Risk Committee Attributes And Bank Performance: A Camels-Based Analysis Of Deposit Money Banks In Nigeria

Risk Committee Attributes and Bank Performance: A Camels-Based Analysis of Deposit Money Banks in Nigeria

Godwin Omoregbee¹, Yusuf Babatunde Rahman², Oluseyi Olanrewaju³

^1,2Department of Accounting, Lagos State University, Ojo, Lagos, Nigeria

³Department of Finance, James Hope University, Lekki, Lagos, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.9010053

Received: 25 December 2024; Accepted: 30 December 2024; Published: 31 January 2025

ABSTRACT

The banking industry is essential for maintaining financial stability and promoting economic development via efficient risk management and corporate governance. This study examines the influence of risk committee attributes such as size, independence, and diversity on the performance of Tier 1 and Tier 2 Deposit Money Banks (DMBs) listed on the Nigerian Exchange Ltd. This study employs the CAMELS framework and panel data from 10 Deposit Money Banks (DMBs) over 11 years (2012–2022) to assess performance using important metrics: Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratio (LR). The research findings indicates that the size of the risk committee adversely affects ROE and LR in Tier 1 banks, whereas diversity enhances CAR and LR in Tier 2 banks. The impact of committee independence is varied, markedly enhancing liquidity in Tier 2 banks while exerting little effects on other indicators. The research highlights the necessity for Tier 1 banks to refine committee size to boost decision-making and advocates for increased diversity in Tier 2 banks to bolster capital adequacy and liquidity management. These discoveries offer significant implications for policymakers, regulators, and bank executives in refining governance frameworks to improve financial performance.

Keywords: Risk Committee Governance, CAMELS Rating, Return on Equity, Capital Adequacy Ratio, Liquidity Ratio

INTRODUCTION

The banking sector plays a crucial role for economic development and financial stability, especially through effective risk management and governance. Although current research has extensively explored risk governance, there has been insufficient focus on the specific effects of risk committee attributes, including size, independence, and diversity, on the performance of Tier 1 and Tier 2 Deposit Money Banks (DMBs) in Nigeria.

This study employs the CAMELS framework to investigate the varying impacts of risk committee attributes on Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratio (LQ) , and offers useful insights for policymakers, bank managers, and academics aiming to optimize governance structures for improved financial performance by examining both systemic and non-systemic institutions.

Statement of the Problem

Although significant progress has been made in corporate governance and risk management procedures, the impact of risk committee attributes on the performance of Nigerian Deposit Money Banks (DMBs) is still inadequately examined, especially regarding Tier 1 and Tier 2 institutions. Risk committees are essential in supervising risk management frameworks, guaranteeing that risks are systematically discovered, evaluated, and mitigated in accordance with the institution’s risk appetite and regulatory obligations (Iwasaki, 2020; Vafeas, 2020). Several recent studies (Kim, Ma, & Wang, 2020; Nahar, Azim, & Jubb, 2021) have found that effective risk committees enhance decision-making and contribute to more transparent and timely risk disclosures, which in turn improves financial stability. These committees are critical in fostering sound governance and meeting regulatory demands, ensuring that risks are managed effectively (Musah, 2019).

The size, independence, and diversity of a bank’s risk committee are crucial governance components that directly impact performance. Recent studies show that larger committees bring a broader range of expertise and perspectives, which is beneficial for identifying and mitigating complex financial risks (Ammari, Menif, & Zéghal, 2020). Independent committee members provide unbiased oversight and prevent conflicts of interest, improving governance effectiveness (Ong & Djajadikerta, 2020). In addition, diversity, particularly in terms of gender, experience, and background, has been linked to more robust risk assessments and overall risk management (Post & Byron, 2021; Nguyen, Locke, & Reddy, 2020). Each of these factors: committee size, independence, and diversity affect a bank’s ability to manage financial risks, influencing critical financial metrics such as Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratios (LQ) (Li, Li, & Minor, 2020; Herbohn, Kent, & Tutticci, 2020).

Studies such as Sugiyanto & Tukiyat (2022) have explored how risk governance influences performance during periods of financial instability, but there is limited research on how risk committee characteristics affect long-term performance during stable economic periods. This gap is significant because governance practices during non-crisis periods are just as vital in ensuring sustained financial health (Musah, 2019). In Nigeria, where the banking sector plays a key role in economic growth, understanding how these governance factors impact long-term financial performance is critical (Ammari, Menif, & Zéghal, 2020).

