Comparative Analysis of FinTech Disruption in the US and UK Banking Sectors

Comparative Analysis of FinTech Disruption in the US and UK Banking Sectors: Quantitative Insights and Regulatory Implications

Abstract

The specific dissertation research is emphasized by outlining the disruption of FinTech and its consequences on the profitability of traditional banks in the US and UK within the period from 2014 to 2023 by conducting an unbalanced panel data analysis. Some of the key financial indicators analysed included ROA, IETR, ROE, ETAR, Revenue Growth, and Bank Size. Results showed UK banks were steady regarding profitability, whereas US banks were more volatile in the various responses of interest expenses and equity management. A bank is stronger, given that the equity therein is larger and better. Limitations include incomplete data and not being able to cover macroeconomic factors.

Keywords: FinTech, traditional banks, US, UK, ROA, financial performance, competition.

Contents

Abstract

1. Introduction
2. Literature Review

2.1 Growth of Fintech in the US

2.2 Fintech Expansion in the UK

2.3 Comparative Regulatory Frameworks and Challenges

2.4 Empirical Research on FinTech's Impact on Bank Profitability

2.5 Gaps in prior research

2.6 Hypothesis Development

3. Data and Methodology

3.1 Data

3.2 Methodology

3.2.1 Econometric Model

4. Empirical Analysis

4.1 Descriptive Statistics

4.2 Unbalanced Panel Data Analysis

5. Conclusion and Limitations

References

Appendix

1.Introduction

This new rise of FinTech is very fast and rapidly brings a change in the face of world finance. Conventional banking methods have been fast displaced by digital banking, blockchain, mobile payment platforms, and artificial intelligence, leaving financial organizations no option but to adapt to changed circumstances or become victims of obsolescence. The US and UK banking sectors are cases in point where FinTech firms have significantly challenged traditional banks, giving faster, cheaper, and more customer-centric financial services (Arner, Barberis, & Buckley, 2017).

The US and UK are leading global financial centres and have taken different trajectories in terms of FinTech innovation and adoption. In the United States, both the enormous size and heterogeneous structure of the banking industry have encouraged a prolific and competitive environment for FinTech companies (Zavolokina, Schlegel, & Schwabe, 2016). The rise of companies like Square, Stripe, and SoFi indicates the significant progress made by FinTech in the fields of consumer banking, payment processing, and lending services.

On the other hand, the more centralized regulation structure in the UK has, with the support of the FCA to business innovations such as Open Banking for example, enabled a fertile landscape for the growth of Fintech companies such as Monzo, Revolut, and Starling Bank (Zhang, Yue, & Huang, 2019). They were able to experience steep growth within this innovative yet strongly regulated sector.

Despite the remarkable development of FinTech in both the US and the UK, there are considerable differences between how these two regions create innovations and regulate the banking sector. While the US regulatory framework is fragmented with many overlapping agencies, this generally leads to confusion and slower regulatory responses toward FinTech innovation (Kaal, 2020). In contrast, the more well-organized regulatory environment in the UK contributes to defining a much more effective and future-oriented regulation style (Zhang et al., 2019). Elaborated differences raise major questions as to how a regulatory environment enacted by the state conditions innovation in FinTech and its impact.

Despite the respective success of FinTech in the US and the UK, there are several very sharp contrasts in how each of these markets attempts to approach innovation and regulation within the financial services industry. Namely, the US regulation is more fragmented, with multiple agencies, many with similar duties and responsibilities that foster a great deal of ambiguity and a more delayed regulatory response to most FinTech innovations. Similarly, the more integrated regulatory environment in the UK has, so far, enabled faster and more proactive steps on regulation. These divergences raise critical questions with regard to the role of regulatory environments shaping both the growth and impact of FinTech.

The findings of this research will have implications on policy, regulation, and banking within the US and the UK. Components in regulation that facilitate or impede the development of FinTech are elaborated herein to offer critical insights for policymaking that balances innovation and financial stability. This study also gives strategic guidance on how traditional banks can follow suit with the disruptive factors brought forth by FinTech in order to compete in the ever-growing digitized world in financial services.

2.Literature Review

The landscape of financial services has been reshaped in recent years due to the rapid emergence of Financial Technology, popularly referred to as FinTech. FinTech companies provide creative financial services, which then become alternatives to the conventional banking systems. This has been especially the case with the big economies of the world, like the United States and the United Kingdom, where various banking aspects have been shattered by Financial Technology in such things as lending, payments, and wealth management. This rise of Financial Technology has thus become the subject of maximum academic and industry attention, resulting in several research works on this very theme of understanding its impacts on the standard banking model. According to Philippon (2016) and Zalan & Toufaily (2017), The paper reviews the extant literature on the current state of FinTech in the US and UK, subsequently discussing various regulatory challenges and defects of prior research.

