental concepts at the basis of most financial decision-making. These concepts are universal, applying to every context and economic environment. Three such concepts are (1) numeracy as it relates to the capacity to do interest rate calculations and understand interest compounding; (2) understanding of inflation; and (3) understanding of risk diversification. Translating these concepts into easily measured financial literacy metrics is difficult, but Lusardi and Mitchell ( 2008 , 2011b , 2011c ) have designed a standard set of questions around these concepts and implemented them in numerous surveys in the USA and around the world.

Four principles informed the design of these questions, as described in detail by Lusardi and Mitchell ( 2014 ). The first is simplicity : the questions should measure knowledge of the building blocks fundamental to decision-making in an intertemporal setting. The second is relevance : the questions should relate to concepts pertinent to peoples’ day-to-day financial decisions over the life cycle; moreover, they must capture general rather than context-specific ideas. Third is brevity : the number of questions must be few enough to secure widespread adoption; and fourth is capacity to differentiate , meaning that questions should differentiate financial knowledge in such a way as to permit comparisons across people. Each of these principles is important in the context of face-to-face, telephone, and online surveys.

Three basic questions (since dubbed the “Big Three”) to measure financial literacy have been fielded in many surveys in the USA, including the National Financial Capability Study (NFCS) and, more recently, the Survey of Consumer Finances (SCF), and in many national surveys around the world. They have also become the standard way to measure financial literacy in surveys used by the private sector. For example, the Aegon Center for Longevity and Retirement included the Big Three questions in the 2018 Aegon Retirement Readiness Survey, covering around 16,000 people in 15 countries. Both ING and Allianz, but also investment funds, and pension funds have used the Big Three to measure financial literacy. The exact wording of the questions is provided in Table  1 .

2.2 Cross-country comparison

The first examination of financial literacy using the Big Three was possible due to a special module on financial literacy and retirement planning that Lusardi and Mitchell designed for the 2004 Health and Retirement Study (HRS), which is a survey of Americans over age 50. Astonishingly, the data showed that only half of older Americans—who presumably had made many financial decisions in their lives—could answer the two basic questions measuring understanding of interest rates and inflation (Lusardi and Mitchell, 2011b ). And just one third demonstrated understanding of these two concepts and answered the third question, measuring understanding of risk diversification, correctly. It is sobering that recent US surveys, such as the 2015 NFCS, the 2016 SCF, and the 2017 Survey of Household Economics and Financial Decisionmaking (SHED), show that financial knowledge has remained stubbornly low over time.

Over time, the Big Three have been added to other national surveys across countries and Lusardi and Mitchell have coordinated a project called Financial Literacy around the World (FLat World), which is an international comparison of financial literacy (Lusardi and Mitchell, 2011c ).

Findings from the FLat World project, which so far includes data from 15 countries, including Switzerland, highlight the urgent need to improve financial literacy (see Table  2 ). Across countries, financial literacy is at a crisis level, with the average rate of financial literacy, as measured by those answering correctly all three questions, at around 30%. Moreover, only around 50% of respondents in most countries are able to correctly answer the two financial literacy questions on interest rates and inflation correctly. A noteworthy point is that most countries included in the FLat World project have well-developed financial markets, which further highlights the cause for alarm over the demonstrated lack of the financial literacy. The fact that levels of financial literacy are so similar across countries with varying levels of economic development—indicating that in terms of financial knowledge, the world is indeed flat —shows that income levels or ubiquity of complex financial products do not by themselves equate to a more financially literate population.

Other noteworthy findings emerge in Table  2 . For instance, as expected, understanding of the effects of inflation (i.e., of real versus nominal values) among survey respondents is low in countries that have experienced deflation rather than inflation: in Japan, understanding of inflation is at 59%; in other countries, such as Germany, it is at 78% and, in the Netherlands, it is at 77%. Across countries, individuals have the lowest level of knowledge around the concept of risk, and the percentage of correct answers is particularly low when looking at knowledge of risk diversification. Here, we note the prevalence of “do not know” answers. While “do not know” responses hover around 15% on the topic of interest rates and 18% for inflation, about 30% of respondents—in some countries even more—are likely to respond “do not know” to the risk diversification question. In Switzerland, 74% answered the risk diversification question correctly and 13% reported not knowing the answer (compared to 3% and 4% responding “do not know” for the interest rates and inflation questions, respectively).

These findings are supported by many other surveys. For example, the 2014 Standard & Poor’s Global Financial Literacy Survey shows that, around the world, people know the least about risk and risk diversification (Klapper, Lusardi, and Van Oudheusden, 2015 ). Similarly, results from the 2016 Allianz survey, which collected evidence from ten European countries on money, financial literacy, and risk in the digital age, show very low-risk literacy in all countries covered by the survey. In Austria, Germany, and Switzerland, which are the three top-performing nations in term of financial knowledge, less than 20% of respondents can answer three questions related to knowledge of risk and risk diversification (Allianz, 2017 ).

Other surveys show that the findings about financial literacy correlate in an expected way with other data. For example, performance on the mathematics and science sections of the OECD Program for International Student Assessment (PISA) correlates with performance on the Big Three and, specifically, on the question relating to interest rates. Similarly, respondents in Sweden, which has experienced pension privatization, performed better on the risk diversification question (at 68%), than did respondents in Russia and East Germany, where people have had less exposure to the stock market. For researchers studying financial knowledge and its effects, these findings hint to the fact that financial literacy could be the result of choice and not an exogenous variable.

To summarize, financial literacy is low across the world and higher national income levels do not equate to a more financially literate population. The design of the Big Three questions enables a global comparison and allows for a deeper understanding of financial literacy. This enhances the measure’s utility because it helps to identify general and specific vulnerabilities across countries and within population subgroups, as will be explained in the next section.

2.3 Who knows the least?

Low financial literacy on average is exacerbated by patterns of vulnerability among specific population subgroups. For instance, as reported in Lusardi and Mitchell ( 2014 ), even though educational attainment is positively correlated with financial literacy, it is not sufficient. Even well-educated people are not necessarily savvy about money. Financial literacy is also low among the young. In the USA, less than 30% of respondents can correctly answer the Big Three by age 40, even though many consequential financial decisions are made well before that age (see Fig.  1 ). Similarly, in Switzerland, only 45% of those aged 35 or younger are able to correctly answer the Big Three questions. Footnote 1 And if people may learn from making financial decisions, that learning seems limited. As shown in Fig.  1 , many older individuals, who have already made decisions, cannot answer three basic financial literacy questions.

Financial literacy across age in the USA. This figure shows the percentage of respondents who answered correctly all Big Three questions by age group (year 2015). Source: 2015 US National Financial Capability Study

A gender gap in financial literacy is also present across countries. Women are less likely than men to answer questions correctly. The gap is present not only on the overall scale but also within each topic, across countries of different income levels, and at different ages. Women are also disproportionately more likely to indicate that they do not know the answer to specific questions (Fig.  2 ), highlighting overconfidence among men and awareness of lack of knowledge among women. Even in Finland, which is a relatively equal society in terms of gender, 44% of men compared to 27% of women answer all three questions correctly and 18% of women give at least one “do not know” response versus less than 10% of men (Kalmi and Ruuskanen, 2017 ). These figures further reflect the universality of the Big Three questions. As reported in Fig.  2 , “do not know” responses among women are prevalent not only in European countries, for example, Switzerland, but also in North America (represented in the figure by the USA, though similar findings are reported in Canada) and in Asia (represented in the figure by Japan). Those interested in learning more about the differences in financial literacy across demographics and other characteristics can consult Lusardi and Mitchell ( 2011c , 2014 ).

Gender differences in the responses to the Big Three questions. Sources: USA—Lusardi and Mitchell, 2011c ; Japan—Sekita, 2011 ; Switzerland—Brown and Graf, 2013

3 Does financial literacy matter?

A growing number of financial instruments have gained importance, including alternative financial services such as payday loans, pawnshops, and rent to own stores that charge very high interest rates. Simultaneously, in the changing economic landscape, people are increasingly responsible for personal financial planning and for investing and spending their resources throughout their lifetime. We have witnessed changes not only in the asset side of household balance sheets but also in the liability side. For example, in the USA, many people arrive close to retirement carrying a lot more debt than previous generations did (Lusardi, Mitchell, and Oggero, 2018 ). Overall, individuals are making substantially more financial decisions over their lifetime, living longer, and gaining access to a range of new financial products. These trends, combined with low financial literacy levels around the world and, particularly, among vulnerable population groups, indicate that elevating financial literacy must become a priority for policy makers.

