Impact of Corporate Governance and ERM on Bank Risk: A US

Abstract

Banks play an integral role in the economic growth of nations. However, it is necessary to take into account the modern-day economic shocks while considering the operation and profitability of banks. The United States and the United Kingdom are two such nations that are highly integrated into the global markets but also have substantially unique banking regulations. The establishment of a dynamic corporate governance framework and ERM methodology is critical for banks. In order to compare the impact of ERM and corporate governance framework on the risk of banks, while controlling for economic factors, this paper uses a GMM methodology. A Driscoll–Kraay fixed-effect estimation was also added as an additional robustness check. Data for 10 banks with a market capitalisation greater than 5 million USD has been considered from both nations between 2019 and 2023. The results of the estimation show that governance factors and risk management factors impact the NPL but not the ROA. However, in the UK, both NPL and ROA are impacted. This shows the non-binding risk in USA whereas the core-binding risk in the UK markets. Finally, macroeconomic factors have a significant impact on the risk of banks across both nations. Based on the research, the binding versus non-binding risk distinction identified here offers a conceptual framework for evaluating governance and ERM effectiveness in other banking systems.

Keywords

enterprise risk management corporate governance GDP growth interest rate GMM model

1 Introduction

1.1 Background and the Importance of Banks

The role of banks in the global economy is immense. As per Demirgüç-Kunt et al. (2013, p. 490), the banks have a growing importance in determining the economic activities within a nation. Banks have key credentials in supplying credit within an economy and help substantially towards the circular flow of money. As a result, it has a great impact on the economic and financial development of a nation. Despite banks being a critical institution for an economy, the banking sector is also prone to economic shocks. As per the research by Challoumis and Eriotis (2024, p. 1005), the presence of a global economic shock leads to uncertainty in the banking system. This happens as the interest rates are altered to accommodate the shocks to the economy. However, this leads to a rise in the servicing rates for consumers impacting the loans and advances proceeded by the banks to the economy. Moreover, the rise in such shocks also creates a risk towards the dissolution of consumers, impacting the retrieval of loans for banks, thus creating a risky business environment for the institutions. As a result, in the modern day, there has been an immense need for restructuring the corporate governance of banks and setting up stable enterprise risk management (ERM) systems for banks.

The setting up of a robust ERM system is an essential parameter for banks in the modern economy. As per Lundqvist and Vilhelmsson (2018, p. 148), the presence of ERM has a negative impact of −0.019 points, on credit default swaps. This allows ERM to select projects with the most favourable risk–return trade-offs and protect associated cash flows for the banks. This means the ERM would identify and assess risks and reduce the number of risky transactions made by the bank. This framework of ERM would ensure that the integrity of providing loans is maintained by the bank and hence, it would reduce the probability of credit defaults. Thus, this shows that ERM is an important facilitator for reducing baking-related risks in the present economy. However, establishing a dynamic ERM system is also backed by a high level of corporate governance factor. A study by Maruhun et al. (2018), has revealed that the structure and size of the board are major parameters towards the establishment of a robust ERM system. This is because having sustainable corporate governance would set clear policies for the operations of the company. As a result, it would lead to the development of a robust ERM procedure.

1.2 Comparison Between USA and UK

Given the background of the research, it is necessary to also analyse the impacts on the United States of America and the United Kingdom. As per the European Central Bank (2010), the USA and the UK have substantially different regulatory benchmarks with regard to banking operations. Moreover, the nations also have unique macroeconomic conditions. In the United States, the banks have a heavy emphasis on investment avenues (Wang, 2022). This influx of investment banks, makes the industry more dynamic in the nation and allows high economic growth. However, on the other hand, the UK has a stringent cost of capital regulation set by the regulatory authority for banks (Benetton, 2021). These factors show that there is a substantial difference in the banking framework for both countries. As a result, it is important to analyse the difference and compare the impact of corporate governance and ERM on the banks of both nations.

1.3 Research Aim

The objective of the research is to analyse whether there is any difference across the banking structures for both countries. The paper also aims to find if the corporate governance factors and the ERM factors lead to reduced banking risk in both countries. Finally, the research would also control for macroeconomic factors and find the impact on banking risk.

1.4 Research Proposition

The primary proposition for the research is to find that if the corporate Governance and enterprise risk management factors would impact the banking risks across the United States and the United Kingdom. This would be done while controlling for economic variables.

1.5 Summary of Paper

The paper first discusses the background on the importance of banks, ERM and Corporate Governance factors. Moreover, the background also provides the importance of why it is important to analyse the impact between the USA and the UK. The paper then analyses the literature on Corporate Governance and ERM in banks. Furthermore, the paper also analyses the impact of macroeconomic changes on banking risks. The paper then proposes an empirical methodology using a GMM model in the Methodology chapter. The results find that corporate governance and ERM impact banking risk in the UK. The economic factors impact both nations. The results are discussed in the sixth chapter and the conclusion and future direction are provided in the seventh chapter.