2.1 Growth of Fintech in the US

New technologies and rapid changes in customer needs have enabled the rapid growth of US FinTech over the years. The US Fintech market has been valued at approximately $7.3 trillion in 2021 and it keeps on growing at a very rapid pace (Statista, 2021). New digital payment solutions, blockchain, and AI have enabled these companies to offer their services quickly and with less cost compared to traditional banking. Firms like Square, PayPal, and Stripe have become household names through their fintech services, ranging from peer-to-peer payments to loan underwriting and business financing (Mills & McCarthy, 2017).

A major area of disruption takes place in the business of lending, primarily through the popularity that P2P lending websites have garnered, like LendingClub and SoFi. In fact, according to researchers, P2P lending in America has increased from $26.16 billion during 2015 to a whopping $100 billion by 2021, a number set to grow further as customers become increasingly dependent on online platforms for their borrowing needs (Buchak et al., 2018). Traditional banks, especially small regional ones, have found it difficult to match the convenience and speed of making available loan approvals by FinTech companies (Fuster et al., 2019). The impact on profitability is apparent: from less than 5% in 2013, FinTech lenders have grabbed close to 36% of the US unsecured personal loan market by 2020 according to TransUnion (2020).

Growth of Financial Technology faced many challenges in the United States of America. There is a highly fragmented regulatory regime in the US that creates big obstructions with regard to operation by the Fintech companies. For instance, while in the United Kingdom regulators have made attempts to provide the regulators of those companies with a more consolidated environment, in the US, OCC, SEC, and the Federal Reserve share that responsibility. The fragmentation often leads to divergent policies and delays in the authorization of new FinTech products (Navaretti et al., 2017). In addition, this regulatory patchwork creates options for arbitrage in regulation, which allows FinTech firms to exploit loopholes or operate in jurisdictions with less burdensome oversight (Kaal, 2020).

2.2 Fintech Expansion in the UK

According to Innovate Finance in 2021 estimated that the UK currently ranked as one of the leading countries in global FinTech and one which produced over 11 billion in revenues, with more than 76,500 people employed in this sector. Even more precisely London has become the centre of FinTech, getting much venture capital investment from both UK and international investors. From 2017 to 2021, the FinTech sector in the United Kingdom received over 37 billion in funding, the most among all competing markets in Europe combined (Zhang et al., 2019). Companies such as Monzo, Revolut, and Starling Bank have been significant drivers, offering digital-focused banking options that appeal to technology-savvy consumers (Milne & Parboteeah, 2016).

One of the key drivers behind the rise of FinTech is said to be the passing of the Open Banking policy by the UK government in 2018. Open Banking compels incumbent banks to share customer data with third parties, and at the same time allows FinTechs to supply specialized financial services to customers for whom they now have access to financial history. This encouraged competition within the financial services industry and allowed FinTech companies to enjoy larger shares of the market than traditional banks, especially in personal finance management and payment services. As Zhang et al. (2019) puts it, "over 4 million customers used Open Banking enhanced services" in 2021 alone; this should be evidence enough to say that the country is leading by the rate of adoption of FinTech solutions.

Consequently, this elicited evidence of UK Fintech companies having sculpted an impressive performance record as far as the digital payment industry is concerned. According to Gomber et al., 2017, this boosted a particular industry such that "UK Fintech companies process more than 700m daily, using digital wallets and contactless solutions on the rise." Besides, there are fully internet banks like Monzo and Starling which rapidly expand by charging very small commissions and offering much better, more flexible service, compared to more conservative, old-fashioned high street banks. McWaters (2016) says that they can get into underserved market niches, including Millennials who prefer digital banking to traditional, interpersonal banking services.

All the same, no matter how much success FinTech may have managed in the UK, it shows some issues regarding data privacy and security, at least. Following Cur 2019, increased customer data sharing in the Open Banking system brought about some risks regarding cybersecurity and data leakage. The FCA undoubtedly was trying hard to ensure that its data is securely protected, but due to the very nature of the growing FinTech industry, the regulator consistently must respond to new challenges.

2.3 Comparative Regulatory Frameworks and Challenges

Other areas where FinTech has seen huge growth and development include regulatory environments in the US and the UK. The UK, for one, has a highly centralized regulatory framework since the FCA and PRA, its regulators, were very proactive in promoting innovation in Fintech. FCA introduced the Regulatory Sandbox back in 2016 as a safe space for FinTech startups to test their various products and services in a no-regulation environment. For instance, Gomber et al. (2017) observe that the sandbox has been credited with allowing the rapid growth of FinTech firms in areas such as digital payments and lending in the UK.

