research papers on banking sector

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• Published: 18 June 2021

Financial technology and the future of banking

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The theory of the banking firm: a revision

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

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

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

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

Therefor it can be said that,

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

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

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

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

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

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

Theoretically, it can be shown that,

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

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

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

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

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

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

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

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

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

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

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

The evolution of the business model in a digital world

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

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

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

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

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

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

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

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

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

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

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

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

Open banking

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

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

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

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

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

Discussion: strategic options

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

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

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

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

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

The client retention strategy (incumbents)

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

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

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

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

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

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

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

The client acquisition strategy (challengers)

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

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

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

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

The banking as a service strategy (new entrants)

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

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

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

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

The social media payment strategy (disintermediators and disruptors)

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

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

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

Further research

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

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

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

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

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

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

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

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

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

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

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

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

Availability of data and materials

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

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European Journal of Innovation Management

ISSN : 1460-1060

Article publication date: 31 March 2022

Issue publication date: 29 August 2023

In recent years, the penetration of digital technologies in the financial industry determined the arising of Fintech, which generated a dynamic and rapid change that business operators and supervisory authorities in the banking industry are struggling to follow it. This is especially due to issues affecting financial intermediaries and customers, and potential risks of stability of the financial system. The aim of this paper is to provide a review of Fintech in the banking industry thus to update the knowledge about technology innovation in the banking sector, identify the major trends in the domain and delineate future research directions.

Design/methodology/approach

The study reviews 377 articles indexed on Scopus from 2014 to 2021 that focus on Fintech and the banking industry. The methodology adopted is structured in two steps: the keywords selection and the analysis of the documents extracted. The first step identified “Fintech” and “bank” as keywords to be searched within the title, abstract or keywords of documents indexed on Scopus; whereas the second step combined R and VOSviewer to provide a descriptive analysis of the dataset and the analysis of keywords and occurrences, respectively.

Results achieved in the study allow providing a systemic view of the Fintech in the banking industry, including the emergent phenomenon of digital banking. In particular, it is provided with a general overview and descriptive information on the entire sample of documents analyzed, their authors, the keywords used and the most cited works. Besides, a deepening on the model of digital banking is provided, by delineating the six dimensions of the key effects generated by the digital bank model.

Originality/value

Two main elements of originality characterize this study. The first one is related to the fact that few review studies have been published on Fintech in the banking industry, and the second one concerns the multiple dimensions of the impact of Fintech in the banking sector, which includes customer, company, bank, regulation authority and society.

• Financial technologies
• Financial services

Acknowledgements

The author(s) thank the Consorzio Universitario Interprovinciale Salentino (CUIS) for supporting this research.

Elia, G. , Stefanelli, V. and Ferilli, G.B. (2023), "Investigating the role of Fintech in the banking industry: what do we know?", European Journal of Innovation Management , Vol. 26 No. 5, pp. 1365-1393. https://doi.org/10.1108/EJIM-12-2021-0608

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

Omar h. fares.

Ted Rogers School of Retail Management, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3 Canada

Seung Hwan Mark Lee

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

Introduction

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

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

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

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

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

• What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?
• How does AI impact the customer's journey process in the banking sector, from customer acquisition to service delivery?
• What are the current research deficits and future directions of research in this field?

Methodology

Selection of articles.

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

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

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

Thematic analysis

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

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

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

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

Thematic map

Themes by timeline

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

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

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

Table ​ Table1 1 highlights the number of papers included in the themes and sub-themes. Overall, the papers related to Processes (77%) were the most frequently occurring, followed by Customer (59%) and Strategy-based (48%) papers. From 2013 onward, there was an increase in the inter-relation between all three areas of Strategy, Processes, and Customers. Since 2016, there has been a surge in research linking the themes of Processes and Customers. More recently, since 2017, papers combining Customers with Strategy have become more frequent.

Thematic count

Leximancer analysis

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

Conceptual and relational analyses

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

Concept map of content of all papers included in the study

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

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

Cloud map of content of all papers included in the study

Customer credit solution application-service blueprint

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

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

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

Customer credit solution application journey

Acquire customer

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

Visit bank’s website & apply for a credit solution

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

Receive a decision

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

Customer contact call center

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

General discussion

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

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

Future research directions

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

Tables ​ Tables2, 2 , ​ ,3, 3 , and ​ and4 4 provide a complete list of recommendations for future research. These recommendations were developed by reviewing all the future research directions included in the 44 papers. We followed Watkins' ( 2017 ) rigorous and accelerated data reduction (RADaR) technique, which allows for an effective and systematic way to analyze and synthesize calls for future research (Watkins, 2017 ).

Detailed future research directions—Theme: Strategy

Detailed future research recommendations—Theme: Processes

Detailed future research recommendations—Theme: Customer

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

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

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

Limitations and implications

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

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

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

Biographies

has a Master of Science in Management from Toronto Metropolitan University.

Dr. Irfan Butt

is Assistant Professor of Marketing at Toronto Metropolitan University.

Dr. Seung Hwan Mark Lee

is a Professor of Retail Management at Toronto Metropolitan University.

See Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4 4 .

Declarations

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Omar H. Fares, Email: [email protected] .

Irfan Butt, Email: [email protected] .

Seung Hwan Mark Lee, Email: [email protected] .