2 Literature

2.1 Impact of Corporate Governance and ERM in Banks

The corporate governance and the ERM of banks have a substantial impact on their operations. A paper by Aebi et al. (2012, p. 12), has revealed that the presence of a risk committee has a 0.307 percentage point negative impact on the buy-and-hold returns for stocks. Furthermore, the instance of the Chief Risk Officer Reporting to the board has a positive impact of 0.241 percentage points on the buy-and-hold returns for stocks (Aebi et al., 2012, p. 12). This conclusion has been drawn from analysing the data of 573 banks in the United States from the COMPUSTAT bank database (Aebi et al., 2012, p. 3). The results show that the reporting of the Chief Risk Officer to the board has benefitted towards the returns of stock for the banks in the United States. This is because the presence of a chief risk officer under the corporate governance guidelines ensures better Enterprise Risk Management within the banks. This leads to a significant positive impact on the returns. However, it has also been deduced that the presence of the risk committee has a −0.307 percentage impact on returns. This is mostly because the period of the study was during the 2008 Global Financial Crisis when a majority of the banks in the United States faced a negative stock return. Banks like JP Morgan, Bank of America, Wells Fargo and Citi Group had negative returns approximately of −0.401, −0.455, −0.371 and −0.498 respectively as stock returns due to volatility (Batten et al., 2023, p. 38). The presence of the ERM framework also ensures that there is long-term stability within the operations of a bank. As per the study by Al-Khadash et al. (2017), a study conducted in Jordan has revealed that internal environment management factors have a 3.94 per cent high impact on the operations of banks. Furthermore, the objective setting and event identification also have a substantial impact of 3.92 points and 3.83 points respectively. Among these specifications, the impact of controlling activities has the highest mean at 4.11 showing the importance of a control committee towards enterprise risk. Having such a control committee would ensure that the investments and advances made by the banks are within their institutional capability and would not impact the operations of the bank negatively over time. As a result, this would ensure that the long-term sustainability of banks is maintained through a regulated ERM. However, the impact of the risk management framework does not always hold. As per Rahadian and Permana (2021, p. 44), the NPL has a negative and insignificant impact on the CAR of banks.

However, Monahan (2008), also explain how the different risk-governance structures impact the risk-taking behaviours of the banks and also their financial returns. Monahan (2008), also reveal that risk drivers and controls are important parameters which are used to balance the outcomes. The risk factors are mostly inherent, whereas the control factors are external interventions. Hence, different banks would have different structural risks and the control interventions would also be unique for each one. Hence, this would lead to a varied impact of of risk committee of banks and CRO on the risk associated.

2.2 Impact of ERM on the Banking Risks

Enterprise Risk Management (ERM) is an important parameter towards the maintenance of the banking risk held by financial institutions. Liem (2018, p. 9), recorded a 0.3746 percentage point impact towards the ROAA of banks in Indonesia. This study used a Random Effects GLS Regression to conclude the results. The presence of an ERM framework enables to identification of structural risks that banks might face. As a result, the ERM could set regulatory actions to reduce the risk levels for the organisation. As a result, it would help banks to optimise their capital allocation efficiency and reduce the related risks. The risk disclosure of banks also has an important effect on the bank risks. Herawaty et al. (2022), used a t-test to analyse the impact of ERM and NPL on financial distress. The results revealed that ERM has a negative impact of −5.1175 on financial risk. This is because the setting of an ERM framework allows the banks to provide loans more cautiously. As a result of this, the probability of having credit defaulters falls for banks. Therefore, there is a subsequent reduction in the financial risks. This literature also indicates that NPL is a robust parameter for analysing banking risks. On the other hand, a study by Obiedallah and Abdelaziz (2024), revealed that an increase in CAR leads to a 0.1006 percentage point impact on financial performance. This is because banks with higher CAR indicate a better risk-absorption capacity. This would help banks to mitigate the losses received through credit defaults. Thereby, the same would also lead to a reduction in banking risks. This shows that CAR is an efficient parameter to determine ERM effectiveness in banks.

2.3 Impact of Macroeconomic Changes on Banking Risks

The macroeconomic factors of a country have a substantial impact on the profitability and risks associated to banks. The study by Luft and Omarkhil (2018), has revealed that macroeconomic factors such as GDP and Inflationary factors impact the long-term ROA for both Islamic banks as well as convent banks in Pakistan. This analysis included Quarterly time series data for ten banks in Pakistan between 2007 and 2015. In the short run, the interest rates also impact the ROA of a majority of the banks significantly (Luft & Omarkhil, 2018). From another study by Ahmed et al. (2021), it has been understood that for emerging nations, the interest rate has a 0.287 percentage point impact on the Non-performing loans(NPL) of a bank. This high sensitivity of NPLs to the interest rate indicates that banks face risk from macroeconomic changes. This happens as the high interest rates lead to higher borrowing costs, leading to a fall in the capability of the borrower to service their debts. As a result, it leads to a higher risk of defaults and increases banking risks. Hunjra et al. (2021) using a two-step system dynamic panel data regression, concluded in the study that GDP Growth has a negative impact of 0.617 percentage points on the bank risk. Furthermore, inflation has a 0.138 percentage point positive impact on the bank risk in Asian banks. The growth in the economy improves the solvency of borrowers, thereby improving the bank risk. Furthermore, inflation can again impact the interest rates, which would reduce the ability of the borrowers to service their debts. As a result, this increases the associated bank risks. Exchange rates also have a negative impact of −0.001719 per cent on the market risks of commercial banks in Vietnam (Huy et al., 2021). In order to determine the results, data during a low-inflationary period of 2015 to 2020 was considered. The valuation of foreign assets and liabilities for banks changes with a fluctuation in the exchange rates. This leads to destabilisation in the cost of foreign debt servicing and enhances the risks faced by the banks.

2.4 Banking Regulations and Corporate Governance in the United Kingdom

Corporate governance in the financial sector of the United Kingdom was developed to improve the financial regulations and ensure a safe financial system. As per Alexander (2003, p. 995), the Financial Services Authority (now known as the Financial Conduct Authority and the Prudential Regulation Authority) played an active role in the designing of internal control systems and risk-management practices of various banks in the UK. This ensured that the functioning of the banks was optimally done, and a significant level of protection was provided to the shareholders, creditors, customers, and the broader economy. As per Alexander (2006), the duties of the directors involved the maximisation of the wealth of the shareholders. This would reduce the principal-agent problems and would ensure that the operations of the various banks within the UK are done diligently and would not affect the financial welfare of the various stakeholders associated with the banks. In the modern era, the Financial Conduct Authority has divided the code into 5 unique segments. The leadership board and the company purpose, the division of responsibilities, composition, succession and evaluation, audit risk and Internal Control and Remuneration (Financial Reporting Council, 2024). The division of these sections would provide a clear framework and focus on the organisational structure of the UK. Moreover, it would ensure that each division has individual leaders looking after it. As a result, this would make the banking sector more sustainable in the United Kingdom. The onset of the COVID-19 pandemic also brought changes with respect to baking regulations and corporate governance in the United Kingdom. The research by Sivaprasad and Mathew (2021), has concluded that banks followed a more flexible structure towards payment days in order to accommodate consumers against defaulting. This shows that having strong governance and regulatory authority would ensure that the welfare of the consumers is maintained. This would also reduce the risks associated with banking in society, especially during economic shocks. Furthermore, banks in the UK like HSBC provided work from home for 85 per cent of the employees during the aftermath of the pandemic (Sivaprasad & Mathew, 2021, p. 14). This shows that having strong employee relations during economic shocks helps protect the welfare of the employees which would eventually help in extending the sustainability of the banks.