Contrary to that, the regulatory framework in the US is fragmented, placing the various aspects of financial services under the control of various regulatory agencies. This may create a fragmented situation for the FinTech firm amidst uncertainty, especially when federal and state legislations conflict with each other. For example, the OCC has attempted to establish a special-purpose national bank charter for FinTech companies, a move resisted by state regulators through the filing of legal challenges that delay the process.

Empirical investigations show the regulatory environment among the essential factors shaping FinTech development. Of these, the work of Philippon (2016) has underlined that the nations with more flexible and adaptive regulatory frameworks-like the UK-are characterized by fast adoptions of FinTech and bank competition. About this, Zhang et al. (2019) mention the fact that the UK centralized approach has so far allowed the entry into the market of FinTech firms at higher and speedier rates compared with the US, where regulatory fragmentation constrains innovation.

2.4 Empirical Research on FinTech's Impact on Bank Profitability

A number of empirical studies have examined the impact of FinTech on traditional bank profitability. For instance, in the US alone, FinTech firms have taken a large chunk from consumer lending, and numerous studies have shown that digital lenders grant faster loan processing and lower interest rates than traditional banks. For example, a study by Buchak et al. (2018) evidence that FinTech lenders in the US attained 36% market share in the unsecured personal loan market between 2013 and 2020. Their study also revealed another important fact that, through superior data analytics and machine learning methods in determining credit risks more accurately than traditional banks, the FinTech firms reduced the rate of defaults.

Equally in the UK, such FinTech growth has impacted traditional banks with regard to the winning of new customers and operational efficiencies. Milne and Parboteeah (2016) believe that traditional UK banks have been slow to embrace digital technologies and are losing market share to innovative fully digital banks like Monzo and Starling. Their research indicated that digital banks could operate at a cost as much as 30% lower than their traditional counterparts, thus allowing them to offer more competitive rates and fees.

Besides, McWaters (2016) claims that FinTech companies have been highly successful in reaching those underserved portions of the market, including millennials and low-income households, for whom traditional banking has not been able to cater efficiently. This has further eroded the market share of incumbent banks, particularly in sectors like consumer finance and payments.

2.5 Gaps in prior research

Despite the existence of some research works on the subject matter, there are still quite a few gaps in the literature on FinTech disruption. Most studies have, therefore, focused on short-run outcomes such as market share and profitability without taking into account the long-term implications FinTech has for financial stability. While FinTech companies have undoubtedly taken market share and have been dramatically successful, the sustainability of that growth and profitability is little understood, particularly in the environment of increased regulatory scrutiny and competition from more traditional banks just commencing to embrace FinTech solutions. Literature also does not indicate how the FinTech firm would deal with an economic downturn or changes in the interest rate environment that would impact its ability to make sustainable profits.

Empirical research on how FinTech influences the core operations of traditional banks, other than in terms of their profitability and market share, is needed. Despite a few relevant studies on how FinTech relates to times of loan approval and customer satisfaction, most of the empirical studies related to the impact of FinTech on core internal bank processes, including risk management, compliance, and human resources, remain scant (Zhang et al., 2019). Gaining an understanding of such operational changes is a necessary prelude to any evaluation of the wider impact FinTech might have on the banking industry and how traditional banks are able to adapt to the new digital landscape.

Third, it would have to aim at more general attempts at comparison, in the analytical direction of what different regulatory frameworks produce upon FinTech growth. Although much of the literature has focused on either US or UK regulatory environments separately, what remains missing from the literature is a systematic comparison of how specific policies have influenced FinTech firm development over time. In fact, this area has been viewed by the policymakers as an area of study which is very important in providing a certain balance on one hand to innovation with the need for financial stability and consumer protection on the other.

2.6 Hypothesis Development

The section below provides a theoretical background for the independent variables used in the analysis, coupled with the empirical evidence from past research on bank performance and financial disruption, especially on FinTech. Based on these works, hypotheses are identified for their relationship with the dependent variable ROA.

1. Interest Expenses to Total Assets Ratio

It refers to the portion of a banks total assets utilized by interest payments on borrowed funds. It is very important for the evaluation of a bank's cost efficiency, especially in those competitive financial markets in which any company must minimize costs to keep profitability. Mester (1996) analysed the interest expenses and bank performance and reported that higher interest expenses usually reduce profitability as it increases the cost burden for banks. Similarly, Altunbas, Liu, Molyneux and Seth 2000 highlighted that higher interest expenses especially tends to deface banks operating in a highly competitive market, which may be the result of FinTech perturbation by their lower prices.