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• Open access
• Published: 01 November 2021

NPAs and profitability in Indian banks: an empirical analysis

• Santosh Kumar Das   ORCID: orcid.org/0000-0002-2685-3971 1 &
• Khushboo Uppal 1  

Future Business Journal volume  7 , Article number:  53 ( 2021 ) Cite this article

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As financial intermediaries, the commercial banks to a large extent depend on the performance of their lending as a critical source of earning. Due to increasing loan failures, the share of non-performing advances has increased substantially in recent years, thereby adversely impacting their profitability. The paper has examined the NPAs and profitability relationship by estimating the determinants of profitability of 39 public sector and private banks for the time period from 2005 to 2019. Using a set of bank specific and macroeconomic predictors of profitability, we found that NPA has negative impact on the rate of profit of the Indian banks. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Introduction

Growing incidence of non performing advances or loans can have potential adverse impact on the performance of the banks by squeezing their earnings, thereby reducing their profitability. Typically, a loan or advance becomes non-performing assets (NPAs) when a borrower defaults on the repayment of either the principal amount or unable to serve its debt. An NPA not only makes an asset unproductive, banks also fail to recover the principal capital. On the one hand, the interest earning of the bank declines; on the other side, there is a risk of recovery of principal amount. Falling interest income while directly impacts the profitability of a bank, under recovery of principal capital can result in erosion of bank’s capital base. Beyond a threshold level, the combination of both can potentially affect the stability a bank.

The Reserve Bank of India (RBI) has defined the NPAs as those assets for which principal or interest payment remains overdue for a period of ninety days. The RBI has classified three types of assets within the category of NPAs—substandard assets, doubtful assets, and loss assets [ 24 ]. A substandard asset is one if it remains as an NPA for a period less than or equal to 12 months. Similarly, a doubtful asset is defined as an asset which has remained as an NPA for a period of more than 12 months. In case of loss asset, the loss has already been identified and the amount is not written off. The combination of the above three types of assets forms total NPAs in a bank. The NPAs reduce the profitability of banks due to increase in operating costs and decline in their interest margins [ 7 , 19 ]. Studies have shown that a bank with high level of NPAs generally incurs ‘carrying costs’ on non-performing assets that reduces their profitability [ 4 ]. Also, a rise in NPA is likely to cause adverse impact on the profitability of the banks due to huge amount of provisioning requirements out of operating profits, which acts as a drain on profitability of banks. Thus, provisioning and carrying costs of NPAs act as drain on the profitability of the banks. Berger and Young [ 7 ] examined the relationship between bad loans and bank efficiency. They found that increasing incidence of loan failures forces banks to incur extra operating costs in the form of increased spending on monitoring of such assets and selling off of these loans. The banks are preoccupied with recovery procedures instead of concentrating on expanding their business. Higher the bank operating costs, lower will be the cost efficiency of banks and thus lower will be the profits. Operating costs include wages and salaries of employees and costs of running branch offices. These costs have an adverse impact on profitability of banks [ 30 ].

There are several factors, including non-performance of loans that can potentially affect the profitability of the banks. It can broadly be categorised into the bank specific, and macroeconomic factors. The bank-specific factors include non-performing advances [ 7 , 19 ], deposits [ 20 , 25 ], non-interest income [ 30 ] (Harbi 2019), interest income [ 5 ], operational efficiency [ 1 , 17 ], and capital adequacy [ 6 , 11 ]. The macroeconomic factor includes GDP growth [ 11 , 30 ], rate of inflation [ 9 ], and interest rate [ 8 , 11 , 29 ].

The present paper empirically analyses the impact of NPAs on the profitability of Indian public sector and leading private banks. Accordingly, the determinants of profitability have been estimated. The paper spreads over five sections. The introduction section has provided the background of the paper. The methodology section elaborates on the empirical strategy, data, variables and estimation model. The findings of the empirical exercise have been presented in the results section. In the discussion section, the findings of the study have been discussed. The concluding remarks have been presented the conclusion section.

Literature review

Previous studies, those have examined the relationship between the non-performance of loans and profitability of banks, have overwhelmingly concluded that NPAs have adverse impact on the profitability of the banks. There are several other factors, including NPAs that affect profitability which have been discussed in the literature.

In a study of banking sector of the US, for the period between 1970 and 1976, Martin [ 18 ] concluded that a rise in NPAs hurt the earnings of the banks, which reduces the profitability of banks. Masood and Ashraf [ 19 ] studied 25 Islamic banks from 12 countries from the Middle East, East Asian, African and South Asian regions for the period from 2006 to 2010. They found that non-performing loans negatively affects the bank performance and profitability. Ongore and Kusa [ 21 ] studied commercial banks in Kenya for the period from 2001 to 2010 and found a negative relationship between bank profitability and non-performing loans. Al-Jafari and Alchami [ 2 ] in their study of 17 Syrian banks, from 2004 to 2011, found a negative relationship between credit risk, as represented by loan loss provision, and bank profitability.

Cucinelli [ 10 ] using a sample of 488 listed and unlisted Italian banks over a period from 2007 to 2013 found that an increase in credit risk by either a rise in the non-performing loans ratio or a fall in credit portfolio quality as represented by a rise in loan loss provision ratio leads to banks to decrease their lending activity, which in turn can negatively impact their profitability. Higher NPAs results in lower bank profitability as higher NPAs require increased provisioning which eats into the profits of banks. Duraj and Moci [ 12 ] in their study of studied 16 Albanian banks between 1999 and 2014 found this negative relationship.

A study by Islam and Nishiyama [ 15 ], using data for 259 commercial banks in South Asian countries including India, for the period from 1997 to 2012, found that there is a negative relationship between non-performing loans and bank profitability. Similarly, Hashem [ 14 ] in his study of Egyptian banks for the period from 2004 to 2014 reported that higher loan loss provisions represent higher credit risk and hence lowers asset quality of banks which badly affects bank profitability. Bace [ 3 ] used data for 13,000 deposit taking institutions around the world for the period from 2014 to 2015 and found negative relationship between the NPAs and bank profitability. Similarly, a study by Etale et al. [ 13 ] that investigated the relationship between the non-performing loans and bank profitability for the period between 1994 and 2014, found a negative relationship between the two. Ozurumba [ 23 ], in his study of Nigerian commercial banks, concluded that the non-performing loans had an adverse impact on the profitability of banks for the period between 2000 and 2013. A study by Ozgur and Gorus [ 22 ] using data for Turkish banks for the period from 2006 to 2016 reported a negative relationship between non-performing loans and bank profitability. Previous studies have used the following dependent and explanatory variables for the empirical analysis.