2.5 Banking Regulations and Corporate Governance in the United States

The banking regulations in the United States have also made substantial amendments with respect to corporate governance. As per the Federal Deposit Insurance Corporation (2025), the effective governance framework within banks helps to maintain profitability and competitiveness for banks within the US economy. This ensures that the banks are resilient enough to navigate through the changes within the macroeconomy. The study by Shakil et al. (2021), concluded that clauses with respect to improving gender diversity were a key change. This amendment included that at least one female member should be there on the board of the firm. This would bring a broader perspective to the board of the banks and would help in more efficient decision-making. The banks in the United States have also worked on their ESG Ratings as a result of improving their sustainability. As per Ersoy et al. (2022), the improvement in the ESG Ratings has improved the bank market value under the non-linear scale. This means that improving components regarding environmental factors, governance factors and societal factors would lead to greater contribution towards the stakeholders. Such a factor would make the banks more sustainable in the United States and would reduce operational risks. As a result, the banking risks would be moderated under such circumstances as well. The presence of CEO Duality and Financial Expertise has proved towards a higher level of capital Ratio in the banks within the US (Gilani et al., 2021, p. 40). Under such corporate governance structures, when the CEO also serves as the chair of the board, there is greater control over the strategic decisions of the banks. As a result, there is quicker decision-making towards capital strategies which eventually works towards maintaining the stability of the bank. Furthermore, having greater financial expertise would also work towards the benefit of the bank and help mitigate operational risks. Having a moderate capital ratio is important towards managing the risks related to the banks. Golbabaei and Botshekan (2022, p. 4), has revealed that increasing capital in US banks increased the capital ratio by 1.56 per cent. However, the rise in weighted risk led to a fall in capital ratio by 0.61 percentage points. This means that having a greater capital ratio in the US improves the capacity of banks to handle potential losses. As a result, there is improved financial stability.

2.6 Research Gap

The studies conducted by Aebi et al. (2012) specifically focus on the impact of ERM on the banks based in the United States, as they use a total of COMPUSTAT bank data from the US. Moreover, other studies like Luft and Omarkhil (2018), and Huy et al. (2021) analyse the impact of macroeconomic factors on the risk level of banks in Asia. Furthermore, papers like Sivaprasad and Mathew (2021) firmly analyse the impact of corporate governance on the banking sector of the UK. Whereas, papers like Golbabaei and Botshekan (2022) analyse the impact of corporate governance and risk in the United States. Given these studies, it could be understood that there is a substantial gap in the comparative literature between the United States and the United Kingdom as both economies are highly integrated into the global supply chain, and are prone to greater macroeconomic shocks. As a result, this paper aims to fill the research gap by providing a comparative study between the United States of America and the United Kingdom. Moreover, it is also worth noting that the coefficients cited from prior studies represent conditional estimates from models including covariates, and are reported here for context rather than as direct bivariate relationships.

2.7 The Hypothesis of the Research

On the basis of the literature the following research hypotheses could be drawn:

a.
Corporate Governance, ERM and Bank Risk
H₀: Board Duality and CAR have no effect on the NPL and the ROA of the Banks

H₁: Board Duality and CAR have substantial impact on the NPL and the ROA of the Banks

b.
Macroeconomic Indicators and Bank Risk
H₀: Interest Rate and economic growth does not impact the NPL or ROA of the banks

H₁: Interest Rate and economic growth impacts the NPL or ROA of the banks

3 Methodology

3.1 Design and Empirical Methodology

This research paper uses a Generalised Method of Moments (GMM) methodology to analyse the impact of Corporate Governance and Enterprise Risk Management on banking risks while controlling for macroeconomic factors. The usage of the GMM Model helps to assess the impact of explanatory variables on outcome variables especially with the presence of multiple instrument variables. This has been further shown in the literature by Pham et al. (2021). The GMM models provide a comprehensive outline of the instruments used and their validity. The Sargan test of the GMM model is a core indicator for the same. On the other hand, the GMM Model also checks for heteroscedasticity within the model and the viability of using the model. The summary statistics are first provided as a part of the empirical methodology. The GMM Models as per the specifications in part 3.3 is then presented and then the post-estimation of Sargan and Hansen test is also used to analyse the robustness of the models.

As an additional robustness check parameter, the study also uses a Driscoll–Kraay methodology. As per the study by Driscoll and Kraay (1998), the usage of such an estimator would address problems regarding heteroscedasticity, autocorrelation and cross-sectional dependence across the panel data structure. As a result, this additional methodology is also adopted by the paper in order to ensure that the findings of the paper are verified. This methodology of Discroll-Kraay is further considered, as the data used in the study contains a small-T and small-N values under the panel structure (Kiviet et al., 2017). Hence, this additional methodology would help to address the limitations faced by system GMM model.

3.2 Data and Variables

The study used data from 10 banks in the United States of America and the United Kingdom over a period of 5 years (2019–2023). One of the key estimation parameters used to ensure a dynamic model is the size of the company within the sample. In order to do the same, banks that have a market capitalisation above 5 million USD have been considered only. This ensures the study focusing on the 10 largest banks across both the USA and UK. This would make sure that a comparison across the state-based factors could be done for banks of both the nations, as the top banks are only being considered, therefore showing homogeneity. Moreover, data between 2019 and 2023 was substantially chosen as it spanned across the COVID-19 induced financial crisis. This would ensure the study capture the shocks created by the pandemic as well as the recovery that was gathered without any structural breaks (Karavias et al., 2023). The variables and the sources are mentioned in Tables 1 to 5.