1. Return on Equity (ROE)

ROE is perhaps one of the most recognized measures of firm performance, as it essentially shows how well the bank has been able to generate profit from owners' equity. In this vein, a higher ROE would therefore show that the bank is effectively using its equity capital to drive profitability. Flamini et al. (2009) documented the positive effect of ROE on financial performance as related to developing economies whereby ROE sends out very strong signals on the efficient management of the capital base. This is within the context that Demirg-Kunt and Huizinga (1999) showed how higher ROE was seen to be associated with higher profitability, although this is highly seen to happen for highly competitive markets.

• Equity-to-Assets Ratio

This is the ratio of a bank's total assets funded by the shareholders' equity, instead of through debt. Typically, the higher the ratio, the more significant is the financial strength and correspondingly lower leverage of the bank. It can be expected to decrease insolvency risk when an economy faces stressful conditions. For example, Kosmidou et al. (2007) took into consideration equity-to-assets ratio impacting the efficiency level of banks in Greece. According to the authors, the higher the ratios, the better the performance level of banks related to the increased market instability.

1. Revenue Growth

It is the ability of the bank to increase income over time and, therefore, is a particularly good indicator of the market extension when circumstances have changed. Consequently, in the study of "Determinants of Bank Profitability in Switzerland," Dietrich and Wanzenried (2011) ascertained that revenue growth related positively with higher profitability since the latter provided banks with greater reinvestment opportunities in new technologies and enhancing operation efficiency. Likewise, Athanasoglou et al. (2008) proved that under competitive markets, revenue growth is positively related with bank profitability.

1. Bank Size

The size of the bank can be said to play a significant role in determining the competitive positions of banks, given that larger banks are more likely to enjoy economies of scale and better market power due to their diversified resources. As indeed documented by Pasiouras and Kosmidou (2007), larger banks tend to outperform small ones, especially in those contexts where their size can enable them to absorb costs with less strain and translate that into higher market share. Along the same line, another important confirmation of bank size positively relating to financial performance-especially when markets are innovative and competitive-was given by Demirg-Kunt and Maksimovic (1998).

Kosmidou et al. (2007) offset this with the case that even larger banks can suffer from bureaucratic inefficiency which would therefore cut off certain economies of scale. In principle, larger banks should outperform their smaller peers and thus make investments in new technologies necessary in order to meet the challenges of FinTech competition.

Based on the literature and the discussion of independent variables, the following hypotheses are developed:

H0: The independent variables have no statistically significant impact on Return on Assets (ROA) of traditional banks in the US and UK banking sectors under FinTech disruption.

H1: The independent variables have a statistically significant impact on Return on Assets (ROA) of traditional banks in the US and UK banking sectors under FinTech disruption.

3. Data and Methodology

3.1 Data

This study uses WRDS Compustat Global data for UK firms and WRDS Compustat IQ data for US firms. The period ranges from 2014 to 2023 because these ten years are the most recent and relevant in the context of FinTech disruption of both UK and US banking sectors. The period chosen encapsulates the sudden emergence of FinTech companies and their effect on traditional banks; hence, it presents a timely context.

Based on this, the dataset for this study will have 855 observations for UK-based firms and 3,399 observations for US-based firms. Such observations will be based on financial data that pertain to total assets, revenues, interest expenses, and return on assets, among others. The GICS code has filtered the bank and financial corporations to make sure that the material being sampled is only those firms that are relevant for the study of FinTech disruption within traditional banking sectors. GICS is known for its exhaustive framework in classifying a firm into an industry classification system. It will be of great importance to the research to have the following specific banking and financial firms included in the study, inasmuch as they stand to represent core traditional financial institutions that have directly suffered the most impact from FinTech innovations. These listed firms are of particular interest to give insight into established institution adaptation in cases of technological disruption. This will further go ahead and provide a comparison analysis in terms of banking sectors in both countries.

Inclusion of firms into analysis has been restricted to those for which complete information is available, because missing information can distort the results. Such an effect is expected particularly in regression analysis since this kind of method requires complete data sets to yield valid statistical inferences. Thus, it is that only those firms could be included in the final sample, in whose case complete financial information was available in respect of the variables required.

STATA, which is the statistical software is used to sort and analyse the data and, in general, is widely used in many econometric analyses. Given the handling of large datasets and running of panel data regressions, STATA is ideal for this kind of analysis.

3.2 Methodology

The methodology section covers the econometric model, variables involved, and estimation techniques employed in this study. Here, the independent variables of analysis are displayed in their mathematical format. Each one of these will test a different aspect of the response of banks in their financial performance to FinTech disruption, relevant to the discussion presented above.