Profitability

In the literature, usually the Return on Assets (ROA) is taken as a proxy for profitability, which measures the percentage of profits that a bank earns with respect to its total assets [ 15 , 17 , 27 ]. We have used ROA as a proxy for profitability as it reflects the average asset value during a fiscal year [ 15 ].

Bank specific determinants of profitability

Net Non-Performing Advances (NNPA) : The higher the portion of income generating assets among total bank assets, the higher would be the interest income of the banks. When NPAs increase, the proportion of interest earning assets falls, which leads to a fall in interest income, and hence ROA declines. Thus, NPAs and ROA have a negative relation; as NPA rises, return on assets (ROA) of banks falls [ 5 ]. Masood and Ashraf [ 19 ] and Berger and Young [ 7 ] have used non-performing loans to total assets as a measure of non-performing assets.

Deposits are the principal and the cheapest source of funds for banks. Therefore, the more deposits a bank collects, higher will be the availability of funds for generating loans and for other profitable uses such as investments, higher will be the bank profitability. Thus, a positive relationship between deposits and profitability is expected [ 20 , 25 ].

Non-interest income

The non-interest income is the income of banks from sources other than interest bearing assets. It is an indicator of bank’s off-balance sheet business and fee income, that is non-traditional activities. Non-interest income consists of commission, service charges, and fees, guarantee fees, net profit from sale of investment securities, and foreign exchange profit. Higher the bank’s non-interest income, higher will be the profits [ 30 ] (Harbi 2019). We have used the ratio of non-interest income to total income as the variable for non-interest income.

Interest Income: Net Interest Margin (NIM)

Interest income is the difference between the interest rate a bank pays to its depositors and the interest rate it charges to its borrowers. It is measured as a ratio of Net Income to Total Assets. NIM represents income of the banks from its ‘core lending business’. NIM is adversely affected by NPAs, because when an asset becomes an NPA, it stops generating interest income and hence, interest earned by banks reduces, while the bank still has to pay interest on deposits [ 5 ]. The profitability of a bank increases with increase in net interest earning.

Capital adequacy

High capital reserve requirement leads to higher profitability for banks because of lower costs of financial risk for banks. Lower financial risks attract higher deposits and boost the banking busies, thereby leading to higher rate of profit. Several studies have found a positive relation between capital and profitability of banks [ 1 , 6 , 11 , 19 ] (Harbi 2019). We have used Tier 1 capital ratio as prescribed by the Basel Committee as the variable for capital adequacy.

Operating costs

It is the total amount of wages and salaries of bank employees and the cost of running branch office facilities. Higher the operating costs, lower will be the profits. Sufian and Habibullah [ 30 ] used the ratio of overhead expenses to total assets as a measure of overhead expenses. Al-Homaidi et al. [ 1 ] used ratio of operating expenses to interest income as a measure of operating efficiency and argued that lower the ratio, higher will be the management efficiency and higher will be the profits of banks, whereas Kohlscheen et al. [ 17 ] took the ratio of operational expenses to gross revenues as the measure of operating efficiency.

Macroeconomic determinants of profitability

Gdp growth rate.

It is the value of all final goods and services produced in a country in a given period of time. During higher economic growth, profitability of banks would be higher because it encourages banks to lend more and charge higher interests [ 11 , 30 ].

It is the rate at which general price level of goods and services rises and the purchasing power of currency falls. Studies have found that profitability of banks will be higher with inflation. It has been used by prior studies on banks’ profitability [ 1 , 9 , 11 , 19 ].

Interest rate

There has been mixed evidence with respect to the relationship between interest rate and profitability. Low interest rates along with stiff competition among banks put pressure on interest margins of banks and hence negatively affect bank profitability (Trujillo-Ponce 2013). Studies such as Demirguç-Kunt and Huizinga [ 11 , 29 ], Bourke [ 8 ] have found a positive relationship between interest rates and bank profitability. The repo rate has been used as it reflects the lending rate of banks.

There are very few studies that cover current phase of NPAs with the revised definition while analysing the NPAs and profitability in Indian banks. The present study not only covers the recent phase of NPAs crisis, but also covers the time period with revised or new definition of NPAs. The definition of NPAs in the present study follows uniformity.

In this study, we have drawn a sample of 39 scheduled commercial banks, out of which 20 are Public sector Banks (PSBs) and 19 are domestic private banks. As per the recent data, these 39 banks constitute more than 90 percent of the banking operation in terms of assets, and close to 95 percent in terms of deposits and credit disbursement in India. In case of Public Sector Banks (PSBs), the overall management responsibility lies with the Government, as it remains the majority stakeholder. The PSBs are governed by specific acts (banking acts) passed by the parliament. On the other side, the private banks are registered under the Companies Act and governed as per that act. Their management lies with the majority promoters or shareholders. In terms of NPA volume, it is largely the PSBs and some private banks that have been badly affected by the NPA crisis. Few small private banks were dropped from the analysis due to unavailability of data. The time period of the study is from 2005 to 2019. The period of the study has been chosen as the definition of NPA underwent a change in 2004, and the NPA data from 2005 onward follow uniformity with the new definition. Annual data for the sample of 39 banks was collected from a Reserve Bank of India (RBI) publication—Statistical Tables Relating to Banks in India. The bank specific determinates or factors that potentially explain the profitability of banks were obtained the above report. The data for macroeconomic variables were collected from the Handbook of Statistics on Indian Economy—a publication of the RBI.