There is ample evidence of the impact of financial literacy on people’s decisions and financial behavior. For example, financial literacy has been proven to affect both saving and investment behavior and debt management and borrowing practices. Empirically, financially savvy people are more likely to accumulate wealth (Lusardi and Mitchell, 2014 ). There are several explanations for why higher financial literacy translates into greater wealth. Several studies have documented that those who have higher financial literacy are more likely to plan for retirement, probably because they are more likely to appreciate the power of interest compounding and are better able to do calculations. According to the findings of the FLat World project, answering one additional financial question correctly is associated with a 3–4 percentage point greater probability of planning for retirement; this finding is seen in Germany, the USA, Japan, and Sweden. Financial literacy is found to have the strongest impact in the Netherlands, where knowing the right answer to one additional financial literacy question is associated with a 10 percentage point higher probability of planning (Mitchell and Lusardi, 2015 ). Empirically, planning is a very strong predictor of wealth; those who plan arrive close to retirement with two to three times the amount of wealth as those who do not plan (Lusardi and Mitchell, 2011b ).

Financial literacy is also associated with higher returns on investments and investment in more complex assets, such as stocks, which normally offer higher rates of return. This finding has important consequences for wealth; according to the simulation by Lusardi, Michaud, and Mitchell ( 2017 ), in the context of a life-cycle model of saving with many sources of uncertainty, from 30 to 40% of US retirement wealth inequality can be accounted for by differences in financial knowledge. These results show that financial literacy is not a sideshow, but it plays a critical role in saving and wealth accumulation.

Financial literacy is also strongly correlated with a greater ability to cope with emergency expenses and weather income shocks. Those who are financially literate are more likely to report that they can come up with $2000 in 30 days or that they are able to cover an emergency expense of $400 with cash or savings (Hasler, Lusardi, and Oggero, 2018 ).

With regard to debt behavior, those who are more financially literate are less likely to have credit card debt and more likely to pay the full balance of their credit card each month rather than just paying the minimum due (Lusardi and Tufano, 2009 , 2015 ). Individuals with higher financial literacy levels also are more likely to refinance their mortgages when it makes sense to do so, tend not to borrow against their 401(k) plans, and are less likely to use high-cost borrowing methods, e.g., payday loans, pawn shops, auto title loans, and refund anticipation loans (Lusardi and de Bassa Scheresberg, 2013 ).

Several studies have documented poor debt behavior and its link to financial literacy. Moore ( 2003 ) reported that the least financially literate are also more likely to have costly mortgages. Lusardi and Tufano ( 2015 ) showed that the least financially savvy incurred high transaction costs, paying higher fees and using high-cost borrowing methods. In their study, the less knowledgeable also reported excessive debt loads and an inability to judge their debt positions. Similarly, Mottola ( 2013 ) found that those with low financial literacy were more likely to engage in costly credit card behavior, and Utkus and Young ( 2011 ) concluded that the least literate were more likely to borrow against their 401(k) and pension accounts.

Young people also struggle with debt, in particular with student loans. According to Lusardi, de Bassa Scheresberg, and Oggero ( 2016 ), Millennials know little about their student loans and many do not attempt to calculate the payment amounts that will later be associated with the loans they take. When asked what they would do, if given the chance to revisit their student loan borrowing decisions, about half of Millennials indicate that they would make a different decision.

Finally, a recent report on Millennials in the USA (18- to 34-year-olds) noted the impact of financial technology (fintech) on the financial behavior of young individuals. New and rapidly expanding mobile payment options have made transactions easier, quicker, and more convenient. The average user of mobile payments apps and technology in the USA is a high-income, well-educated male who works full time and is likely to belong to an ethnic minority group. Overall, users of mobile payments are busy individuals who are financially active (holding more assets and incurring more debt). However, mobile payment users display expensive financial behaviors, such as spending more than they earn, using alternative financial services, and occasionally overdrawing their checking accounts. Additionally, mobile payment users display lower levels of financial literacy (Lusardi, de Bassa Scheresberg, and Avery, 2018 ). The rapid growth in fintech around the world juxtaposed with expensive financial behavior means that more attention must be paid to the impact of mobile payment use on financial behavior. Fintech is not a substitute for financial literacy.

4 The way forward for financial literacy and what works

Overall, financial literacy affects everything from day-to-day to long-term financial decisions, and this has implications for both individuals and society. Low levels of financial literacy across countries are correlated with ineffective spending and financial planning, and expensive borrowing and debt management. These low levels of financial literacy worldwide and their widespread implications necessitate urgent efforts. Results from various surveys and research show that the Big Three questions are useful not only in assessing aggregate financial literacy but also in identifying vulnerable population subgroups and areas of financial decision-making that need improvement. Thus, these findings are relevant for policy makers and practitioners. Financial illiteracy has implications not only for the decisions that people make for themselves but also for society. The rapid spread of mobile payment technology and alternative financial services combined with lack of financial literacy can exacerbate wealth inequality.

To be effective, financial literacy initiatives need to be large and scalable. Schools, workplaces, and community platforms provide unique opportunities to deliver financial education to large and often diverse segments of the population. Furthermore, stark vulnerabilities across countries make it clear that specific subgroups, such as women and young people, are ideal targets for financial literacy programs. Given women’s awareness of their lack of financial knowledge, as indicated via their “do not know” responses to the Big Three questions, they are likely to be more receptive to financial education.

The near-crisis levels of financial illiteracy, the adverse impact that it has on financial behavior, and the vulnerabilities of certain groups speak of the need for and importance of financial education. Financial education is a crucial foundation for raising financial literacy and informing the next generations of consumers, workers, and citizens. Many countries have seen efforts in recent years to implement and provide financial education in schools, colleges, and workplaces. However, the continuously low levels of financial literacy across the world indicate that a piece of the puzzle is missing. A key lesson is that when it comes to providing financial education, one size does not fit all. In addition to the potential for large-scale implementation, the main components of any financial literacy program should be tailored content, targeted at specific audiences. An effective financial education program efficiently identifies the needs of its audience, accurately targets vulnerable groups, has clear objectives, and relies on rigorous evaluation metrics.

Using measures like the Big Three questions, it is imperative to recognize vulnerable groups and their specific needs in program designs. Upon identification, the next step is to incorporate this knowledge into financial education programs and solutions.

School-based education can be transformational by preparing young people for important financial decisions. The OECD’s Programme for International Student Assessment (PISA), in both 2012 and 2015, found that, on average, only 10% of 15-year-olds achieved maximum proficiency on a five-point financial literacy scale. As of 2015, about one in five of students did not have even basic financial skills (see OECD, 2017 ). Rigorous financial education programs, coupled with teacher training and high school financial education requirements, are found to be correlated with fewer defaults and higher credit scores among young adults in the USA (Urban, Schmeiser, Collins, and Brown, 2018 ). It is important to target students and young adults in schools and colleges to provide them with the necessary tools to make sound financial decisions as they graduate and take on responsibilities, such as buying cars and houses, or starting retirement accounts. Given the rising cost of education and student loan debt and the need of young people to start contributing as early as possible to retirement accounts, the importance of financial education in school cannot be overstated.

There are three compelling reasons for having financial education in school. First, it is important to expose young people to the basic concepts underlying financial decision-making before they make important and consequential financial decisions. As noted in Fig.  1 , financial literacy is very low among the young and it does not seem to increase a lot with age/generations. Second, school provides access to financial literacy to groups who may not be exposed to it (or may not be equally exposed to it), for example, women. Third, it is important to reduce the costs of acquiring financial literacy, if we want to promote higher financial literacy both among individuals and among society.

There are compelling reasons to have personal finance courses in college as well. In the same way in which colleges and university offer courses in corporate finance to teach how to manage the finances of firms, so today individuals need the knowledge to manage their own finances over the lifetime, which in present discounted value often amount to large values and are made larger by private pension accounts.