Independent Variable	Parameter	Abbreviation	Source
Loan-to-Deposit Ratio	Risk Management Indicator	LDR	Capital IQ (2025)
Trading Assets/Total Assets	Scale Indicator	TA	Capital IQ (2025)

Table 1.
Indicators and Proxies Used for Empirical Analysis.

Variable Parameter Abbreviation Source

Dependent Variable

Company Return Return of Assets ROA Capital IQ (2025)

Risk Parameter Non-Performing Loans NPL Capital IQ (2025)

Independent Variable

Corporate Governance Duality of Board DUAL Capital IQ (2025)

Enterprise Risk Management Capital-Adequacy Ratio CAR Capital IQ (2025)

Control Variables

Policy Rates Interest Rate IRR UK: FRED (2025a)

USA: FRED (2025b)

Economic Growth GDP Growth Rate GDPGR World Bank (2025)

Variable	Parameter	Abbreviation	Source
Dependent Variable
Company Return	Return of Assets	ROA	Capital IQ (2025)
Risk Parameter	Non-Performing Loans	NPL	Capital IQ (2025)
Independent Variable
Corporate Governance	Duality of Board	DUAL	Capital IQ (2025)
Enterprise Risk Management	Capital-Adequacy Ratio	CAR	Capital IQ (2025)
Control Variables
Policy Rates	Interest Rate	IRR	UK: FRED (2025a)
USA: FRED (2025b)
Economic Growth	GDP Growth Rate	GDPGR	World Bank (2025)

Table 2.

Summary of the Dependent and Independent Factors for the Banks in the United States.

Variable	ROA	NPL	DUAL	CAR	IRR	GDPGR
OBS	50	50	50	50	50	50
AVG	0.00974	0.01284	0.7	0.1434	1.864	2.38
SD	0.003416	0.019296	0.46291	0.015308	1.776647	2.678257
MIN	−0.003	0.002	0	0.119	0.08	−2.2
MAX	0.016	0.1	1	0.177	5.02	6.1

OBS = Number of Observations, AVG = Mean Value, SD = Standard Deviation, MIN = Minimum Value, MAX = Maximum Value.

Table 3.

GMM Estimation on ROA and NPL of Corporate Governance, ERM and Macroeconomic Factors, (*) Significant at 90%, (**) Significant at 95%, (***) Significant at 99%.

	Estimate		z-statistic		p-value		Sig.
	ROA	NPL	ROA	NPL	ROA	NPL	ROA	NPL
Lag 1	0.5870	0.6406	2.4300	5.2500	0.0150	0.0000	**	***
DUAL	0.0002	−0.0005	0.1100	−0.0800	0.9150	0.9370	-	-
CAR	−0.0062	0.2716	−0.1200	1.1000	0.9010	0.2730	-	-
IRR	−0.0118	0.0104	−5.4400	1.7500	0.0000	0.0800	***	*
IRR_SQ	0.0017	−0.0016	4.8100	−1.6200	0.0000	0.1060	***	-
GDPGR	−0.0006	0.0007	−6.5100	1.9200	0.0000	0.0550	***	*
Const.	0.0176	−0.0472	2.5300	−1.2100	0.0120	0.2250	**	-

Table 4.

Diagnostic Using Arellano-Bond Estimates and Sargan Test.

		Value		Probability
Diagnostic Test	Stat.	(1)	(2)	(1)	(2)
Arellano-Bond test for AR(1)	z	−1.51	−1.89	0.132	0.058
Arellano-Bond test for AR(2)	z	1.15	0.96	0.249	0.339
Sargan Test	chi2(2)	1.18	0.66	0.555	0.882
Hansen Test	chi2(2)	2.9	6.47	0.235	0.091

Table 5.

Estimates of Driscoll–Kraay Regression at FE Lag (1) for USA.

	Estimate		t-statistic		p-value		Sig.
	ROA	NPL	ROA	NPL	ROA	NPL	ROA	NPL
LAG 1	−0.48095	−0.44759	−2.51	−1.58	0.087	0.213	**	-
CAR	−1.02837	6.232278	−1.44	2.79	0.245	0.069	-	*
TARatio	−0.00164	−0.00484	−0.98	−6.85	0.401	0.006	-	***
LDR	−0.00028	0.002677	−1.03	2.93	0.377	0.061	-	*
CARxLDR	0.230355	−1.54585	1.39	−2.79	0.258	0.068	-	*
IRR	0.029036	−0.12055	2.08	−2.99	0.129	0.058	-	*
IRR_SQ	−0.00457	0.018485	−2.12	2.99	0.124	0.058	-	*
GDPGR	0.002968	−0.0111	2.56	−3.1	0.083	0.053	*	*
GDPGR_SQ	0.001863	−0.00715	2.12	−2.87	0.124	0.064	-	*

Additional variables were also used for the robustness check by the Driscoll–Kraay corrections. The independent variables are:

Under this model, the ERM denotes a bank-wide process to identify, measure, and manage risks in pursuit of strategic objectives (Monahan, 2008). Given the rarity of ERM in public data, capital adequacy (CAR) is used as a proxy for ERM. Moreover, for robustness, funding mix (LDR) and trading intensity (Trading Assets/Total Assets) to capture liquidity and market-risk management. The other regressors used in the paper are also theory-driven. DUAL captures board power concentration that can weaken or strengthen oversight (Alexander, 2006). CAR reflects ERM effectiveness through loss-absorption capacity (Obiedallah & Abdelaziz, 2024). IRR and GDPGR capture macro-financial conditions known to affect bank asset quality and margins as noted by Luft and Omarkhil (2018) and Ahmed et al. (2021). Moreover, Squared terms allow for nonlinear exposure and LDR for funding mix, and TA ratio for trading intensity are added as robustness indicators.

3.3 Empirical Model

To analyse the research hypotheses, two distinctive models have been considered in the paper. One uses ROA to gauge the impact on company returns, while the other model uses banking risk parameters using NPL.