In this study, we have estimated the determinants of profitability of Indian Scheduled Commercial Banks. The dependent variable is profitability, which is determined by a set of bank specific and macroeconomic factors (Table 1 ). In the study, the Return on Assets (ROA) has been used as the variable for profitability. In literature, the ROA is widely used as indicator or proxy for bank profitability. It is an appropriate indicator of profitability, as it measures the earnings of a bank in relative to its total assets. Therefore, it has been used as the dependent variable. We have used the following bank specific explanatory variables like Net NPA, total deposit, interest income, non-interest income, operational efficiency and capital adequacy. The study has used the following macroeconomic predictors of bank profitability—economic growth, inflation and interest rate to estimate the determinants of profitability.

To understand how NPAs impact the profitability, we have estimated the determinants of profitability of Indian scheduled commercial banks. We have employed the panel data estimation procedure to estimate the factors that have affected the profitability of banks in India. The following functional relationship has been employed to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15. \({\varepsilon }_{i,t}\) is the error term.

In the above equation, six bank specific factors and three macro-economic factors combined determine the profitability of a bank. In the paper, we have employed both the fixed and random effect approach to estimate the determinants of bank profitability. By using fixed effect (FE) model, the impact of variables those are time variant can be analysed. The FE estimation also controls for all time invariant heterogeneity among the sample banks. It therefore is likely to produce unbiased coefficient estimates due to omitted time invariant characteristics [ 31 ]. The general form of the fixed effects model can be expressed in the following equation [ 32 ].

In Eq. ( 2 ), the dependent variable ‘profitability’ is \({P}_{i,t}\) for i-th bank and t -th year. The dependent variable \({P}_{i,t}\) is determined by a set of exogenous regressor that includes both the bank specific and macroeconomic variables, \({X}_{i,t}\) , for i -th bank and t -th year; and \(\beta s\) are model parameters. Beta value in regression is the estimated coefficients of the independent or explanatory variables. It indicates a change in the dependent variable as a result of a unit change in explanatory variables keeping other independent or explanatory variables constant. The unobserved individual bank effect is \({\mu }_{i}\) , and the random error is, \({u}_{i,t}\) .

Unlike the fixed effects model, in the random effects (RE) model, it is assumed that the error term is uncorrelated with the explanatory variables. It allows the time invariant variables to act as similar to the predictors in the model. The benefit of RE is that the inferences can be generalised, beyond the sample drawn in a model [ 31 ]. The general form of the RE model can be expressed in the following equation [ 32 ].

In Eq. ( 3 ), the random error, \({\varepsilon }_{i,t}\) is with in entity error term and \({u}_{i,t}\) is between entity error term. \(\mu\) is the bank specific random effect. Random effect model assumes that the unobservable individual-specific effects (unobserved heterogeneity) are distributed independently of the explanatory variables or independent variables. More clearly, it assumes that the unobserved heterogeneity is uncorrelated with each explanatory variable across in all time period. Then, if the random effect model is significant, it indicates that the unobserved individual (cross-sectional) effects are uncorrelated with all the explanatory variables across all time-period.

The following fixed effects (FE) model has been estimated to analyse the determinants of profitability.

where i  = bank, 1,….0.39, and t  = time, 1,….,15.

In Eq. ( 4 ), the dependent variable is \(\text{ROA}_{i,t}\) . It is determined by a set of exogenous regressors that includes both the bank specific and macroeconomic variables. The unobserved individual bank effect is \({\mu }_{i}\) , and random error is \({u}_{i,t}\) . It is assumed that the set of explanatory variables is uncorrelated with the error term \({u}_{i,t}\) , and the error term is normally distributed, \({u}_{i,t}\) ~ N (0, \({\sigma }_{u}^{2}\) ), where \({\sigma }_{u}^{2}\) is > 0.

We have estimated the following random effect (RE) model to analyse the determinants of profitability in Indian scheduled commercial banks.

The descriptive statistics of the variables that has been used in the estimation of determinants of profitability is presented in Table 2 . The descriptive statistics of both the dependent and explanatory variables for the time period between 2005 and 2019 is presented in the form of mean, standard deviation, minimum and maximum. The results show that the return on profitability (ROA) ranges from − 5.49 to 2.13, with a mean ROA value of 0.65. Similarly, the minimum and maximum values of the explanatory variables range low to high. The mean and standard deviation values of the variables suggest that there is variation between the two.

The correlation matrix with correlation coefficients of the variables used is presented in Table 3 . The results suggest that there is no multicollinearity problem in the data. The results show a negative association of ROA with NNPA and CapT1. The rest of the explanatory variables exhibit positive association with ROA.

We have estimated both the fixed effect (Eq.  4 ) and random effect (Eq.  5 ) models to analyse the determinants of profitability in Indian scheduled commercial banks. The estimation result of the FE model shows that there is an inverse relationship between the rate of profit (ROA) and non-performing loans (NNPA), and the association is statistically significant (Table 4 ). Non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) are found to be positively associated with the rate of profit (ROA). The estimates are found to be statically significant. Ratio of operating cost to interest income (OCTII) shows negative relationship with profitability (ROA). The other macroeconomic variables like rate of inflation and interest rate show negative and positive associations, respectively. However, their association is not statistically significant.

The regression estimates of the RE model also give a similar result (Table 3 ). NPAs and operating cost (OCTII) are negatively associated with the rate of profit (ROA). Their relationship is statistically significant. On the other side, deposit (lnTD), non-interest income (NII), interest income (II), capital adequacy (CAPT1) and GDP growth (GDPGr) exhibit positive association with profitability (ROA). Their association is statistically significant. The other two macroeconomic explanatory variables, the rate of inflation and interest rate exhibit negative and positive associations, respectively. While total deposit was found to be significant in RE, it is found to be insignificant in FE model. In order to arrive at an appropriate test between FE and RE, the Hausman test was conducted. The results of Hausman test suggest that the RE estimate will be appropriate for the sample as the ‘ p ’ value is greater than 0.05 (Table 5 ).