Financial education can also be efficiently provided in workplaces. An effective financial education program targeted to adults recognizes the socioeconomic context of employees and offers interventions tailored to their specific needs. A case study conducted in 2013 with employees of the US Federal Reserve System showed that completing a financial literacy learning module led to significant changes in retirement planning behavior and better-performing investment portfolios (Clark, Lusardi, and Mitchell, 2017 ). It is also important to note the delivery method of these programs, especially when targeted to adults. For instance, video formats have a significantly higher impact on financial behavior than simple narratives, and instruction is most effective when it is kept brief and relevant (Heinberg et al., 2014 ).

The Big Three also show that it is particularly important to make people familiar with the concepts of risk and risk diversification. Programs devoted to teaching risk via, for example, visual tools have shown great promise (Lusardi et al., 2017 ). The complexity of some of these concepts and the costs of providing education in the workplace, coupled with the fact that many older individuals may not work or work in firms that do not offer such education, provide other reasons why financial education in school is so important.

Finally, it is important to provide financial education in the community, in places where people go to learn. A recent example is the International Federation of Finance Museums, an innovative global collaboration that promotes financial knowledge through museum exhibits and the exchange of resources. Museums can be places where to provide financial literacy both among the young and the old.

There are a variety of other ways in which financial education can be offered and also targeted to specific groups. However, there are few evaluations of the effectiveness of such initiatives and this is an area where more research is urgently needed, given the statistics reported in the first part of this paper.

5 Concluding remarks

The lack of financial literacy, even in some of the world’s most well-developed financial markets, is of acute concern and needs immediate attention. The Big Three questions that were designed to measure financial literacy go a long way in identifying aggregate differences in financial knowledge and highlighting vulnerabilities within populations and across topics of interest, thereby facilitating the development of tailored programs. Many such programs to provide financial education in schools and colleges, workplaces, and the larger community have taken existing evidence into account to create rigorous solutions. It is important to continue making strides in promoting financial literacy, by achieving scale and efficiency in future programs as well.

In August 2017, I was appointed Director of the Italian Financial Education Committee, tasked with designing and implementing the national strategy for financial literacy. I will be able to apply my research to policy and program initiatives in Italy to promote financial literacy: it is an essential skill in the twenty-first century, one that individuals need if they are to thrive economically in today’s society. As the research discussed in this paper well documents, financial literacy is like a global passport that allows individuals to make the most of the plethora of financial products available in the market and to make sound financial decisions. Financial literacy should be seen as a fundamental right and universal need, rather than the privilege of the relatively few consumers who have special access to financial knowledge or financial advice. In today’s world, financial literacy should be considered as important as basic literacy, i.e., the ability to read and write. Without it, individuals and societies cannot reach their full potential.

See Brown and Graf ( 2013 ).

Abbreviations

Defined benefit (refers to pension plan)

Defined contribution (refers to pension plan)

Financial Literacy around the World

National Financial Capability Study

Organisation for Economic Co-operation and Development

Programme for International Student Assessment

Survey of Consumer Finances

Survey of Household Economics and Financial Decisionmaking

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Acknowledgements

This paper represents a summary of the keynote address I gave to the 2018 Annual Meeting of the Swiss Society of Economics and Statistics. I would like to thank Monika Butler, Rafael Lalive, anonymous reviewers, and participants of the Annual Meeting for useful discussions and comments, and Raveesha Gupta for editorial support. All errors are my responsibility.

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Lusardi, A. Financial literacy and the need for financial education: evidence and implications. Swiss J Economics Statistics 155 , 1 (2019). https://doi.org/10.1186/s41937-019-0027-5

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Financial technology and the future of banking

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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.

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Broby, D. Financial technology and the future of banking. Financ Innov 7 , 47 (2021). https://doi.org/10.1186/s40854-021-00264-y

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A Review of the Research on Financial Performance and Its Determinants

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To carry out the review, the study was designed in such a manner as to enable us to: (a) identify the degree of interest that researchers displayed for scientific grounding of concepts they operate with and (b) identify the degree to which new lines of research have been shaped on determinants of financial performance. Based on a sample of 45 articles which analyzed the corporate financial performance, published during 2014–2019, was established a database which details: the researches’ topic; dependent and independent analyzed variables (and the indicators used for their assessment); samples; sources of data and periods in which they have been collected; results of the research; and authors’ contributions in defining the concept of performance. In terms of study’s first aim, we have shown that authors are concerned with grounding concepts with which they operate, but they mostly focus on the determinants and not on the financial performance. In terms of determinants of the financial performance, the study reveals that the research is more detailed and they extend the analyses with new variables (such as ethics of stakeholders, corporate lobbying, corporate culture, green credit or non-financial reporting) for explaining the dynamics of the financial performance.

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Tudose, M.B., Avasilcai, S. (2020). A Review of the Research on Financial Performance and Its Determinants. In: Prostean, G., Lavios Villahoz, J., Brancu, L., Bakacsi, G. (eds) Innovation in Sustainable Management and Entrepreneurship. SIM 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-44711-3_17

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What do Financial Markets say about the Exchange Rate?

Financial markets play two roles with implications for the exchange rate: they accommodate risk sharing and act as a source of shocks. In prevailing theories, these roles are seen as mutually exclusive and individually face challenges in explaining exchange rate dynamics. However, we demonstrate that this is not necessarily the case. We develop an analytical framework that characterizes the link between exchange rates and finance across all conceivable market structures. Our findings indicate that full market segmentation is not necessary for financial shocks to explain exchange rates. Moreover, financial markets can accommodate a significant extent of international risk sharing without leading to the classic exchange rate puzzles. We identify plausible market structures where both roles coexist, addressing challenges faced when examined separately.

The authors have no relevant or material financial interests that relate to the research described in this paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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The OFR Financial Stress Index (OFR FSI or FSI) is a daily market-based snapshot of stress in global financial markets. Originally, the OFR FSI was constructed from 33 financial market variables that are correlated with some form of financial stress. Seven of these variables are based on LIBOR or other ceasing and/or already-ceased benchmark interest rates. As such, these seven variables are now obsolete. However, since its inception, the OFR FSI was intended to allow for the periodic replacement of obsolete variables as the need arises (Working Paper no. 23-07).

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May 16, 2023 | By Gregory Phelan and David Love

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April 25, 2023 | By R. Jay Kahn, Matthew McCormick, Vy Nguyen, Mark Paddrik, and H. Peyton Young

Anatomy of the Repo Rate Spikes in September 2019

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April 20, 2023 | By Jean-Paul L’Huillier and Gregory Phelan

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Empirical estimates find that the relationship between inflation and the output gap is close to nonexistent—a so-called flat Phillips curve. We show that standard pricing frictions cannot simultaneously produce a flat Phillips curve and meaningful inflation from plausible supply shocks. This is because imposing a flat Phillips curve immediately implies that the price level is also rigid with respect to supply shocks. In quantitative versions of the New Keynesian model, price markup shocks need to be several orders of magnitude bigger than other shocks in order to fit the data, leading to unreasonable assessments of the magnitude of the increase in costs during inflationary episodes. Hence, we propose a strategic microfoundation of price stickiness in which prices are sticky with respect to demand shocks but flexible with respect to supply shocks (Working Paper no. 23-03).

April 3, 2023 | By Thomas M. Eisenbach and Gregory Phelan

Fragility of Safe Assets

The market for U.S. Treasury securities experienced extreme stress in March 2020, when prices dropped precipitously (yields spiked) over a period of about two weeks. This was highly unusual, as Treasury prices typically increase during times of stress. Using a theoretical model, we show that markets for safe assets can be fragile due to strategic interactions among investors who hold Treasury securities for their liquidity characteristics. Worried about having to sell at potentially worse prices in the future, such investors may sell preemptively, leading to self-fulfilling “market runs” that are similar to traditional bank runs in some respects. Our results motivate potential policy interventions to stabilize the market during times of stress and disruption (Working Paper no. 23-02).

March 22, 2023 | By William Chen and Gregory Phelan

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September 22, 2022 | By Andrew Ellul and Dasol Kim

Counterparty Choice, Bank Interconnectedness, and Bank Risk-taking

We investigate whether banks’ counterparty choices in OTC derivative markets contribute to network fragility. We use novel confidential regulatory data and show that banks are more likely to choose densely connected non-bank counterparties and do not hedge such exposures. Banks are also more likely to connect with riskier counterparties for their most material exposures, suggesting the existence of moral hazard behavior in network formation. Finally, we show that these exposures are correlated with systemic risk measures despite greater regulatory oversight after the crisis. (Working Paper no. 22-06).