Equation 1: Returns to the Company

\begin{aligned} (1) & (R O A)_{i t} = β_{1} + β_{2} (L a g . R O A_{i t}) + β_{3} (D U A L_{i t}) + β_{4} (C A R_{i t}) + β_{5} (I R R_{i t}) + β_{6} (I R R . S q_{i t}) + β_{7} (G D P G R_{i t}) + ε_{i t} \end{aligned}

(1)

Equation 2: Banking Risk

\begin{aligned} (2) & (N P L)_{i t} = β_{1} + β_{2} (L a g . N P L_{i t}) + β_{3} (D U A L_{i t}) + β_{4} (C A R_{i t}) + β_{5} (I R R_{i t}) + β_{6} (I R R . S q_{i t}) + β_{7} (G D P G R_{i t}) + ε_{i t} \end{aligned}

(2)

Both the models are classified and used for the United States of America and the United Kingdom separately, and the results are provided in the Empirical Analysis chapter. STATA 14.0 software has been used as a tool to analyse the models.

Within the system-GMM specification, a lagged approach for both the NPL and ROA are considered as internal instruments which eventually isolate the within-bank evolution of defaults or ROA. Moreover, the results of the Arellano–Bond AR(1) and AR(2) tests in Table 3 and Table 6 indicates that there is no second-order serial correlation in the differenced residuals, validating the moment conditions.

Table 6.

Summary of the Dependent and Independent Factors for the Banks in the United Kingdom.

Variable	ROA	NPL	DUAL	CAR	IRR	GDPGR
OBS	47	48	50	47	50	50
AVG	0.0092	0.0268	0.3000	0.1927	1.7140	1.0000
SD	0.0099	0.0131	0.4629	0.0280	1.3760	6.4006
MIN	−0.0010	0.0100	0.0000	0.1400	0.3700	−10.3000
MAX	0.0380	0.0580	1.0000	0.2570	4.0500	8.6000

OBS = Number of Observations, AVG = Mean Value, SD = Standard Deviation, MIN = Minimum Value, MAX = Maximum Value.

Given the additional specification test, the Driscoll–Kraay corrections account for any remaining cross-sectional or serial correlation in the transformed errors. The model specification for the Discroll-Kraay Estimation is as follows:

Equation 3: Returns to the Company

\begin{aligned} (R O A)_{i t} & = β_{1} + β_{2} (R O A_{i, t - 1}) + β_{3} (C A R_{i t}) + β_{4} l n (T A_{i t}) + β_{5} (L T D_{i t}) + β_{6} (C A R_{i t} \times ln {(L T R)}_{i t}) + β_{7} (I R R_{i t}) + β_{8} (I R {R^{2}}_{i t}) \\ (3) & + β_{9} (G D P G R_{i t}) + β_{10} (G D P G {R^{2}}_{i t}) + ε_{i t} \end{aligned}

(3)

Equation 4: Banking Risk

\begin{aligned} (N P L)_{i t} & = β_{1} + β_{2} (N P L_{i, t - 1}) + β_{3} (C A R_{i t}) + β_{4} l n (T A_{i t}) + β_{5} (L T D_{i t}) + β_{6} (C A R_{i t} \times ln {(L T R)}_{i t}) + β_{7} (I R R_{i t}) + β_{8} (I R {R^{2}}_{i t}) \\ (4) & + β_{9} (G D P G R_{i t}) + β_{10} (G D P G {R^{2}}_{i t}) + ε_{i t} \end{aligned}

(4)

Here, both the models are further analysed for the United States and the UK.

Given the small T and potential cross-sectional dependence, the Driscoll–Kraay fixed-effects estimator is considered as the more robust specification. The same is highlighted in the paper by Beylik et al. (2022), who highlight the short time dimension of panel data could create cross-sectional dependence. Hence, the Driscoll–Kraay fixed-effects was used as the more robust specification. Moreover, as per Wooldridge (2001), the GMM Estimation is not likelihood-based. As a result, it cannot gauge for BIC values which are likelihood based. As a result, this also show that Driscoll–Kraay fixed-effects can be used as an additional and robust estimator for the paper.

4 Comparative Analysis Between the United States and the United Kingdom

4.1 Impact on the United States of America

A preliminary statistic for the variables used in the study for the United States is shown in Table 2.

Statistics from Table 2 reveal an average value of 0.00974 for the ROA in the United States, whereas the mean for NPL is 0.01284. There is also substantial duality in the banks of the United States, where 70 per cent have a dual managerial role under corporate governance. The CAR has an arithmetic mean of 0.1434. For the economic parameters, the average IRR in the USA is 1.864 and the average GDPFR is 2.38 per cent.

The predictive modelling is revealed in Table 3.

The table shows that the Lagged value of ROA and NPL both impact the outcome variables significantly. The estimate for ROA is classified as 0.5870 and the same for NPL is classified as 0.6406. The governance parameters like DUAL and CAR have no significant impact on the risk estimates. DUAL has a 0.0002 percentage impact on ROA and a −0.0005 percentage impact on the NPL in the US. CAR has a −0.0062 percentage impact on the ROA and a 0.2716 percentage impact on NPL. The economic parameters like IRR have a −0.0118 magnitude impact on ROA and are statistically viable. The IRR also leads to a considerable relative change in NPL 0.0104 percentage. The quadratic term of IRR impacts the ROA notably at 0.0017 percentage point. Finally, the GDPGR negatively impacts ROA at a −0.006 percentage point. The GDPGR also creates a relative change in the NPL at 0.0007 percentage points.

Diagnostic tests using Arellano–Bond and Sargan estimators is provided in Table 4.

The test in Table 4 shows that there is no autocorrelation problem under the AB Test statistic for Lags 1 and Lags 2. The Sargan Test Statistic show estimates above 0.05. Hence, the variable parameters are valid. The Hansen test also shows the validity of the instruments. For both Model (1) and Model (2), AB values are above 0.05. Similar post-estimation results are found in p-values for Model (1) and Model (2).