In this paper, we have examined the impact of NPAs on the profitability of Indian banks. Using set of bank specific and macroeconomic variables, we have estimated the determinants of profitability of 39 commercial banks in India. The estimation result suggests that growing incidence of NPA is likely to reduce the profitability of the banks considerably. Results also suggest that increase in operating cost has negative impact on the profitability in Indian banks. The negative association between profitability (ROA) and NPA (NNPA); and profitability (ROA) and operating cost (OCTII) is statistically significant. The results show that there is a positive relationship between profitability (ROA), and interest earning (II) and non-interest earnings (NII). Their association is found to be statistically significant. The results further show that the volume of deposit (lnTD) is positively associated with the profitability (ROA). As financial intermediaries, commercial banks largely relay on interest earnings as their major source of income. In order to boost up their interest earnings, the banks must reduce their NPA volumes. The result suggests that Indian banks must reduce NPAs and operating cost in order to enhance their profitability.

The findings of the empirical estimation are similar to the findings of the studies by Kannan et al. [ 16 ], Sensarma and Ghosh [ 26 ], and Sinha and Sakshi [ 28 ]. A study by Kannan et al. [ 16 ], using data for 86 Indian banks, for the period from 1995–96 to 1999–2000 found that banks with higher NPAs have relatively lower profit margins. A study by Sensarma and Ghosh [ 26 ] of Indian commercial banks, for the period from 1997–98 to 2000–01, reported that a rise in NPA adversely affects the interest margins for banks and hence reduces bank profitability. Similarly, Sinha and Sakshi [ 28 ], in their study of 42 Indian commercial banks for the period from 2000 to 2013, found that higher credit risk, as measured by provision non-performing assets, negatively impacts bank profitability. Analysing NPAs in 46 Indian commercial banks from 2007 to 2014, Bawa et al. [ 5 ] found a negative relationship between NPAs and return on assets.

The paper has empirically estimated the factors that determine the profitability of Indian scheduled commercial banks, in order to understand the relationship between increasing non-performing advances and the rate of profit. The determinants of profitability have been estimated by taking a set of bank specific and macroeconomic explanatory variables. From the panel data estimation of 39 Public Sector and private banks, we found that the increase in non-performing advances has negative impact on the rate of profit. Operating cost is also found to be negatively associated with profitability. The estimates of both the FE and RE model suggest that non-interest income, interest income, capital adequacy and GDP growth rate have positively contributed to the rate of profit of the Indian banks. Given that, banks to a large extent depend on the performance of their loan assets as a critical source of income and profit, the rising NPAs is a cause of concern. It on the one hand reduces their interest earning and on the other side also affects their future deposits and increases their operating cost as the cost of recovery of NPAs will go up. The study suggests that the banks must reduce their NPAs and operating cost to improve their profitability.

Limitation of the study and future research avenues

The findings of the study are based on a sample of banks that mostly covers the PSBs and the private banks, covering the time period from 2005 to 2019. Though data for the year 2020 are available, it could not be incorporated due to recent bank mergers in India. Between 2020 and 2021, several mergers took place within the Public Sector Banks (PSBs). Post-merger, the number of PSBs has declined from 20 to 12. While it would be interesting to include the mergers into the empirical analysis, however one year is a too short time period to make any meaningful conclusion. The effect of merger in the analysis of NAPs and profitability of banks can be studied in future, with the availability of data for a longer time period.

Availability of data and materials

The data that support the findings of this study are collected from public domain resources. It is available at https://dbie.rbi.org.in/DBIE/dbie.rbi?site=publications [RBI publications/database on Indian economy].

Abbreviations

Non-Performing Assets

Gross Domestic Product

Fixed Effects

Random Effects

Reserve Bank of India

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Acknowledgements

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The paper is drawn from a research project “Performance of India’s Banking Sector: A Critical Focus on Non-Performing Advances (NPAs)”, funded by the Indian Council of Social Science Research under ICSSR-MHRD IMPRESS Scheme. The funding body has NO role in designing of the study, analysis, interpretation of the data and in writing. The research paper/study has been designed and prepared by the authors.

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Santosh Kumar Das & Khushboo Uppal

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Both the authors have contributed in completing the research paper/study. The paper was conceptualised by SKD. The structure of the paper was prepared by SKD in consultation with KU. KU largely contributed to the literature section and data collection. Estimation and analysis were done by SKD. Both the authors have contributed to the methodology section. Both the authors have read and approved the final manuscript.

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Das, S.K., Uppal, K. NPAs and profitability in Indian banks: an empirical analysis. Futur Bus J 7 , 53 (2021). https://doi.org/10.1186/s43093-021-00096-3

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DOI : https://doi.org/10.1186/s43093-021-00096-3

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• Bank performance
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Navigating headwinds in the U.S. banking sector

Key takeaways

We believe that U.S. banks have strengthened their balance sheets and improved risk management since the Global Financial Crisis, enhancing their ability to withstand any potential shifts in current economic conditions.

In our opinion, proposed new regulatory requirements—particularly “Basel III Endgame”—should have a credit neutral to slightly positive effect on the banking industry. 

Banks have been tightening their lending requirements, given the fluctuating levels of uncertainty regarding the economic outlook.

Commercial real estate and increasing credit card balances are growing concerns for the U.S. banking sector, though we believe that any losses should be manageable in an otherwise benign credit environment.

This complex, dynamic backdrop illustrates why we maintain rigorous credit selection processes and proactive surveillance systems across our Wasmer Schroeder™ Strategies.