1. Interest Expense to Total Assets Ratio =

This ratio expresses or measures a bank's ability to manage its interest-related costs, especially in a competitive market. According to Molyneux and Thornton (1992), the higher the interest expense ratio, the lower is the profitability. Since banks compete with FinTech, which is a low-cost intermediary, this ratio and management of costs are material for assessing profitability.

1. Return on Equity (ROE) =

ROE is a test of a bank's efficiency in using its equity to derive returns, something quite relevant in competitive environments influenced by FinTech. Demirg-Kunt and Huizinga (1999) found ROE to be a good determinant of bank performance and hence applies in this study to measure the adaptability level of banks against FinTech disruption in sustaining profitability.

• Equity-to-Assets Ratio =

This is the ratio that determines reliance on equity as opposed to debt financing. The higher this ratio, the better the bank's stability. Kosmidou et al. (2005) cited that the higher equity over assets ratio is positively related to better performance during periods of financial uncertainty; hence, the relevance towards how banks navigate through the challenges posed by FinTech firms.

1. Revenue Growth =

This variable tests the ability of a bank to grow and expand its operations and become competitive in alternative financial services developed by FinTech firms. According to the studies done by Dietrich and Wanzenried (2011) revenue growth positively affects bank profitability, especially in competitive markets.

1. Bank Size = log (Total Assets)

Larger banks may enjoy superior resources to accommodate FinTech disruption. Pasiouras and Kosmidou (2007) documented that larger banks outperform their smaller peers because they are better positioned to exploit economies of scale; size is thus an essential control variable for this study.

3.2.1 Econometric Model

The unbalanced panel data regression model is, therefore, used in this study to analyse how bank profitability-which in this case is measured by ROA-relates to the independent variables across time. Panel data controls for both cross-sectional and time-series variations and is ideal in analysing banks across the US and UK throughout the study period from 2014 to 2023.

In this paper, two panel models were considered: Fixed Effects and Random Effects. In the FE model, time-invariant factors relevant for each firm are controlled for, while in the RE model, it is assumed that the individual effects are uncorrelated with the independent variables.

A Hausman test was performed where the fixed and random effects models were compared to choose between them. The option sigmamore was used to take care of potential inconsistencies in the covariance matrix of the differences in coefficients.

However, based on the Hausman test result, chi-square is 329.69 and the p-value is 0.0000, thus the null hypothesis can be rejected, and fixed effects model is more appropriate in this analysis.

The fixed-effect model controls for unobserved heterogeneity across firms that could affect the relationship between independent variables and the dependent variable. This is particularly true in the financial services industry, in that firm-specific factors such as management style, risk appetite, and strategic focus may considerably affect performance.

The final regression model is as follows:

Return on Assets (ROA) = ? + ?1(IETR) + ?2(ROE) + ?3(ETAR) + ?4(Revenue Growth) + ?5(Bank Size) + ?

Where:

• ROA is the dependent variable from 2014 to 2023.
• ? is the constant term.
• ?₁?₂?₃?₄?₅ are the coefficients of the independent variables.
• ? is the error term.

4. Empirical Analysis

4.1 Descriptive Statistics

Table 1 Overall Descriptive Statistics

Table 1 represents the summary of descriptive statistics of the US and UK banking industry from 2014 to 2023. These descriptive statistics bring forth some interesting differences in the financial performance and characteristics between UK and US banking sector, hence conveying important signals concerning how these institutions manage profitability, leverage, cost efficiency, and growth in a FinTech disruption setting. UK banks are relatively stable in ROA, with an average of 0.025 and median of 0.021, all indicating that the profits from asset realization for most banks in the sample are modest. The profitability realized is at a moderate variance; the standard deviation of 0.179 could be attributed to differences in the strategic choice of the firms, cost management, or varying market conditions. For the US banks, however, the picture is far more erratic, with the mean ROA standing at -0.174 and the median at 0.013. The far larger SD of 3.685 then underlines that there is extreme variability in profitability for US firms, whereas several banks experienced intense losses, evidenced by the minimum ROA of -135.023. Other possible reasons for this extreme volatility in the profitability of US banks could be increased competition due to the emergence of FinTech firms, constraining effects of macroeconomic conditions, or simply differing risk management practices across the industry.

Consequently, one of the reasons for differences in profitability may be the way firms handle interest-related expenses represented by an Interest Expense to Total Assets Ratio (IETR), showing for UK banks an average value as low as 0.009 compared to that of US banks, coming at 0.033. In general, with the larger spread of values, the UK has an SD of 0.014; hence, the UK banks are relatively more efficient on average in managing their interest expenses in relation to their assets. Perhaps this could be as a result of conditions of lending being more favourable or perhaps access to lower-priced sources of funds which may be a result of less aggressive risk-taking behaviour. Within the US, the higher standard deviation of 0.348 shows wide variability; some banks carry interest costs way out of proportion to their assets. This perhaps indicates higher dependence on external borrowings, and some institutions have taken more leverage to have some life in the disrupted financial landscape. This disruption from FinTech, by often lowering the cost of transactions and lending for consumers, may have put additional pressure on traditional US banks to rely more on debt, thereby artificially inflating their interest expenses.