August 23, 2022 | By Ron Alquist and Ram Yamarthy

Hedge Funds and Treasury Market Price Impact: Evidence from Direct Exposures

The increasing importance of non-bank financial intermediaries has raised new questions about the risks that hedge funds pose to the financial system. The OFR examined how changes in hedge fund exposures affect U.S. Treasury prices and the yield curve. Using confidential hedge fund data from the SEC's Form Private Fund (PF), OFR analysts calculated hedge funds' aggregate, net Treasury exposures, and their fluctuations over time. This revealed economically significant and consistent evidence that changes in hedge fund exposures are related to Treasury yield changes. Furthermore, particular strategy groups and lower-levered hedge funds were seen to have a larger estimated price impact on Treasuries. Finally, asset pricing tests show that U.S. Treasury investors demand additional return compensation due to the risks associated with hedge fund demand (Working Paper no. 22-05).

July 11, 2022 | By Todd Keister and Cyril Monnet

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June 28, 2022 | By Chase P. Ross, Landon J. Ross, and Sharon Y. Ross

Cash-Hedged Stock Returns

This paper studies firms’ cash holdings and the implications for asset prices and financial stability. Corporate cash piles vary across companies and over time, and cash holdings are important for financial stability because of their value in crises. Firms’ cash holdings earn low returns that are correlated across firms. Thus, the asset pricing results are important both for investors who are managing a portfolio’s risk and policymakers concerned about sources of vulnerability. We show how investors can hedge out the cash on firms’ balance sheets when making portfolio choices. Cash generates variation in beta estimates, and we decompose stock betas into components that depend on the firm’s cash holding, return on cash, and cash-hedged return. Common asset pricing premia have large implicit cash positions, and portfolios of cash-hedged premia often have higher Sharpe ratios because of the correlation between firms’ cash returns. We show the value of a dollar increased in 2020, and firms hold cash because they are riskier. (Working Paper no. 22-03).

April 14, 2022 | By Johannes Poeschl and Ram Yamarthy

Aggregate Risk in the Term Structure of Corporate Credit

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February 10, 2022 | By Samuel J. Hempel, Dasol Kim, and Russ Wermers

Financial Intermediary Funding Constraints and Segmented Markets

This working paper examines the role of financial intermediaries, namely authorized participants (APs), in the propagation of shocks across funds that they support and the underlying assets held by those funds. Corporate bond ETF trades by the Federal Reserve through the Secondary Market Corporate Credit Facility (SMCCF) beginning in May 2020 were extremely large and likely alleviated inventory capacity constraints for APs that were counterparties to those transactions. ETFs that were not traded by the Federal Reserve, but overlap in their bond holdings with those traded, exhibit a positive and significant price reaction within minutes of the transaction. Consistent evidence is found for the prices of their underlying bonds. The paper's findings support the view that the inclusion of ETFs in the SMCCF had broader "spillover" effects in stabilizing markets beyond the ETFs directly targeted by the program (Working Paper no. 22-01).

June 9, 2021 | By Mark Paddrik and H. Peyton Young

Assessing the Safety of Central Counterparties

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April 1, 2021 | By Daniel Barth and R. Jay Kahn

Hedge Funds and the Treasury Cash-Futures Disconnect

This paper examines the potential financial stability risks of the Treasury cash-futures basis trade, an arbitrage of pricing differences between the two Treasury markets. Using regulatory data on hedge fund exposures and repurchase agreement (repo) transactions, the paper provides evidence that at its peak the trade was associated with more than half of hedge funds' Treasury positions and a quarter of dealers' repo lending. The trade exposes hedge funds to rollover risk on repo financing and margin risk on the futures, both of which materialized in March 2020. While Treasury market disruptions spurred hedge funds to sell Treasuries, the unwinding of the basis trade was likely a consequence rather than the primary cause of the stress. We present evidence suggesting prompt intervention by the Federal Reserve prevented larger spillovers from the trade into broader Treasury market functioning. (Working Paper no. 21-01)

December 3, 2020 | By Berardino Palazzo and Ram Yamarthy

Credit Risk and the Transmission of Interest Rate Shocks

Unexpected changes in interest rates, often observed through the course of monetary policy, can have a significant effect on corporate credit risk. Using high frequency measures of interest rate surprises surrounding Federal Open Market Committee announcements and daily credit default swap (CDS) spreads, this paper finds a positive, significant relationship between monetary policy shocks and corporate credit risk over the last two decades. One component of the spread affected is compensation related to expected losses in default. The other affected component is the credit risk premium, which measures additional compensation for default risk. Riskier firms, with higher CDS spreads or leverage, or lower market capitalization, are much more sensitive to monetary policy shocks. Among the three measures of firm risk, CDS spreads appear to capture this sensitivity best. High frequency and daily equity returns also exhibit a significant and asymmetric response to policy announcements. (Working Paper no. 20-05)

June 18, 2020 | By Mark Paddrik and Simpson Zhang

Central Counterparty Default Waterfalls and Systemic Loss

This paper examines how a central counterparty (CCP) uses a default waterfall to manage and allocate resources to cover defaults of clearing members and clients. A resilient waterfall ensures cleared payments are paid in full and on-time, reducing the threat to financial stability from losses and their spillovers. However, the amount of resources collected and their allocation affect clearing incentives. This paper models and evaluates the trade-offs between resiliency and participation in a credit default swaps market. It finds that the benefits of greater central clearing rates generally dominate the benefits of increased waterfall resources. (Working Paper no. 20-4)

February 25, 2020 | By Daniel Barth and Phillip Monin

Illiquidity in Intermediate Portfolios: Evidence from Large Hedge Funds

This paper examines whether hedge funds’ returns include a premium that compensates investors for accepting the risk from illiquid asset holdings. It finds that the premium is large and a significant share of risk-adjusted returns. The size of the premium matters for financial stability because it signals investors’ view of the importance of illiquidity risk. (Working Paper no. 20-03)

February 25, 2020 | By Daniel Barth, Laurel Hammond, and Phillip Monin

Leverage and Risk in Hedge Funds

This paper examines the relationship between hedge funds’ use of leverage and their portfolio risk. It finds that more leveraged funds tend to have less volatile returns and less chance of an extreme negative return. More leveraged funds also tend to hold higher quality and more liquid assets. (Working Paper no. 20-02)

February 25, 2020 | By Daniel Barth, Juha Joenvaara, Mikko Kauppila, and Russ Wermers

The Hedge Fund Industry is Bigger (and has Performed Better) Than You Think

This paper shows that hedge fund industry gross assets exceeded $8.3 trillion, and net assets were at least $5.0 trillion, at year-end 2016. This estimate is around 37 percent larger than the next largest estimate. This paper also shows that funds reporting publicly available data have much lower returns, and much higher net flows, than funds reporting only non-public regulatory data. The outperformance of the non-publicly reporting funds appears to arise entirely from alpha rather than greater exposure to systematic risk factors. (Working Paper no. 20-01)

October 23, 2019 | By Robert Garrison, Pankaj Jain, and Mark Paddrik

Cross-Asset Market Order Flow, Liquidity, and Price Discovery

This paper examines the complex intra-day linkages between the U.S. equity securities market and the equity derivatives market. The paper finds a positive, but short-lived, relationship between the two markets’ order flow activities, which relate to the supply, demand, and withdrawal of liquidity between the two markets. The paper also finds that cross-asset market order flow is a key component of liquidity and price discovery, particularly during periods of market volatility. (Working Paper no. 19-04)

October 1, 2019 | By Mathias S. Kruttli, Phillip J. Monin, and Sumudu W. Watugala

The Life of the Counterparty: Shock Propagation in Hedge Fund-Prime Broker Credit Networks

This paper shows the post-crisis hedge fund-prime broker credit network is concentrated among 10 percent of participants. The average fund borrows from three brokers, and the brokers lending the most are highly connected. The paper finds that a liquidity shock to a prime broker results in reduced borrowing by hedge funds due to the broker reducing its supply of credit. Larger, more connected, and better-performing funds, and those that do less over-the-counter trading, are better able to compensate for the reduction in credit from the broker. (Working Paper no. 19-03)