Executive overview

The Global Financial Crisis, the COVID-19 pandemic, the 2023 regional bank failures, commercial real estate issues, and potential asset quality deterioration in consumer lending have highlighted the importance of having a resilient banking system. This article examines the U.S. banking industry and its capacity to withstand potential idiosyncratic shocks, an expected slowdown in U.S. economic growth as a consequence of the Federal Reserve’s series of interest rate hikes, regulatory changes, and asset quality concerns.

Bank capital ratios are up and net loans and leases in relation to core deposits have improved

Regulators and banks took steps to improve stability after the Global Financial Crisis. “Basel III”—a new internationally agreed set of capital regulations—was introduced by the Basel Committee on Banking Supervision with the intention of increasing both the quantity and quality of capital for large banks. At the same time, banks strengthened their balance sheets and improved risk management, reducing operational and reputational risk concerns. As a result, banks must meet increased liquidity requirements and retain larger amounts of better-quality capital.

Exhibit 1: Capital ratios for U.S. bank balance sheets are up

Source: Federal Deposit Insurance Corporation (FDIC). For more information, see: https://www.fdic.gov/ .

Exhibit 2: Net loans and leases to core deposit ratios for U.S. banks have improved

Source: FDIC. For more information, see: https://www.fdic.gov/ .

Loan-to-deposit ratios for the larger banks are well below pre-Global Financial Crisis levels, as noted above. The loan-to-deposit ratio is one measure of a bank’s balance sheet risk, with a higher ratio indicating the bank may be taking on more risk because it has lower cash reserves to cover losses. Banks with a higher ratio may have to rely on wholesale funding to fund lending, which could increase risk in an economic slowdown.

The bottom line: We believe that large U.S. banks are well positioned to withstand idiosyncratic risks in the current economic environment.

Proposed regulation focuses on capital requirements

As we move into 2024, regulation is a major topic for the banks. A number of proposals are expected to be finalized, with a wide range of implications. In addition, if economic growth slows amid the elevated interest rate environment as has been widely expected and the Federal Reserve switches from tight monetary policies to potentially easing interest rates, we believe that analysts’ and investors’ relative areas of focus will shift from deposit and capital considerations to credit quality and revenue streams.

In July of 2023, regulators published the long-awaited capital rules known as “Basel III Endgame.” If finalized, Basel III Endgame would require the most extensive and significant changes to the U.S. capital rules since the implementation of Basel III in 2013. Basel III Endgame substantially revises the capital requirements for banks with total assets of $100 billion ($100B) or more (category I–IV banks), with the largest increase required for the U.S. Global Systematically Important Banks (G-SIBs).

Exhibit 3: Basel III Endgame to raise CET1 requirements

* The Financial Stability Board. As of November 2023, U.S. G-SIBs included: Bank of America Corporation; The Bank of New York Mellon Corporation; Citigroup, N.A.; Goldman Sachs Bank USA; JP Morgan Chase; Morgan Stanley; State Street Corporation; and, Wells Fargo & Company. For more information, see: https://www.fsb.org/2023/11/2023-list-of-global-systemically-important-banks-g-sibs/ . Source: The Federal Reserve. For more information, see: https://www.federalreserve.gov/ .

According to regulatory agencies, the Basel III Endgame proposal would result in an estimated 16% increase in common equity tier 1 (CET1) requirement for Category I to IV banks, as defined in Exhibit 3. This estimate is an average based on past data and the impact on each bank will be different. The proposal’s higher capital requirements aim to push larger banks to manage their capital more effectively, which most have already shifted toward in anticipation of the reforms. In addition, Category III and IV banks will no longer be able to option out of recognizing most elements of additional other comprehensive income (AOCI), which includes unrealized gains and losses from available-for-sale securities. With the sharp increase in rates since the end of 2021, many banks have accumulated relatively large unrealized losses in their available-for-sale securities portfolios. Due to this, several banks would have significantly lower capital ratios if AOCI was included. Given that the new regulations are expected to be phased in over a three-year period and losses on available-for-sale securities seem likely to decline materially by 2028 as securities mature, banks should be able to meet their minimum capital requirements, inclusive of AOCI. We therefore expect that the credit impact of the proposed new regulatory requirements will be neutral to slightly positive, although some of the changes will likely affect how balance sheets are managed, which in turn could introduce a degree of uncertainty.

The bottom line: In our opinion, proposed new regulatory requirements, particularly Basel III Endgame, should have a credit neutral to slightly positive effect on the U.S. banking industry.

Regional bank failures of 2023 were not due to insufficient capital

Based on subsequent review by entities including the Federal Deposit Insurance Corporation and Board of Governors of the Federal Reserve System, the failures of Silicon Valley Bank, First Republic Bank, and Signature Bank in early 2023 were generally considered to reflect a combination poor risk management and a quickly shifting interest rate environment, which resulted in risky funding structures and large unrealized losses. Due to the ease with which deposit withdrawals can now be made through internet banking and the rapid dissemination of information via social media, deposit runs on the banks happened quickly, fueling their failures. Despite these results, new liquidity regulations are not part of the proposed regulatory changes, though enhancements and/or new liquidity requirements seem likely. Recently, the director of the Office of the Comptroller of the Currency (OCC) recommended a 5-day liquidity coverage ratio (LCR) to supplement the existing 30-day Basel III LCR requirement.

The bottom line: The high-profile U.S. bank failures in 2023 were primarily fueled by poor risk management and the quickly shifting interest rate environment, rather than by systemic issues.

Bank senior loan officers tightened lending standards

The October 2023 Senior Loan Officer Opinion Survey 1 (SLOOS)—which assesses changes in the standards and terms of, and demand for, bank loans to businesses and households over the prior three months—revealed tighter lending standards. For the third consecutive quarter, banks tightened lending standards across all loan categories reviewed by the Federal Reserve. In addition, banks reported weaker demand for commercial & industrial, commercial real estate, residential, auto, and other consumer loans. Regarding commercial real estate, a net 68.3% of respondents noted that they had tightened credit standards for non-farm, non-residential properties, which includes office commercial real estate. This is the second-highest tightening score in the SLOOS data, just under the peak during the COVID-19 pandemic.