Still another measure of profitability for banks is ROE, which describes the efficiency of exploiting equity in bringing in profits. UK banks have an average Return on Equity of 0.169, substantially higher compared to the average ROE of 0.067 for US banks. The high SD for UK and US banks of 2.197 and 3.761, respectively, denote an intra-region equity utilization strategy whereby a number of banks realize considerably higher returns while others are underperformers. Such variation, however, is more in the US context, whereby ROE figures are reported to be abnormally negative for some banks, as low as -79.769, which means those banking institutions fail to generate profits from their base of equity. This might point out more aggressive risk-taking or strategic errors for US banking, perhaps driven by challenges imposed by FinTech competition. By contrast, UK banks, although under the same pressure due to FinTech, appear to maintain equity utilization in a more stable and efficient manner, which could be part of the reason for their higher average ROE. This can be either a source of evidence of more cautious risk management strategies or of a more enabling regulatory environment for traditional banking operations in the UK.

The difference in financial structures is further extended by the equity-to-assets ratio. Whereas UK banks are highly reliant on equity financing, with the average ETAR equalling 0.511, the same figure for US banks constitutes only 0.182 on average. The fact that the equity base is higher in the United Kingdom may also indicate that banks there are more prudent in their financial management since they are less leveraged and hence less exposed to the risks associated with excessive borrowings. This is further supported by the relatively low SD for UK banks of 0.365, indicating that the majority of institutions follow similar structures of capital by being more focused on equity rather than debt to finance their assets. By contrast, US banks reveal a very heterogeneous approach to leverage: The 1.817 SD means very large variability in the equity-to-assets ratio. This means that some US banks go aggressive leverage position by raising their vulnerability to financial fragility, especially within the financial market where FinTech players continue to force down the margins and thereby force the traditional banks to innovate rapidly.

Revenue growth could be considered indicative of how prepared the banks are for changes in the markets and for the rise of FinTech companies. From the statistics, there was a huge difference between the two regions. UK banks reported an average revenue growth of 9.109%, while US banks only recorded an average of 0.427%. As reflected by its SD of 257.067, UK has high variability across responses that, although certain banks reap huge growth in the UK, some may be doing just about okay or recording its minimum revenue growth figure of -187.804%. It might be due to how different banks react individually to the pressures created by the digital transformation and regulatory shift. Some UK banks might now have become successful at implanting new technologies and improved customer offerings, which have seen them increase their respective market shares, while others have not been quite so successful and thus suffered revenue declines. The US means and SD for revenue growth are far lower, indicating that the banking sector revenue growth in the US is consistent but modest. It could be a result of increased competition, including from within the US banking fraternity, but also from the FinTech challenge in holding them back from realizing the full revenue growth potential. This may also suggest a more mature market where, without significant innovation, further opportunities for growth will be tougher to find.

Finally, Bank size is measured as the log of total assets, which shows that US banks are generally larger since the mean of the log of total assets for US Banks, 8.081, is greater than that of UK banks, 6.651. For this reason, maybe US banks would have economies of scale, which could enable them to spread their costs over greater assets and invest more heavily in technologies and innovations. However, the higher SD of UK banks at 3.257 would suggest that there is greater variability in the size of UK banks, with smaller, perhaps regional, banks operating alongside larger, more national players. This could have an impact on the diverging ways in which different banks respond to FinTech disruption-that is, smaller banks lack full resources and capabilities to compete both against FinTech firms and larger incumbents, while larger banks are in a better position to invest in digital transformation. The more homogeneous bank size in the US, represented by an SD of 2.704, points toward a more homogenous sector where large, well-capitalized banks feature. Maybe this is why certain US banks happen to stay above the challenges posed by FinTech disruption, while others fail to remain profitable, especially those smaller or less efficient.

These descriptive statistics therefore underscore a direction of divergence between the UK and US banking sectors on profitability, cost efficiency, and financial structure. Besides regulatory considerations, many other aspects-market competitiveness, the degree of disruption at the hands of FinTech firms to traditional models of banking-may constitute reasons for these divergences in how banks manage their resources and respond to market pressures.