August 6, 2019 | By Meraj Allahrakha, Jill Cetina, Benjamin Munyan, and Sumudu Watugala

The Effects of the Volcker Rule on Corporate Bond Trading: Evidence from the Underwriting Exemption

This paper examines the impact of the Volcker rule, which bans proprietary trading by commercial banks and their affiliates, with some exceptions. It finds evidence that the rule has increased the cost of liquidity provided by firms it covers, but not decreased the firms’ exposure to liquidity risk. It also finds that the rule has decreased the market share of covered firms. Customers appear to be trading more with non-bank dealers, who are exempt from the Volcker rule but also cannot borrow at the Federal Reserve's discount window. (Working Paper no. 19-02)

March 12, 2019 | By Mark Paddrik and Stathis Tompaidis

Market-Making Costs and Liquidity: Evidence from CDS Markets

This paper examines whether liquidity deteriorated in the single-name credit default swaps market due to regulatory reforms after the 2007-09 financial crisis. It finds evidence of both increased spreads and lower volumes, consistent with the reforms increasing the cost of market-making for bank-dealers. It also finds that transaction prices between dealers and clients have become more dependent on the inventories of individual dealers as interdealer trade has declined. (Working Paper no. 19-01)

October 9, 2018 | By Haelim Anderson, Daniel Barth, and Dong Beom Choi

Reducing Moral Hazard at the Expense of Market Discipline: The Effectiveness of Double Liability Before and During the Great Depression

This paper examines the impact of double liability on bank risks and depositor safety before and during the Great Depression. Under double liability, shareholders of failing banks lost their initial investments and had to pay up to the par value of their stock to compensate depositors. The paper finds that double liability did not reduce bank risk before the Great Depression, but that deposits were less susceptible to runs. (Working Paper no. 18-06)

August 29, 2018 | By Andrea L. Eisfeldt, Bernard Herskovic, Sriram Rajan, and Emil Siriwardane

OTC Intermediaries

This paper estimates the systemic effects of exit by a key over-the-counter (OTC) intermediary. In the model, risk-averse traders are connected by a core-periphery network. If traders are also averse to concentrated bilateral exposures, then the incomplete network prevents full risk sharing. The impact of the network structure on prices is quantified using proprietary data on all credit default swap transactions in the United States from 2010 to 2013. There are a small number of key OTC intermediaries whose exit can move markets dramatically. Eliminating one of these intermediaries leads to over a 20 percent increase in credit spreads. The Internet Appendix includes extensions of the model. (Working Paper no. 18-05)

August 28, 2018 | By Agostino Capponi, Paul Glasserman, and Marko Weber

Swing Pricing for Mutual Funds: Breaking the Feedback Loop Between Fire Sales and Fund Runs

This paper develops a model of a downward spiral of falling prices and increasing redemptions that can lead to the failure of a mutual fund. It shows how mutual funds can best design swing pricing for effectiveness at preventing runs, even under extreme market stress. (Working Paper no. 18-04)

April 19, 2018 | By Simpson Zhang and Mihaela van der Schaar

Reputational Dynamics in Financial Networks During a Crisis

This paper studies the role of learning and reputation in economic networks, such as interbank lending and derivatives trading networks, in times of market distress or financial crisis. The model demonstrates the importance of maintaining firm anonymity and identifies network structures that offer increased resilience. (Working Paper no. 18-03)

April 10, 2018 | By Jen-Wen Chang and Simpson Zhang

Competitive Pay and Excessive Manager Risk-taking

This paper assesses whether compensation plans can drive excessive risk-taking. It develops a model showing that principals offer contracts incentivizing less risky behavior when the market for managers is sluggish. But hot labor markets result in contracts that incentivize risk-taking. The market for executive talent heats up for larger projects and during financial bubbles, when debt funding increases. The results suggest policymakers should consider the impacts of compensation and corporate governance policies on competition for managers. (Working Paper no. 18-02)

March 28, 2018 | By Joe McLaughlin, Adam Minson, Nathan Palmer, and Eric Parolin

The OFR Financial System Vulnerabilities Monitor

This paper describes the purpose, construction, interpretation, and use of the OFR Financial System Vulnerabilities Monitor. The monitor, a heat map of 58 quantitative indicators, is a starting point for assessing vulnerabilities in the U.S. financial system. The OFR launched the monitor in 2017 to help fulfill its mandate to measure and monitor risks to U.S. financial stability. (Working Paper no. 18-01)

December 15, 2017 | By Mathias S. Kruttli, Phillip J. Monin, and Sumudu W. Watugala

Investor Concentration, Flows, and Cash Holdings: Evidence from Hedge Funds

Some hedge funds have a few large investors. Such a concentrated investor base can make a fund vulnerable to unexpected requests for large redemptions. This paper shows that U.S. hedge funds in part account for that risk by holding more cash and liquid assets. These holdings help funds accommodate large outflows, but also result in lower risk-adjusted returns. The Internet Appendix includes methodology details. (Working Paper no. 17-07)

November 2, 2017 | By Mark Paddrik and H. Peyton Young

How Safe are Central Counterparties in Derivatives Markets?

How likely is a central counterparty, or CCP, to default after a severe credit shock? This working paper uses credit default swap data to estimate the direct and indirect impacts of a default by CCP counterparties in derivatives trades. It finds that a CCP could be more vulnerable to failure than conventional stress tests have shown. (Working Paper no. 17-06)

October 31, 2017 | By Kenechukwu Anadu and Viktoria Baklanova

The Intersection of U.S. Money Market Mutual Fund Reforms, Bank Liquidity Requirements, and the Federal Home Loan Bank System

After the financial crisis, reforms of money market funds and changes to banks’ liquidity requirements had an unintended consequence of increased Federal Home Loan Banks’ reliance on short-term funding from money market funds to finance longer-term loans and other assets. This increase could make the financial system more vulnerable and pose risks to financial stability. (Working Paper no. 17-05)

October 25, 2017 | By Phillip Monin

The OFR Financial Stress Index

The 2007-09 financial crisis showed that stress in the financial system can have devastating effects on the economy. To measure such stress, the OFR has developed a Financial Stress Index. This working paper describes how the index is constructed and how the OFR uses it to monitor financial stability. (Working Paper no. 17-04)

September 29, 2017 | By Mark D. Flood, Dror Y. Kenett, Robin L. Lumsdaine, and Jonathan K. Simon

The Complexity of Bank Holding Companies: A New Measurement Approach

Some bank holding companies are very complex, with hundreds or thousands of subsidiaries. This complexity complicates the job of unwinding a failed bank holding company. In this working paper, OFR researchers propose a new way to measure complexity that can support the resolution process after a bank holding company fails. (Working Paper no. 17-03)

April 5, 2017 | By Katherine Gleason, Steve Bright, Francis Martinez, and Charles Taylor

Europe’s CoCos Provide a Lesson on Uncertainty

European banks issue contingent convertible bonds. These bonds can force investors to absorb losses when a bank is under stress. The authors find that heightened uncertainty about discretion by banks on when to make payments to investors and by regulators on when to trigger a loss absorption mechanism worsened price declines in a stressed market. (Working Paper no. 17-02)

February 21, 2017 | By Paul Glasserman and Qi Wu

Persistence and Procyclicality in Margin Requirements

This paper describes how to set margin levels for derivatives contracts so that margin calls do not add to market stress during times of instability. Price volatility varies by asset class. Certain qualities of volatility should be taken into account to set the most effective margin levels without adding to market stress. (Working Paper no. 17-01)

December 20, 2016 | By Anqi Liu, Mark Paddrik, Steve Yang, and Xingjia Zhang

Interbank Contagion: An Agent-based Model Approach to Endogenously Formed Networks

The authors create an agent-based model that can help regulators understand risk in the interbank funding market. Tests of the model against actual bank failures before, during, and after the 2007-09 financial crisis suggest that the market has become more resilient to asset write-downs and liquidity shocks. The model uses balance sheet data from more than 6,600 U.S. banks. (Working Paper no. 16-14)