The estimates of the Driscoll–Kraay regression analysis are as follows

The estimates of the Driscoll–Kraay regression show that TA Ratio is the only estimate which is statistically significant at 95 per cent CI on NPL. The lag of ROA is also significant at the position. However, the majority of the estimators impacting ROA in USA is not significant. There are a number of estimates which are significant at 90 per cent CI for NPL estimates in USA.

4.2 Impact on the United Kingdom

Table 6 shows the insights that the average for ROA is 0.0092 in the UK, and the same for NPL is 0.0268. 30 per cent of the dataset shows the presence of DUAL, whereas the rest of the 70 per cent of the firm-year data do not show the presence of DUAL on board. The mean CAR for the firm-year data is 0.1927 in the British economy. For macroeconomic factors, IRR has a mean statistic of 1.7140 between 2019 and 2023. The GDPGR has a mean statistic of 1.0000 percentage points.

The lag of ROA impacts the ROA by 0.8884 percentage points. The lag of NPL is not significant but impacts the NPL by 0.0987 percentage points. DUAL has a negative impact of −0.0079 on NPL. The impact of DUAL on ROA is 0.0018, but not statistically significant. CAR impacts the NPL negatively by −0.2384 percentage points. The impact of CAR on ROA is 0.0070, but not significant. IRR_SQ has a 0.0047 percentage impact on ROA and a 0.0071 percentage impact on NPL. IRR has a −0.0383 point impact on ROA and a −0.0332 point impact on NPL. GDPGR has a −0.0007 percentage impact on ROA and 0.0006 percentage points impact on NPL.

The test statistics for diagnostic test for the Arellano–Bond and Sargan test is given below.

The Arellano-Bond estimates in Table 4 reveal that the model (2) that uses NPL is robust and does not overfit. The Arellano-Bond estimate in AR (1) and AR (2) is above 0.05 for Model 2. Moreover, the Sargan test is also above 0.05 in Model (2) and the Hansen test is also above 0.05. This eliminates problems with model fit using Model (2).

The estimates for the Driscoll–Kraay (DK) Regression are as follows:

The results show that a majority of the variables in ROA are significant at 95 per cent CI and the majority of the variables under NPL for UK are significant under the 90 per cent CI.

5 Comparative Analysis Results

The results reveal that there is a substantial contrast between the United States and the United Kingdom. In the United States, governance and ERM variables influence risk but not profitability. However, in the United Kingdom, the same variables affect both NPL and ROA. This indicates that in the United States, risk is being managed, but the same is not binding on bank margins. On the other hand, in the UK, risk management is core-binding, directly shaping asset quality and profitability.

Analysing and comparing the results for the USA and the UK, it has been revealed that the mean ROA is greater in the United States compared to Britain. On the contrary, the NPL in the United Kingdom is comparatively greater than that in the States. The ROA in the USA is 0.97 per cent, whereas for the UK it has been recorded as 0.92 per cent. The NPL in the States is 1.284 per cent on average, whereas in UK, it has an average of 2.68 per cent (Table 2 and Table 6). In the United States, there is far more duality (DUAL) among the board at 70 per cent. For the United Kingdom, the duality (DUAL) is just 30 per cent (Table 2 and Table 6). Concerning the economic factors, the IRR rate in the US is higher than that in the UK. The policy rate in the US is 1.86 per cent, whereas in the UK it is 1.71 per cent. The economic growth (GDPGR) is also higher in America. This compares to 2.67 per cent in the USA and 1 per cent in the UK during the entire timeline of the study (Table 2 and Table 6). Analysing and comparing the results of the GMM Regression analysis, it has been found that DUAL and CAR do not have a substantial impact on the estimates of ROA or NPL in the United States (Table 3). Here, the size of the effect on ROA is 0.0002 per cent for DUAL and −0.0062 per cent for CAR. The impact of DUAL on NPL is −0.0005 and for CAR it is 0.2716. The macroeconomic variables like the IRR and GDPGR have significant effects on the ROA and NPL in the USA. IRR has a negative impact of 0.0118 on ROA and 0.0104 on NPL. GDPGR has a −0.0006 percentage point impact on ROA and a 0.0007 percentage impact on NPL (Table 3). The results in the United Kingdom are significantly different from that in the United States of America (Table 7). Here, DUAL and CAR both have a significant impact on the NPL. Duality (DUAL) causes a −0.0079 percent change in NPL and a 0.0018 percent change in ROA. The estimates for ROA are not significant. Adequacy Ratio (CAR) also impacts the ROA by 0.007 and NPL by −0.0079. The estimate indicator for ROA is again not significant here. The IRR leads to a negative impact of −0.0383 on ROA and a −0.0332 per cent impact on NPL. Both can be accepted here in the scenario. The GDPGR has created a negative ROA of −0.0007 and a positive impact of 0.0006 on NPL. Given the post-estimation statistic, the ROA criteria for Britain is not viable as the Sargan Test rejects the conditionality of the instruments (Table 8). Therefore, it is feasible to analyse the impact of corporate governance parameters and macroeconomic factors on the NPL for both nations. Comparing the results, it can be said that corporate governance factors such as DUAL have no significant impact on NPL for banks in the United States. However, in the United Kingdom, DUAL reduces NPL significantly. The ERM estimates of CAR also do not impact risk factors like NPL in the USA, but the results are significant for the UK. Finally, macroeconomic factors like IRR creates a negative impact on NPL in the United States but the effect in the United Kingdom is positive. Finally, the GDPGR impacts the NPL of both America and UK positively.

Table 7.
GMM Estimation on ROA and NPL of Corporate Governance, ERM and Macroeconomic Factors, () Significant at 90%, () Significant at 95%, () Significant at 99%.

Estimate z-statistic p-value Sig.