Table 2 Correlation Matrix UK

Table 3 Correlation Matrix US

In the UK and the US, ROA is inversely related to IETR. However, the degree of association is larger in the US at -0.3677 compared to that of the UK, estimated at -0.1787. This means that US banks endure larger negative influences on profitability due to higher interest expenses, possibly reflecting their capital structure features. In addition, both regions have ROA and ROE which are negatively correlated, but again, it is stronger in the US than in the UK with -0.2427 and -0.1617, respectively, which indicates US banks fail to make return on equity. This could reflect a greater reliance on equity in US banks to manage risks. The interesting thing is that Bank Size is more positively correlated with ROA in the US at 0.1916 than in the UK at 0.0840, showing that larger US banks are better at converting their size into profitability compared to their UK counterparts. These matrices above show relationships of profitability and leverage-related variables to be stronger in the US, highlighting greater volatility and reliance on financial structure.

Table 4 Variance Inflation Factor

UK mean VIF = 1.27

US mean VIF = 1.37

As it can be seen, both the UK and US have low multicollinearity because the highest values are considerably less than 10. It is also possible to see that the UK has a higher VIF in respect to ETAR and Bank Size with 1.65 and 1.45 correspondingly, hence these two variables can be said to have a rather modest correlation with the rest of the independent variables. The US also has a higher VIF for IETR at 1.91 and ETAR at 1.90, indicating that in the US, interest expenses are closer to equity with the other variables. The average VIFs are fairly consistent with each other, being 1.37 in the US, marginally higher than that in the UK at 1.27, reflecting modest overall variation in overall multicollinearity.

4.2 Unbalanced Panel Data Analysis

Table 5

Interesting differences and similarities, according to various factors, are carried out in the analysis of unbalanced panel data with respect to the UK and US banking sectors in how they affect profitability measured by Return on Assets. These regressions allow for the determination of the influence undertaken by such variables as they provide a relative measure of how traditional banks in the two countries operate within an environment shaped by the ever-changing competition from FinTech firms.

Taking the IETR first, it would appear that the IETR and ROA have a negative association in both UK and US banks. More precisely, the higher the interest expenses, the lower profitability is likely to be. In the US, however, this is much more substantial at a coefficient of -0.34 (p = 0.00) than for the UK, which is an insignificant -0.40 at the 5 percent level, as indicated by a p value of 0.59. This large negative impact in the US therefore suggests that US banks are more sensitive to these borrowing costs, probably because they depend more on debt financing or face worse conditions in providing credit. This is in line with the findings of Demirg-Kunt and Huizinga (1999), who observed that higher interest margins compensate banks in countries where competition or deregulation is more developed, and where bank profitability may be impaired. In the UK, the insignificant relationship may indicate that interest expenses are less of a determining factor towards bank performance, either due to more stable conditions of borrowing or conservative financial management strategies.

The ROE is negatively related to the ROA in both countries, having coefficients of -0.02 in the UK and -0.20 in the US, both statistically significant. This suggests an inverse relationship, which means that higher returns on equity do not always translate into higher profitability. This is especially so in the US, whose magnitude of effect is larger. This could be explained by the fact that US banks, in their desire to have a higher ROE, may be going for riskier investments or strategies that will reduce their profitability. Molyneux and Thornton documented this trend during 1992, wherein banks operating in highly competitive markets, like that of the US, often accept greater risks to sustain high returns. However, it boomerangs in terms of overall profitability. While this suggests there is some sort of trade-off between ROE and profitability in the UK, the negative coefficient is smaller than that of the US, which could indicate that UK banks have been balancing the two more effectively, possibly due to regulatory environments that encourage better risk management habits.

ETAR has a positive and highly significant relationship with ROA in both the UK (0.33, p = 0.00) and the US (0.30, p = 0.00). This means that banks with relatively high equity in proportion to their assets are more profitable, which again goes to strengthen the hypothesis that a sound equity base acts as a cushion in case of financial turmoil and leads to greater financial stability. This finding agrees with that of Kosmidou et al. (2007), who found that banks with higher equity ratios usually outperform others in turbulent market conditions since equity financing dampens the reliance on costly debt and lowers risks associated with high leverage. In both countries, the significantly positive influence of ETAR on profitability confirms the hypothesis that only banks with high-structured capital would continue to be profitable due to the pressures of competitive strains generated by FinTech companies, since they would have more financial leeway to invest in innovation or market disruption.