Exhibit 4: Bank lending standards tightened in 2023

Source: The Federal Reserve. For more information, see: https://www.federalreserve.gov/ .

The most common justification given by banks for tightening lending requirements included a less favorable or more uncertain economic outlook, decrease in risk tolerance, decline in the credit quality of loans and collateral values, and funding cost concerns. In the past, tighter lending standards resulted in higher loan losses in that category, while lower demand resulted in softer loan growth.

The bottom line: U.S. banks have tightened their lending standards amid the more uncertain economic outlook.

Credit performance expected to deteriorate

Credit performance should deteriorate, although not significantly, while commercial real estate and consumer exposure pose growing concerns. As previously stated, most U.S. banks are approaching the shifting environment with materially higher levels of capital and reserves than prior to the Global Financial Crisis. Given these considerations, most banks appear to have entered the current environment with a higher potential capacity to absorb losses.

Exhibit 5: Loss allowances relative to total loan portfolios

The bottom line: Credit performance may deteriorate, although in our opinion, larger banks have the capacity to manage potential losses.

Commercial real estate exposure remains manageable

Although larger banks will likely realize losses due to commercial real estate exposures, we believe that these should be manageable in an otherwise benign credit environment. However, smaller banks, with a greater concentration of commercial real estate, could see additional pressure and a loss in confidence.

Banks are mainly exposed to commercial real estate by:

• Holding nearly half of the approximately $5.8 trillion in outstanding commercial real estate and multifamily debt. 2
• Having roughly $380 billion in commercial mortgage back securities (CMBS) on their balance sheets, though Fannie Mae and Freddie Mac securities account for 84% of this exposure, limiting the credit risk.
• Having indirect exposure to commercial real estate through lending to nonbank financial institutions.

Exhibit 6: Commercial bank total loans

Source: FDIC, data as of Q3 2023. For more information, see: https://www.fdic.gov/ .

Commercial real estate concerns center on office space

The focus on commercial real estate primarily centers on office space, which accounted for approximately $750B of total commercial real estate and approximately $330B of bank commercial real estate. While commercial real estate loans accounted for approximately 24% of total bank loans, as shown in Exhibit 6, office commercial real estate accounted for less than 3%. The focus on office primarily reflects structural changes due to increased hybrid workforce models adopted by many employers amid the COVID-19 pandemic. The “higher-for-longer” interest rate environment has placed additional pressure on borrowers’ ability to refinance loans while valuations deteriorate. As noted by the Mortgage Bankers Association, property sales and mortgage origination volumes were each down over 50% through the first three quarters of 2023 compared to the same period in 2022. As of the fourth quarter of 2023, Moody’s Analytics put the national office vacancy rate at 19.6%, exceeding the previous high of 19.3% in 1991 during the Savings and Loan Crisis.

Community banks more exposed than large banks

U.S. banks with over $100 billion in total assets—the Federal Reserve’s threshold to be considered a large bank—hold almost 34% of the approximately $2.9 trillion in commercial real estate loans held within the U.S. banking industry. These loans represent a significantly lower concentration in terms of total loans when compared to community banks, which have less than $10 billion in total assets.

Exhibit 7: Commercial real estate exposure vs. total loans

Commercial real estate loans account for a much larger percentage of all loans at community banks. Therefore, these smaller banks would likely have a more challenging time absorbing potential losses as commercial real estate exposure may represent well over 250% of their equity capital.

Exhibit 8: Commercial real estate exposure vs. equity capital

The bottom line: Community banks are much more exposed to potential commercial real estate losses than large U.S. banks, based on equity capital holdings.

Potential loan losses for banks

At the same time, total non-accrual commercial real estate and delinquencies greater than 90 days are higher for larger banks compared to the smaller regional and community banks. This is primarily due to the higher number of non-owner-occupied commercial real estate loans at larger banks, which have generally underperformed amid the hybrid workforce models adopted during the COVID-19 pandemic. Smaller banks tend to have a higher percentage of owner-occupied commercial real estate loans on their books and a deeper relationship with borrowers, where the business is often the primary source of repayment.

Exhibit 9: Past-due loans for commercial real estate in perspective

The leverage and property type underlying commercial real estate loans are likely to play critical roles in their valuations. Owners of buildings with substantial equity cushions are less likely to default, as loans with high loan-to-value ratios are typically harder to refinance or modify. Most commercial real estate property values increased in the years leading up to the pandemic, resulting in lower loan-to-values for many mortgages, providing a cushion. Also, differences in mortgages by property type, deal vintage, term, market, and many other factors will affect performance. Moody’s Analytics noted that as of December 1, 2023, the total number of office loans maturing in the subsequent 12 months was just over $15B, of which approximately $12B, or 80%, have current performance characteristics that would make them difficult to refinance. On a positive note, consumer confidence was robust heading into 2024, inflation has generally been heading in the right direction, and interest rate cuts may finally be on the horizon, all of which is welcomed news for commercial real estate and credit in general.

Commercial real estate loan losses will likely be a challenge for banks, though we believe the losses are unlikely to destabilize the broader financial system since banks have been relatively proactive and generally appear to have sufficient capital reserves to weather any related issues. However, commercial real estate loan losses could be problematic for some smaller community banks, potentially leading to more bank failures, illustrating the need for careful due diligence, and underscoring why thoughtful credit research is such an important ingredient for the success of our Wasmer Schroeder Strategies.

The bottom line: We believe that any losses on commercial real estate loans will likely affect community banks more than regional or large banks but seem unlikely to destabilize the broader financial system.