ROA NPL ROA NPL ROA NPL ROA NPL

Lag 1 0.8884 0.0987 2.2200 0.4500 0.0260 0.6500 -

DUAL 0.0018 −0.0079 0.5400 −2.8000 0.5900 0.0050 - *

CAR 0.0070 −0.2384 0.2500 −2.8000 0.8060 0.0050 - *

IRR −0.0383 −0.0332 −2.2200 −2.9600 0.0270 0.0030 *

IRR_SQ 0.0047 0.0071 2.2900 2.7700 0.0220 0.0060 *

GDPGR −0.0007 0.0006 −2.2400 1.6800 0.0250 0.0940 *

Const. 0.0780 0.0958 2.4600 4.0400 0.0140 0.0000 *

	Estimate	z-statistic	p-value	Sig.
Lag 1	0.8884	0.0987	2.2200	0.4500	0.0260	0.6500	**	-
DUAL	0.0018	−0.0079	0.5400	−2.8000	0.5900	0.0050	-	***
CAR	0.0070	−0.2384	0.2500	−2.8000	0.8060	0.0050	-	***
IRR	−0.0383	−0.0332	−2.2200	−2.9600	0.0270	0.0030	**	***
IRR_SQ	0.0047	0.0071	2.2900	2.7700	0.0220	0.0060	**	***
GDPGR	−0.0007	0.0006	−2.2400	1.6800	0.0250	0.0940	**	*
Const.	0.0780	0.0958	2.4600	4.0400	0.0140	0.0000	**	***

Table 8.

Diagnostic Using Arellano-Bond Estimates and Sargan Test.

		Value		Probability
Diagnostic Test	Stat.	(1)	(2)	(1)	(2)
Arellano-Bond test for AR(1)	z	−1.03	−1.25	0.30	0.21
Arellano-Bond test for AR(2)	z	−1.17	−0.34	0.24	0.73
Sargan Test	chi2(2)	18.84	1.38	0.00	0.50
Hansen Test	chi2(2)	2.84	1.72	2.42	0.42

In the United States, DUAL and CAR show no significant association with either NPL or ROA, indicating governance and ERM are not binding on bank risk or profitability (Table 5). In the UK, NPL is significantly affected but ROA is not (Table 9). This suggests that while governance and ERM matter for asset quality, UK banks are still able to maintain profitability, consistent with the notion of pricing power that insulates returns from risk.

Table 9.

Estimates of Driscoll–Kraay Regression at FE Lag (1) for UK.

	Estimate		t-statistic		p-value		Sig.
	ROA	NPL	ROA	NPL	ROA	NPL	ROA	NPL
LAG 1	0.1332	0.1136	0.5200	0.7000	0.6400	0.5320	-	-
CAR	−1.6709	−1.3422	−7.0800	−3.0700	0.0060	0.0550	**	*
TARatio	0.0005	0.0069	2.4200	2.7200	0.0950	0.0730	*	*
LDR	−0.0009	−0.0015	−3.5700	−2.5100	0.0380	0.0870	**	*
CARxLDR	0.3934	0.3135	8.5200	3.1400	0.0030	0.0520	**	*
IRR	0.0329	0.0662	2.6100	2.4000	0.0800	0.0960	*	*
IRR_SQ	−0.0032	−0.0055	−2.3200	−2.1700	0.1030	0.1190	-	-
GDPGR	0.0022	0.0045	3.2100	2.3100	0.0490	0.1040	**	-
GDPGR_SQ	0.0007	0.0016	2.6300	2.3800	0.0790	0.0980	*	*

With the Driscoll–Kraay fixed-effects estimator, there has been substantial advances to the results as well. The CAR becomes strongly positive using the DK-FE estimate for NPL. However, the significance is only at 90 per cent CI (Table 5). The size effect is also negative in nature. The LDR is also marginally significant, but shows a low and positive direction (Table 5). Under the Driscoll–Kraay model, the Interest Rate becomes more stable and so does the square term of interest rate. For the ROA, the CAR becomes largely negative. However, this relation is still insignificant, the size and risk effects are also not significant. Here, the IRR is shown to have a positive impact, similar to the IRR square value (Table 5). Moreover, the patterns within the Driscoll–Kraay estimator is sharper. In the United States, governance and ERM affect NPL but not ROA. This shows that asset quality is influenced, whereas the profit margins are shielded. However, in the UK, both NPL and ROA respond significantly to governance and ERM (Table 9). This indicates that risk management is core-binding.

6 Discussion on Corporate Governance and ERM of Banks

The results of the analysis show that Governance factors have a significant impact on banking risks in the United Kingdom. This is because of the stringent regulations set in the UK with respect to banking operations. The UK has strict guidelines set by the regulatory authorities towards the maintenance of capital (Benetton, 2021). As a result, it is necessary for the management of the banks to adhere to such policies. Therefore, under the directive of the directors, the governance of the banks also has an important role to play in reducing risks. As per Alexander (2006), one of the key responsibilities of the directors was to maximise the wealth of the shareholders. Hence, there is a robust corporate governance within the banks of Britain that helps in reducing risks. This has been further portrayed in the analysis, as duality in board reduced the NPL for banks by 0.79 percent. The CAR also has a negative and significant impact on the NPL in the UK. This is supported in the literature by Obiedallah and Abdelaziz (2024), who argued that capital adequacy is an integral parameter towards ERM effectiveness. Having greater capital adequacy as an ERM technique means that the banks would be able to handle losses in a better manner. This would reduce the risk factor of the banks. As a result, both variables have an inverse relation. Macroeconomic factors in the UK like IRR and GDP significantly impact the NPL. An increase in the IRR leads to a fall in the NPL among banks. As per Ahmed et al. (2021), the interest rates positively impact NPL because of high borrowing costs. However, on the contrary, here, the higher interest rate has a negative effect in the UK. This comes as the Financial Reporting Council (2024), issued stricter regulations on banks in Britain regarding the auditing of risks. As a result, during periods of high interest rates, the loans made by banks were highly scrutinised. This led to lower NPL factors and led to a lower impact on non-performing loans. GDPGR has a positive impact on the banks in UK. This also comes as periods of economic growth call for greater borrowings by enterprises for operations (Luft & Omarkhil, 2018). This also increases the risks for loans provided by financial institutions. As a result, there is an increased probability of NPL. In the United States, it has been observed that the corporate governance indicators and ERM indicators do not affect the NPL. For the United States, the IRR has a positive impact on the NPL. This comes as the higher interest rates raise the cost of loan servicing in the economy (Luft & Omarkhil, 2018). Thus, the probability of defaulters rises in the USA as there are less substantial regulations towards screening of risky loans compared to the UK. Finally, there is also a positive relation between GDPGR and NPL in the States.