However, the growth in revenues does not significantly associate with ROA in the UK, since the p-value is 0.77, and also in the US, at 0.67. This insignificance could suggest that country-level variation in revenue growth is not a strong predictor of bank profitability across these regions. This could be an indication that, in a mature banking sector-like those in the UK and the US-profitability is more strongly linked to cost management and operational efficiency than revenue growth. Athanasoglou et al. (2008) underline that in competitive and saturated markets, growth in revenues is not always transformed into higher profitability, either when costs cannot be kept under control or when new revenue sources bear higher risks. Another reason for which this study may also find that the revenue growth is nonsignificant can be explained by the fact that traditional banks increasingly face the challenge of converting additional revenues into profits, with leaner and more effective competition from FinTech firms.

Bank size comes out to be positively significant towards determining profitability in both UK and US markets, having coefficients 0.04 (p = 0.00) and 0.29 (p = 0.00), respectively. This is because larger banks enjoy economies of scale whereby fixed costs are dissipated over a much greater asset base and can, therefore, afford high investments in technology and innovation. This also agrees with the findings by Buchak et al. (2018), that larger financial institutions are better placed to compete with FinTech firms because they can gain from economies of scale, as well as adopt new technologies with ease. This could mean that generally, there might be some connection between size and profitability, but for the US, this relationship is considerably strong. It is for this reason that it may be surmised that larger US banks have been notably adept at using size to overcome disruptions in competitiveness and profitability caused by FinTech. In the UK, while size indeed positively influences profitability, its effect is smaller, probably because the banks in this country are more medium-size and may not enjoy the same advantages that come with scale. The values of R-squared also indicate a difference in the explanatory capability of the models. In the UK, it is 0.13, indicating that only 13% of ROA variation is explained by independent variables. However, in the US model, R-square is 0.39, hence showing far stronger explanatory capability, with the model explaining 39% of variation in ROA. This would, therefore, imply that the selected independent variables are stronger in their explanatory power within the US context, where factors such as interest expenses, equity structure, and bank size appear to be playing a much larger role in the determination of profitability. This reflects potential structural differences between the two banking sectors in the form of regulatory environments, competitive dynamics, or something else such as the degree of FinTech disruption; all these might fall harder on US banks.

The F-tests for both countries are highly significant with a p-value of 0.00. This means both models are statistically valid, as their independent variables have been of significance in explaining a part of the variation in profitability. The magnitude of the F-test value was still higher in the case of the US-341.34, than in that of the UK-21.35, further consolidating the fact that this model fits better in explaining the profitability of the US banking sector.

Summarising the unbalanced panel data for the UK and US, however, provides evidence that while there are several common factors impacting profitability across the two economies - such as the positive impact of equity ratios and bank size - a number of important differences remain. While US banks seem more responsive to borrowing costs and the associated trade-offs of high ROE, UK banks are better positioned in terms of interest expenses and resultant impacts on profitability due to ROE. The differences that emerge could be a consequence of different regulatory environments, market sentiments, and levels of FinTech competitors for each jurisdiction. The figures give an overview of how traditional banks are faring in the UK and US in their efforts to keep pace with a rapidly changing financial world.

5. Conclusion and Limitations

This study discussed how FinTech disruption influenced traditional banks in the US and UK by considering several financial indicators and their relations to profitability, as represented by Return on Assets, or ROA. Surprisingly enough, striking differences were found to be at variance between these two regions. UK banks were likely to be more profitable in a stable manner, being less sensitive to interest expenses with higher efficiency of using equity. US banks, in turn, have shown higher volatility of profitability, sensitivity to interest expenses, and variability in financial structure. Larger banks in both regions and the ones with higher equity-to-assets ratios have shown more resiliency, underpinning the importance of financial stability in facing challenges competitively posed by FinTech. These findings consequently support previous research that focuses on equity, bank size, and control of overheads/costs as the principal drivers of profitability in very competitive financial markets.

The findings have considerable implications for the study of the adaptation of traditional banks in a rapidly changing financial environment; nevertheless, several limitations are present in this study. First, throughout the period of the study ranging from 2014 to 2023, data for all companies is not completely available. More precisely, in some of these years, a certain number of firms did not report data, and this influences the efficiency of panel data analysis. Besides, data quality and comparability on small and less established banks could further cause biased results since large ones have more complete and reliable financial reporting. Further, the fact that it focused only on banks that fall within specific GICS codes excludes small institutions or new entrants emanating from the FinTech sector, hence limiting the generalizability of the findings.

Another limitation is that some factors, which would impact bank performance and interest rate volatility, changes in regulations, and global economic conditions were not controlled for in the analysis. Thus, these factors might influence accordingly and hit hard on profitability and competitive balance between traditional banking organizations and FinTech firms. Further research overcomes such limitations, using finer and longer granularities of data for longer times, along with other external determinants that can be useful for further detailed investigation of how traditional banks could prosper in this context of technological disruption.

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Appendix

Unbalanced Panel Data Analysis, Random Effect Model

Hausman Test