Credit card losses are expected to peak in 2024

Revolving debt continues to grow and growth in credit card lending continues to outpace commercial and other consumer lending. Both credit card limits and credit card balances have resumed growth trajectories following slight downturns during the COVID-19 pandemic. The increase in credit card debt likely reflects an increase in spending, a lower savings rate, and the loosening of lending standards prior to the Federal Reserve’s recent monetary tightening cycle. Credit line utilization—credit card balance divided by credit card limit—has moved higher but remains below pre-pandemic levels. An increase in utilization could potentially reflect cardholders need to draw on their available credit lines and might be an early warning sign of a deterioration in credit performance.

According to the SLOOS, banks reported loosening lending standards in the fourth quarter of 2020 through the first quarter of 2022 and reported tightening lending standards in third quarter of 2022 through the third quarter of 2023. This included most terms on credit card loans.

Exhibit 10: Revolving debt trends in credit limits and balances

Source: Federal Reserve Bank of New York, Consumer Credit Panel. For more information, see: https://www.newyorkfed.org/microeconomics/faq .

Banks and other credit card lenders pointed to a normalization in credit performance across most loan portfolios as of late 2023 and expect losses to peak in 2024. Credit card delinquencies increased during the four quarters ended September 30, 2023. According to Federal Reserve data, 9.43% of credit card balances were 90+ days delinquent in the third quarter 2023. These levels were higher than prior to the pandemic, but lower than historical highs experienced in 2011. Delinquencies and losses have increased faster among portfolios with lower credit quality.

Exhibit 11: 90-plus day delinquencies by loan type

U.S. banking industry considerations for 2024 and beyond

We believe that the U.S. banking industry is relatively well positioned to withstand potential idiosyncratic shocks that may emerge as the U.S. economy transitions from the current backdrop of a restrictive stance on monetary policy to a more accommodative one. Banking industry positioning is particularly improved versus conditions prior to the Global Financial Crisis. Materially higher capital reserve levels and any potential benefits associated with Basel III Endgame requirements should provide support for the broader U.S. banking industry moving forward as it begins to navigate an economy transitioning toward expected slower economic growth.

However, some uncertainty remains. Asset-quality pressure points have emerged for smaller U.S. banks—particularly for regional and community banks—with commercial real estate a growing concern that may fuel losses in an otherwise largely benign credit environment. Peak credit card losses are anticipated in 2024, with banks and other lenders reporting normalization in credit performance across most loan portfolios as of the end of the third quarter of 2023.

With these collective points in mind, we plan to continue maintaining rigorous credit selection processes and proactive surveillance systems to better ensure that any credit-related shifts within the U.S. banking industry are addressed for potential impact within our Wasmer Schroeder Strategies, supporting the potential for better long-term investment outcomes for our clients.

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Research Discussion Paper – RDP 2024-02 Valuing Safety and Privacy in Retail Central Bank Digital Currency

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This paper explores the merits of introducing a retail central bank digital currency (CBDC) in Australia, focusing on the extent to which consumers would value having access to a digital form of money that is even safer and potentially more private than commercial bank deposits. To conduct our exploration we run a discrete choice experiment, which is a technique designed specifically for assessing public valuations of goods without markets. The results suggest that the average consumer attaches no value to the added safety of a CBDC. This is consistent with bank deposits in Australia already being perceived as a safe form of money, and physical cash issued by the Reserve Bank of Australia continuing to be available as an alternative option. Privacy settings of a CBDC, which can take various forms, look more consequential for the CBDC value proposition. We find no clear relationship between safety or privacy valuations and the degree of consumers’ cash use.

https://doi.org/10.47688/rdp2024-02

Analyzing the Impact of Intellectual Capital on the Financial Performance: A Comparative Study of Indian Public and Private Sector Banks

• Published: 11 April 2024

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• Monika Barak   ORCID: orcid.org/0000-0001-6598-3093 1 &
• Rakesh Kumar Sharma 1  

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This paper explores the effect of intellectual capital (I.C) on the financial performance (F.P) of 12 public and 17 private sector banks in India. To get a comprehensive viewpoint, the study will try to answer the research question, i.e., Does the intellectual capital affect the financial performance of the Indian banks with respect to their multidimensionality? We used intellectual capital and financial performance as multidimensional constructs (human capital (H.C), capital employed (C.E), structural capital (S.C), and relational capital (R.C) for intellectual capital and return on assets (ROA), return on equity (ROE), return on capital employed (ROCE), and return on sales (ROS) for financial performance). Data were collected over 12 years, specifically from 2010 to 2022 from each bank. This study employed the modified value-added intellectual coefficient (MVAIC) measure as an alternative to the disputed value-added intellectual capital (VAIC) model to address the shortcomings of the previous research. The present study employed advanced longitudinal cointegration techniques to authenticate and validate the results. The fully modified ordinary least squares (FMOLS) method is employed to assess the effectiveness of the intellectual capital. The results suggest a positive relationship between human capital and all financial metrics, except for ROE, in the context of public sector banks. Further efficiency of capital employed and structural capital positively affects public sector bank financial performance indicators like ROA, ROE, ROCE, and ROS. Private sector banks have a negative correlation between relational capital and ROS whereas it demonstrates a positive association with ROCE. Similarly, there is a negative correlation between relational capital and both ROA and ROE in the case of public sector banks. For instance, the MVAIC model improves all financial performance measures except ROA, especially in private sector banking. The findings will assist executives, government officials, and policymakers in quantifying the efficiency and discerning the essential intellectual elements that enhance their effectiveness. Additionally, these findings will aid in devising strategies to foster and enhance their intellectual capacity.

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The datasets used and analyzed during this study are available upon reasonable request from the corresponding author.

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Barak, M., Sharma, R.K. Analyzing the Impact of Intellectual Capital on the Financial Performance: A Comparative Study of Indian Public and Private Sector Banks. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01901-4

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