Additionally, Tier 1 and Tier 2 DMBs in Nigeria face distinct operational challenges. Tier 1 DMBs, with larger capital bases and more advanced governance structures, tend to be more resilient to economic fluctuations (Iwasaki, 2020). Their extensive networks and access to resources make them better equipped to manage financial risks (Hassan, Habib, & Al‐Hadi, 2020). In contrast, Tier 2 DMBs, with smaller asset bases, often face more significant challenges in maintaining liquidity, managing capital adequacy, and achieving profitability (Nguyen, Locke, & Reddy, 2020). For these banks, an effective risk committee is critical for addressing these issues, particularly as they are more exposed to operational risks (Post & Byron, 2021).

The lack of comprehensive research comparing the influence of risk committee governance on the performance of Tier 1 and Tier 2 DMBs leaves a significant gap in the literature. By investigating the impact of risk governance mechanisms across both types of banks, this study aims to provide empirical evidence that can inform policymakers and bank managers in optimizing governance structures. These insights are vital to ensuring that risk committees are well-structured and capable of supporting the long-term financial stability of Nigerian DMBs (Li, Li, & Minor, 2020). The findings from this study could also contribute to the development of more tailored regulatory frameworks that enhance governance practices across the Nigerian banking sector (Vafeas, 2020).

Research Objectives

The overall objective of this study is to examine the influence of Risk Committee characteristics on the performance of Tier 1 and Tier 2 Deposit Money DMBs listed on the Nigerian Exchange Ltd (NGX). The specific objectives are:

1. To evaluate the impact of Risk Committee Size (RCS) on the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd
2. To evaluate the impact of Risk Committee Size (RCS) on the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.
3. To analyze the influence of Risk Committee Independence (RCI) on the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd.
4. To analyze the influence of Risk Committee Independence (RCI) on the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.
5. To assess the influence of Risk Committee Diversity (RCD) on the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd.
6. To assess the influence of Risk Committee Diversity (RCD) on the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.

Research Questions

The study seeks to answer the following research questions:

1. Does Risk Committee Size (RCS) have influence on the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd?
2. Does Risk Committee Size (RCS) have influence on the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd?
3. How does Risk Committee Independence (RCI) influence the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd?
4. How does Risk Committee Independence (RCI) influence the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd?
5. In what ways does Risk Committee Diversity (RCD) enhance the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd?
6. In what ways does Risk Committee Diversity (RCD) enhance the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd?

Statement of Hypotheses

The study is guided by the following null hypotheses:

1. H_0a: Risk Committee Size (RCS) does not significantly influence the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd.
2. H_0b: Risk Committee Size (RCS) does not significantly influence the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.
3. H_0c: Risk Committee Independence (RCI) does not significantly impact the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd.
4. H_0d: Risk Committee Independence (RCI) does not significantly impact the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.
5. H_0e: Risk Committee Diversity (RCD) does not significantly influence the performance of Tier 1 DMBs listed on the Nigerian Exchange Ltd.
6. H_0f: Risk Committee Diversity (RCD) does not significantly influence the performance of Tier 2 DMBs listed on the Nigerian Exchange Ltd.

Scope and Significance of the Study

Scope of the Study

This study focuses on Risk committee Governance in Tier 1 and Tier 2 banks listed on the Nigerian Exchange Ltd and its impact on bank performance. Specifically, the study examines the influence of risk committee characteristics size, independence, and diversity on key performance metrics such as Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratios (LQ) over the period 2012 to 2022. The data used in the study is derived from the audited financial statements of the selected DMBs.

Significance of the Study

The findings of this study will be significant to various stakeholders, including:

Bank Management: The insights gained from this research will help managers understand how different risk committee characteristics impact bank performance, enabling them to optimize their governance structures for improved financial outcomes.

Investors: Understanding the role of risk governance in influencing key performance metrics will aid investors in making more informed investment decisions, particularly when choosing between Tier 1 and Tier 2 DMBs.

Policymakers and Regulators: This study will provide empirical evidence to support regulatory reforms aimed at improving corporate governance standards in Nigerian banks. Policymakers can use the results to ensure that governance frameworks are tailored to the specific needs of Tier 1 and Tier 2 DMBs, enhancing financial stability and protecting depositors.