December 6, 2016 | By Mark Paddrik, Haelim Park, and Jessie Jiaxu Wang

Bank Networks and Systemic Risk: Evidence from the National Banking Acts

This paper uses unique data to analyze how the national banking acts in 1863 and 1864 reshaped the U.S. bank network in the 1860s. The laws concentrated reserves in New York and regional cities, creating systemically important banks. The paper shows this concentration made contagion more likely if big banks faced economic shocks. (Working Paper no. 16-13)

December 1, 2016 | By Mark Paddrik, Sriram Rajan, and H. Peyton Young

Contagion in the CDS Market

This paper assesses the risk of contagion in the credit default swap (CDS) market. This risk emerges through the inability of CDS counterparties to make payments during systemic stress. The authors find that the central counterparty contributes significantly less to network contagion than do several peripheral firms that are large net sellers of CDS protection. (Working Paper no. 16-12)

November 10, 2016 | By Meraj Allahrakha, Jill Cetina, and Benjamin Munyan

Do Higher Capital Standards Always Reduce Bank Risk? The Impact of the Basel Leverage Ratio on the U.S. Triparty Repo Market

This paper examines how risk-taking in the repurchase agreement, or repo, market changed after regulators introduced the supplementary leverage ratio for banks. The paper finds that broker-dealers owned by U.S. bank holding companies now borrow less in the repo market overall after the change, but a larger percentage of the borrowing is backed by more risky collateral. (Working Paper no. 16-11)

October 11, 2016 | By Richard Neuberg, Paul Glasserman, Benjamin Kay, and Sriram Rajan

The Market-implied Probability of European Government Intervention in Distressed Banks

This paper assesses the likelihood of European government support in distressed banks. To measure market expectations of these events, the authors study the credit spread between old credit default swap contracts and new ones with a definition of default linked to government intervention. (Working Paper no. 16-10)

September 27, 2016 | By Matthias Raddant and Dror Y. Kenett

Interconnectedness in the Global Financial Market

This working paper shows how network analysis can facilitate the monitoring of movements by stocks in the global financial system over time. The paper analyzes nearly 4,000 stocks in 15 countries. It concludes that stock returns tend to move together within regions — but not across them — in times of stability, but move in sync globally in times of crisis. (Working Paper no. 16-09)

August 23, 2016 | By Viktoria Baklanova, Cecilia Caglio, Frank Keane, and Burt Porter

A Pilot Survey of Agent Securities Lending Activity

A new securities lending survey sheds light on these transactions that help underpin smooth-functioning capital markets. The pilot project by the OFR, Federal Reserve, and staff of the Securities and Exchange Commission shows that participating agents facilitated about $1 trillion in daily securities loans during a three-day period in 2015. Collecting these data on a permanent basis could help regulators identify potential vulnerabilities in a key component of our financial system. (Working Paper no. 16-08)

July 26, 2016 | By Samim Ghamami and Paul Glasserman

Does OTC Derivatives Reform Incentivize Central Clearing?

The requirement that standardized over-the-counter derivatives be cleared through central counterparties, or CCPs, is intended in part to create a cost incentive favoring central clearing. This working paper shows that the cost incentive does not necessarily favor central clearing, and when it does, it might be because of insufficient levels of guarantee funds, which banks provide to protect CCPs in the event of CCP member default. (Working Paper no. 16-07)

May 26, 2016 | By Andrea Aguiar, Richard Bookstaber, Dror Y. Kenett, and Thomas Wipf

A Map of Collateral Uses and Flows

Collateral is exchanged among market participants to support financial activities, including secured funding, securities lending, securities exchanges, margin lending, derivatives, and clearing. This working paper creates a collateral map to show how collateral moves among bilateral counterparties, triparty banks, and central counterparties, and can spread stress through the financial system. The paper also discusses the recent increase in collateral demand, effects of post-crisis regulation, and collateral-related stress scenarios. (Working Paper no. 16-06)

May 11, 2016 | By Cindy M. Vojtech, Benjamin S. Kay, and John C. Driscoll

The Real Consequences of Bank Mortgage Lending Standards.

This paper describes how mortgage lending standards, as measured by responses to the Federal Reserve's quarterly Senior Loan Officer Opinion Survey, relate to changes in the availability of mortgage loans at banks from 1990 to 2013. The research suggests that the survey's reported changes in credit standards are a leading indicator of the financial industry's vulnerability to shocks. (Working Paper no. 16-05)

April 20, 2016 | By Harry Mamaysky and Paul Glasserman

Does Unusual News Forecast Market Stress?

This paper investigates the use of automated text analysis by computers as a tool for monitoring financial stability. The authors find negative sentiment extracted from tens of thousands of news articles about 50 large financial services companies is useful in forecasting volatility in the stock market. The method, which also considers the "unusualness" of news, may help anticipate stress in the financial system. (Working Paper no. 16-04)

March 30, 2016 | By John Bluedorn and Haelim Park

Stopping Contagion with Bailouts: Microevidence from Pennsylvania Bank Networks During the Panic of 1884

This working paper examines how a bailout orchestrated by New York Clearinghouse member banks stopped financial contagion during the Panic of 1884. The private-sector assistance to Metropolitan National Bank, an important correspondent bank for many banks outside New York City, prevented a minor financial crisis in New York from becoming a broad, systemic event, according to the authors' analysis. (Working Paper no. 16-03)

March 23, 2016 | By Mark D. Flood and Phillip Monin

Form PF and Hedge Funds: Risk-measurement Precision for Option Portfolios

This paper examines the precision of Form PF in measuring the risk hedge funds pose to the financial system. Hedge funds and other private funds now file Form PF with the Securities and Exchange Commission. The paper extends the methodology of a 2015 OFR working paper and finds that options significantly weaken the risk-measurement tolerances in Form PF. (Working Paper no. 16-02)

March 8, 2016 | By Jill Cetina, Mark Paddrik, and Sriram Rajan

Stressed to the Core: Counterparty Concentrations and Systemic Losses in CDS Markets

This paper applies the Federal Reserve's supervisory stress test scenarios to examine the impacts on banks — and the banking system as a whole — from default of their largest counterparties in the credit derivatives markets. The authors find higher loss concentrations for the banking system than for individual firms and potential for large indirect losses when a major counterparty defaults. (Working Paper no. 16-01)

November 25, 2015 | By Maya Eden and Benjamin Kay

Safe Assets as Commodity Money

This paper examines the systemic implications of the supply of liquid safe assets, such as Treasury bills. The paper explores how liquid safe assets facilitate the trades of risky assets. The paper finds that financial markets may be remarkably resilient to changes in the stock of liquid assets. (Working Paper no. 15-23)

October 29, 2015 | By Benjamin Munyan

Regulatory Arbitrage in Repo Markets

This paper documents a pattern of foreign-owned broker-dealers reducing their borrowing in the U.S. triparty repo market, a key source of short-term funding in the financial system, at quarter end and immediately returning to the market when a new quarter begins. This activity reduces their capital requirements under the leverage ratio. (Working Paper no. 15-22)

October 20, 2015 | By Paul Glasserman and H. Peyton Young

Contagion in Financial Networks

This paper surveys the rapidly growing literature about interconnectedness and financial stability. The paper focuses on insights in the literature on the relationship between network structure and the vulnerability of the financial system to contagion. (Working Paper no. 15-21)

October 7, 2015 | By Jill Cetina and Katherine Gleason

The Difficult Business of Measuring Banks' Liquidity: Understanding the Liquidity Coverage Ratio

Bank regulators adopted a new requirement called the Liquidity Coverage Ratio after the financial crisis to help ensure banks maintain enough liquid assets to cover their financial obligations during times of stress. This paper uses a series of increasingly complex examples to demonstrate issues in analyzing this new liquidity metric. (Working Paper no. 15-20)

October 1, 2015 | By Jingnan Chen, Mark D. Flood, and Richard B. Sowers

Measuring the Unmeasurable: An Application of Uncertainty Quantification to Financial Portfolios

Uncertainty is a crucial factor in financial stability, but it is notoriously difficult to measure. This working paper extends techniques from engineering to quantify fundamental economic uncertainty, and applies the method to an example of portfolio stress testing. By this measure, uncertainty peaked in late 2008. (Working Paper no. 15-19)