The DK-FE estimates were added as a robustness test to the paper. In the UK, the CAR remains negative and highly significant under both estimators. This is consistent with the research by Obiedallah and Abdelaziz (2024). Whereas, the scale effect have higher NPL ratios. The same is validated in the paper by Liem (2018). The liquidity parameter indicates that banks with a higher deposit base show fewer bad loans (Boyd & De Nicolo, 2005). Moreover, the interaction term show that capital adequacy only really curbs NPLs when a bank also has a healthier deposit funding structure.

For the United States, the CAR was insignificant under GMM. This is validated by Golbabaei and Botshekan (2022). However, under the DK-FE model, CAR yields a positive coefficient for NPL. The size is negatively related to the NPL and LDR has a positive impact. The interaction term show that capital buffers only translate into lower NPLs when funding is more deposit-rich.

The empirical results also highlight an important conceptual distinction between non-binding risk and core-binding risk. As noted from the results in the United States, governance and ERM indicators such as board duality and the capital adequacy ratio have limited or inconsistent effects on profitability (ROA). This is valid even when the asset quality (NPL) is influenced. This suggests that U.S. banks are able to price through risk, absorbing deterioration in loan portfolios without a parallel decline in profitability. This is validated through Wang (2022) and Golbabaei and Botshekan (2022), who show that US banks emphasize more on investment activities and also have a diversified income stream. This allows them to absorb loan-portfolio shocks without immediate hits to profitability. Moreover, US banks are also more flexible in terms of capital management. This allows them to maintain financial stability and absorb risk, even if NPLs increase. As a result, in US the ERM may reduce risk indicators without binding on profitability.

In contrast, the United Kingdom shows a different dynamic. Governance and ERM variables significantly affect both NPL and ROA, implying that risk management in the UK is core-binding. As per Alexander (2006) and Benetton (2021), the same is highlighted regarding the corporate governance in the UK. Given the tighter regulatory framework, the governance and ERM in the UK are core-binding as they affect both the NPL and ROA. Overall, the results suggest a structural asymmetry as U.S. banks primarily manage non-core risks, with profitability insulated by pricing power and capital flexibility, whereas UK banks face core-binding risks, where governance and ERM have a direct and significant impact on both risk and profitability.

The results of the study focus mainly on the United States and the United Kingdom. However, the findings provide useful insights for other banking systems. In countries where banks enjoy greater pricing power and more flexible capital strategies, governance and ERM factors may influence asset quality. On the contrary, jurisdictions with tighter prudential regulation and stronger governance obligations, similar to the UK, risk management is more likely to be core-binding, affecting both loan quality and returns. Emerging markets with weaker governance or volatile macroeconomic conditions may face even stronger dependencies. Overall, the binding versus non-binding distinction provides a conceptual framework that can guide cross-country comparisons and regulatory design.

7 Conclusion

7.1 Summary

Overall, the study shows that corporate governance and ERM frameworks mitigate asset-quality risks in both countries. However, the effects translate differently into profitability across the nations. In the US, banks can reprice to offset risk, whereas in the UK, governance and ERM remain core-binding for both risk and margins.

The banking sector is one of the most important institutions for an economy as it facilitates the supply of money and enables economic growth. However, in the modern economy, it is necessary to address the enterprise risk management facilities and the corporate governance levels in order to mitigate banking risks, especially during global shocks. To aim of the study was to analyse the impact that corporate governance and risk management had on the banking risks while controlling for macroeconomic variables across the UK and the USA. The methodology entailed a GMM method and used longitudinal data for 10 banks across the UK and the USA for 5 years. A DK-FE Robustness test was also considered in the paper using additional variables. The results of baseline GMM present that CEO duality and the CAR has a significant negative effect on the NPL in UK. However, there was no such relation found for the US. This result holds when we re-estimate via Driscoll–Kraay fixed-effects. In the In the DK–FE robustness check, larger UK banks exhibit higher NPLs unless they also maintain strong deposit funding.

Moreover, macroeconomic factors like policy rates and economic growth impact the NPL for both countries significantly. Based on the results, the first hypothesis could be accepted for the UK only. This is because Corporate Governance and ERM impact the NPL for the UK significantly. Hence, after testing the hypotheses, it can be concluded that both the hypotheses are rejected. For the first hypotheses, it is rejected in UK only and not in USA. However, the second hypotheses is rejected in both nation.

7.2 Limitations of the Research

The research also comes with a number of limitations, despite being able to analyse the impact of corporate governance and risk-management on banks in America and UK. One of the main limitations is the analysis the impact that global economic shocks have on the present risk factors for banks while controlling for ERM and governance. This would be impactful in policy-making for banking sectors across the United States and the United Kingdom. Secondly, GMM estimates could be biased given the T-value and N-value of the dataset considered. This can lead to biased estimates.

7.3 Directions for Future Papers

Future papers could use an Impulse Response Function to analyse the effect of economic shocks on bank risks. Other than that, future papers could also use a longer list of banks and create a comparative study for financial institutions across different segments of market capitalisation. This would make the analysis more dynamic. Other corporate governance indicators could also be included in the study to enhance the power of estimation. In future, the similar methodology using system-GMM can also be considered with a longer dataset specification to ensure validity. Finally, future studies could extend this framework to emerging economies to test whether the binding or non-binding distinction holds across diverse regulatory environments.

Footnotes

Ethical Considerations

This article does not involve human life science and medical research, so ethical review is not applicable.

Consent to Participate

Yes

Consent for Publication

Yes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Jiayu Fang

Hanini Ilyana Che Hashim

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