September 16, 2015 | By Richard Bookstaber and Mark Paddrik

An Agent-based Model for Crisis Liquidity Dynamics

This paper presents an agent-based model for examining price impacts and liquidity dynamics during financial crises, which are often characterized by sharp reductions in liquidity followed by cascades of falling prices. The model highlights the implications of changes in market makers' ability to provide intermediation services and the decision cycles of liquidity demanders versus liquidity suppliers during a crisis. (Working Paper no. 15-18)

September 9, 2015 | By Viktoria Baklanova, Adam Copeland, and Rebecca McCaughrin

Reference Guide to U.S. Repo and Securities Lending Markets

This paper is a reference guide on U.S. repo and securities lending markets. It discusses the main institutional features of these markets, their vulnerabilities, and data gaps that prevent market participants and regulators from addressing known vulnerabilities. (Working Paper no. 15-17)

August 19, 2015 | By Paul Glasserman and Linan Yang

Bounding Wrong-Way Risk in Measuring Counterparty Risk

This paper proposes a new method for bounding the impact of "wrong-way risk" on counterparty credit risk measurement for a portfolio of derivatives. Wrong-way risk refers to the possibility that a counterparty's default risk increases with the market value of the exposure. (Working Paper no. 15-16)

August 13, 2015 | By Chester Curme, Rosario N. Mantegna, Dror Y. Kenett, Michele Tumminello, and H. Eugene Stanley

How Lead-Lag Correlations Affect the Intraday Pattern of Collective Stock Dynamics

This paper explores how the increasing correlation among intraday stock returns affects the possibility to diversify investment risk and potentially may affect market stability. (Working Paper no. 15-15)

August 6, 2015 | By Sumudu W. Watugala

Economic Uncertainty and Commodity Futures Volatility

This paper investigates the dynamics of commodity futures volatility and analyzes the impact of increased emerging market demand on commodity markets. (Working Paper no. 15-14)

July 30, 2015 | By Mark D. Flood, Phillip Monin, and Lina Bandyopadhyay

Gauging Form PF: Data Tolerances in Regulatory Reporting on Hedge Fund Risk Exposures

This paper examines the precision of Form PF, a regulatory filing introduced after the financial crisis to measure risk exposures for private funds, including hedge funds. The paper finds that Form PF's measurement tolerances are large enough to allow private funds with dissimilar risk profiles to report similar risk measurements to regulators. (Working Paper no. 15-13)

June 18, 2015 | By Dror Y. Kenett, Sary Levy-Carciente, Adam Avakian, H. Eugene Stanley, and Shlomo Havlin

Dynamical Macroprudential Stress Testing Using Network Theory

This paper presents a dynamic bipartite network model for a stress test of a banking system's sensitivity to external shocks in individual asset classes. As a case study, the model is applied to investigate the Venezuelan banking system from 1998 to 2013. The model quantifies the sensitivity of bank portfolios to different shock scenarios and identifies systemic vulnerabilities that stem from connectivity and network effects, and their time evolution. The model provides a framework for dynamical macroprudential stress testing. (Working Paper no. 15-12)

May 28, 2015 | By Mark D. Flood, John C. Liechty, and Thomas Piontek

Systemwide Commonalities in Market Liquidity

This paper identifies hidden liquidity regimes (high, medium and low) across a broad range of financial markets that can be used for characterizing periods of market stress and identifying underlying predictors of liquidity shocks. This regime could have provided meaningful predictions of liquidity disruptions up to 15 trading days in advance of the 2008 financial crisis. These methods offer a potential framework for monitoring and predicting a systemwide collapse in market liquidity, which could signal a collapse of liquidity in the funding markets as experienced in the financial crisis. (Working Paper no. 15-11)

May 13, 2015 | By Javed Ahmed, Christopher Anderson, and Rebecca Zarutskie

Are the Borrowing Costs of Large Financial Firms Unusual?

This paper examines evidence of a too-big-to-fail subsidy for large financial firms by comparing borrowing costs of large and small firms across industries. The paper finds that larger firms borrow more cheaply in many industries, and this size effect is often largest in nonfinancial industries. These results challenge the notion that expected government bailouts are behind borrowing cost advantages enjoyed by the largest financial firms. (Working Paper no. 15-10)

May 13, 2015 | By Jill Cetina and Bert Loudis

The Influence of Systemic Importance Indicators on Banks' Credit Default Swap Spreads

This paper examines credit default swap (CDS) spreads in a sample of international banks for evidence of a benefit related to possible measures of systemic importance. The authors find a consistent, statistically significant negative relationship between five-year CDS spreads of banks and nine different systemic importance indicators. The paper shows that the benefit is most pronounced for banks within a certain asset range. Such evidence is weaker for banks identified by regulators as global systemically important banks. (Working Paper no. 15-09)

May 7, 2015 | By Agostino Capponi, W. Allen Cheng, and Sriram Rajan

Systemic Risk: The Dynamics under Central Clearing

This paper develops a model for concentration risks that clearing members pose to central counterparties. Over time, larger clearing members crowd out smaller clearing members. Systemic risk is created because high clearing member concentration results in relatively lower lending, higher cost of capital, and increasingly costly hedging. To address this risk, the paper proposes a self-funding systemic risk charge. (Working Paper no. 15-08)

May 7, 2015 | By Paul Glasserman, Ciamac C. Moallemi, and Kai Yuan

Hidden Illiquidity with Multiple Central Counterparties

This paper focuses on the systemic risks in markets cleared by multiple central counterparties (CCPs). Each CCP charges margins based on the potential impact from the default of a clearing member and subsequent liquidation of a large position. Swaps dealers can split their positions among multiple CCPs, effectively "hiding" potential liquidation costs. A lack of coordination among CCPs can lead to a "race to the bottom" because CCPs with lower perceived liquidation costs can drive competitors out of the market. (Working Paper no. 15-07)

May 7, 2015 | By Therese C. Scharlemann and Stephen H. Shore

The Effect of Negative Equity on Mortgage Default: Evidence from HAMP PRA

This paper uses data from the Home Affordable Modification Program to examine the impact of principal forgiveness on mortgage default. On average 3.1 percent of loans become delinquent and exit the program each quarter. The authors estimate that the rate would have been 3.8 percent absent principal forgiveness, which averaged 28 percent of the initial mortgage balance. (Working Paper no. 15-06)

April 2, 2015 | By Charles W. Calomiris, Matthew Jaremski, Haelim Park, and Gary Richardson

Liquidity Risk, Bank Networks, and the Value of Joining the Federal Reserve System

The Federal Reserve System was created to reduce risks related to seasonal swings in loan demand and to stabilize fluctuations in interest rates. Many state-chartered banks chose not to join the system because of the cost of the Federal Reserve's reserve requirements. The inability to attract many state-chartered banks created indirect access to government protection (lender of last resort) without federal regulation. (Working Paper no. 15-05)

March 26, 2015 | By Mark D. Flood and Oliver R. Goodenough

Contract as Automaton: The Computational Representation of Financial Agreements

This paper shows that the fundamental legal structure of a well-written financial contract follows a logic that can be formalized mathematically as a "deterministic finite automaton." This allows, for example, automated reasoning to determine whether a contract is internally coherent and complete. The paper illustrates the process by representing a simple loan agreement as an automaton. (Working Paper no. 15-04)

March 10, 2015 | By Richard Bookstaber, Michael D. Foley, and Brian F. Tivnan

Market Liquidity and Heterogeneity in the Investor Decision Cycle

This paper presents a model of market liquidity in which those who need to sell come into the market with a greater need for immediacy than those who are willing to buy. This is a critical market dynamic behind the illiquidity that arises during market dislocations and crises, when some are in forced-selling mode while others are hesitant to come in and take the other side of the trade. (Working Paper no. 15-03)

March 3, 2015 | By Paul Glasserman and Gowtham Tangirala

Are the Federal Reserve's Stress Test Results Predictable?

This paper examines the results of four rounds of stress testing of the largest U.S. bank holding companies, starting in 2009. The data reveal a growing correlation in results from one year to the next, highlighting whether the stress tests in their current form may be losing some of their information value over time. The authors discuss the implications of these patterns and recommend greater diversity in the stress scenarios analyzed. (Working Paper no. 15-02)

February 11, 2015 | By Richard Bookstaber, Paul Glasserman, Garud Iyengar, Yu Luo, Venkat Venkatasubramanian, and Zhizun Zhang

Process Systems Engineering as a Modeling Paradigm for Analyzing Systemic Risk in Financial Networks

This paper demonstrates the value of signed directional graphs, a modeling methodology used for risk detection in process engineering, in tracing the path of potential instabilities and feedback loops within the financial system. This approach expands the usefulness of network models of the financial system by including critical information on the direction of influence and the points of control between the various nodes of the network. (Working Paper no. 15-01)

December 22, 2014 | By Emil Siriwardane

Concentrated Capital Losses and the Pricing of Corporate Credit Risk

This paper uses proprietary credit default swap (CDS) data for 2010 to 2014 to show that capital fluctuations for sellers of CDS protection are an important determinant of CDS spread movements. (Working Paper no. 14-10)

November 25, 2014 | By Mark Paddrik, Roy Hayes, William Scherer, and Peter Beling

Effects of Limit Order Book Information Level on Market Stability Metrics

This paper uses an agent-based model of the limit order book to explore how the levels of information available to participants, exchanges, and regulators can be used for insights on the stability and resiliency of a market. (Working Paper no. 14-09)

November 13, 2014 | By Phillip Monin

Hedging Market Risk in Optimal Liquidation

This paper discusses optimal strategies for financial institutions in selling large blocks of securities and in hedging the resulting market risk. (Working Paper no. 14-08)

October 23, 2014 | By Robert Engle and Emil Siriwardane

Structural GARCH: The Volatility-Leverage Connection

This paper proposes a new model of volatility featuring a "leverage multiplier" by which financial leverage amplifies equity volatility. The model estimates daily asset returns and asset volatility. (Working Paper no. 14-07)

August 19, 2014 | By Paul Glasserman and Wanmo Kang

Design of Risk Weights

This paper investigates the design of risk weights used in setting minimum levels of regulatory capital for banks and presents a formula for regulators to set those weights by analyzing bank portfolios. (Working Paper no. 14-06)

July 29, 2014 | By Rick Bookstaber, Mark Paddrik, and Brian Tivnan

An Agent-based Model for Financial Vulnerability

This paper develops an agent-based model that uses a map of funding and collateral flows to analyze the financial system's vulnerability to fire sales and runs. (Working Paper no. 14-05)

July 2, 2014 | By Zoltan Pozsar

Shadow Banking: The Money View

This paper presents an accounting framework for measuring the sources and uses of short-term funding in the global financial system and introduces a dynamic map of global funding flows. (Working Paper no. 14-04)

May 29, 2014 | By Andrea Aguiar, Rick Bookstaber, and Thomas Wipf

A Map of Funding Durability and Risk

This paper features a funding map to illustrate the flow of funding from its initial providers through the bank/dealers to the end-users. In addition to showing the plumbing of the system, the paper also shows the processes for transforming funding liquidity, credit quality, and tenor. The paper then applies the funding map to track risk through various types of financial institutions, and to identify gaps in data needed for financial stability monitoring. (Working Paper no. 14-03)

May 9, 2014 | By Mark D. Flood, Victoria L. Lemieux, Margaret Varga, and B.L. William Wong

The Application of Visual Analytics to Financial Stability Monitoring

This paper provides an overview of visual analytics - the science of analytical reasoning enhanced by interactive visualizations produced by data analytics software - and discusses potential benefits in monitoring financial stability. (Working Paper no. 14-02)

April 16, 2014 | By Javed I. Ahmed

Competition in Lending and Credit Ratings

This paper explores the relationship between the quality of corporate credit ratings and competition in lending between the public bond market and banks. It finds that the quality of credit ratings plays a role in financial stability because the behavior of rating agencies can reduce the impact of macroeconomic shocks. (Working Paper no. 14-01)

December 5, 2013 | By Matthew McCormick and Lynn Calahan

Common Ground: The Need for a Universal Mortgage Loan Identifier

The U.S. mortgage finance system is a critical part of our nation's financial system, representing 70 percent of U.S. household liabilities. The establishment of a single, cradle‐to‐grave, universal mortgage identifier that cannot be linked to individuals using publicly‐available data would significantly benefit regulators and researchers. (Working Paper no. 13-12)

September 4, 2013 | By Mark Flood, Jonathan Katz, Stephen Ong, and Adam Smith

Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

This paper explores tradeoffs between transparency and confidentiality in financial regulation and discusses new techniques from the fields of secure computation and statistical data privacy that can facilitate the secure sharing of financial information. (Working Paper no. 13-11)

July 18, 2013 | By Rick Bookstaber, Jill Cetina, Greg Feldberg, Mark Flood, and Paul Glasserman

Stress Tests to Promote Financial Stability: Assessing Progress and Looking to the Future

Stress testing of large bank holding companies in the United States - a valuable exercise used to determine regulatory capital and liquidity planning at these institutions - should be adapted to be made more useful for financial stability monitoring. (Working Paper no. 13-10)

June 21, 2013 | By Paul Glasserman and H. Peyton Young

How Likely is Contagion in Financial Networks?

This paper estimates how much interconnections among financial institutions - potential channels for contagion and amplification of shocks to the financial system - can increase expected losses from a wide range of shocks. (Working Paper no. 13-09)

May 15, 2013 | By Douglas J. Elliot, Greg Feldberg, and Andreas Lehnert

The History of Cyclical Macroprudential Policy in the United States

This paper presents a survey and historical narrative of policies to smooth the credit cycle in light of their potential future application as "macroprudential" policies to reduce the build-up of risks in U.S. financial markets. (Working Paper no. 13-08)

April 9, 2013 | By Paul Glasserman, Chulmin Kang, and Wanmo Kang

Stress Scenario Selection by Empirical Likelihood

This paper develops a method for selecting and analyzing stress-testing scenarios for financial risk assessment. (Working Paper no. 13-07)

March 13, 2013 | By Ozgur (Ozzy) Akay, Zeynep Senyuz, and Emre Yoldas

Hedge Fund Contagion and Risk-adjusted Returns: A Markov-switching Dynamic Factor Approach

This paper uses a flexible framework to analyze two important phenomena influencing the hedge fund industry - contagion and time variation in risk-adjusted return. (Working Paper no. 13-06)

February 7, 2013 | By Mark D. Flood and George G. Korenko

Systematic Scenario Selection: Stress Testing and the Nature of Uncertainty

This paper offers a technique for selecting multidimensional shock scenarios for use in financial stress testing. The technique uses a grid search of sparse, well distributed stress-test scenarios that are considered a middle ground between traditional stress testing and reverse stress testing. (Working Paper no. 13-05)

January 23, 2013 | By Nan Chen, Paul Glasserman, Behzad Nouri, and Markus Pelger

CoCos, Bail-in, and Tail Risk

This paper develops a capital structure model of a bank to analyze the incentives created by contingent convertibles (CoCos) and bail-in debt, which convert to equity when a bank approaches insolvency. These two forms of contingent capital have been proposed as potential mechanisms to enhance financial stability. (Working Paper no. 13-04)

December 21, 2012 | By Richard Bookstaber

Using Agent-Based Models for Analyzing Threats to Financial Stability

This paper discusses the concepts and research related to agent-based models and explores how the dynamics of a flock of birds in flight, a group of drivers in a traffic jam, or a panicked crowd of stampeding people might inform our analysis of threats to financial stability. (Working Paper no. 12-03)

March 26, 2012 | By Mark J. Flannery, Paul Glasserman, David K.A. Mordecai, and Cliff Rossi

Forging Best Practices in Risk Management

This paper assesses risk management practices and how risk management can be improved. The paper approaches risk management from three perspectives: (1) risk measurement by individual firms, (2) governance and incentives, and (3) systemic concerns. The paper evaluates each approach separately and also discusses the importance of considering them as interrelated. (Working Paper no. 12-02)

January 5, 2012 | By Dimitrios Bisias, Mark Flood, Andrew W. Lo, and Stavros Valavanis

A Survey of Systemic Risk Analytics

The paper focuses on quantitative tools to assess threats to financial stability. It gives a broad overview of the state of the art in measuring systemic risk by focusing on a key set of 31 specific measurements outlined elsewhere in peer-reviewed articles or working papers. (Working Paper no. 12-01)

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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