Academics and Researchers: The study contributes to the growing body of literature on corporate governance and bank performance, particularly in emerging economies. It provides a basis for future research on risk governance and its impact on financial performance in other sectors or regions.

LITERATURE REVIEW

Conceptual Review

Risk Committee Governance

Risk committee governance refers to the structures, processes, and practices that enable a bank’s risk committee to effectively oversee risk management activities. Effective governance ensures that risks are identified, evaluated, and managed within the bank’s risk appetite and regulatory framework. Research shows that banks with stronger risk committee governance structures are better positioned to mitigate risks, improve transparency, and align management objectives with shareholders’ interests (Nahar, Azim, & Jubb, 2021; Kim, Ma, & Wang, 2020). Well-governed risk committees play a pivotal role in preventing excessive risk-taking and ensuring that the bank remains resilient to financial shocks (Musah, 2019). Key dimensions of risk committee governance include Risk Committee Size (RCS), Risk Committee Independence (RCI), and Risk Committee Diversity (RCD).

Risk Committee Size (RCS)

Risk Committee Size (RCS) refers to the number of members constituting a bank’s risk committee, a specialized governance body tasked with overseeing the identification, evaluation, and mitigation of risks. The size of the committee significantly influences its capacity to bring diverse expertise, perspectives, and resources to the risk management process (Jiang & Ji, 2023). An optimally sized risk committee balances diversity and efficiency, ensuring effective oversight without operational delays or redundancies.

The size of a bank’s risk committee is a pivotal determinant of its effectiveness in managing and mitigating risks. Larger committees often incorporate a broader range of expertise and perspectives, facilitating comprehensive risk evaluations and informed decision-making. Such diversity enhances the committee’s ability to address complex financial challenges and align risk governance with strategic objectives (Zoghlami & Jallali, 2022). For instance, Jiang and Ji (2023) observed that medium-sized committees effectively reduced tail risks and improved profitability in U.S. banks, especially those operating under the Dodd-Frank regulatory framework.

However, excessively large committees may face challenges such as inefficiencies, slower decision-making, and coordination difficulties. These operational bottlenecks can hinder a committee’s agility in responding to emerging risks, particularly in dynamic financial markets (Moridu, 2023). Similarly, Zoghlami and Jallali (2022) highlighted the risks of diluted accountability and communication challenges in overly large committees, suggesting that size must align with the bank’s operational complexity.

Conversely, smaller risk committees, while more agile and cohesive, may lack the diversity necessary to effectively oversee risk management. Limited expertise in smaller committees can result in narrow perspectives and oversight gaps, especially in institutions with multifaceted risk profiles (Ellul & Yerramilli, 2023). These findings underline the importance of tailoring the committee size to a bank’s regulatory environment and operational needs.

Striking an optimal balance between inclusivity and efficiency is essential for effective risk committee governance. Studies consistently emphasize that the ideal size of a risk committee should be informed by the bank’s strategic objectives, complexity, and regulatory requirements (Jiang & Ji, 2023; Zoghlami & Jallali, 2022). By doing so, banks can enhance the effectiveness of their risk oversight processes, ensuring robust governance and resilience in the face of financial uncertainties.

Risk Committee Independence (RCI)

Risk Committee Independence (RCI) refers to the inclusion of independent members within a bank’s risk committee who are not part of the institution’s management team. These independent members bring an unbiased perspective to risk oversight, free from internal politics or management influence. Their role is critical in ensuring objective decision-making, enhancing transparency, and aligning management actions with shareholder interests (Basel Committee on Banking Supervision, 2020). Independent risk committees are considered vital for curbing excessive risk-taking and fostering accountability within the governance framework.

The independence of a risk committee is a fundamental element of effective risk governance. Independent members are better positioned to critically evaluate management proposals, challenge assumptions, and oversee risk practices objectively, as they are not directly involved in the day-to-day operations of the bank. This objectivity enhances the committee’s ability to identify potential risks and ensure they are managed in alignment with the bank’s strategic objectives and regulatory requirements (Jiang & Ji, 2023).

Studies have shown that greater committee independence correlates with improved risk management outcomes. For instance, Zoghlami and Jallali (2022) observed that independent risk committees in Islamic banks were more effective in enhancing governance structures and mitigating risks. Their research emphasized that independence fosters robust decision-making, particularly in complex regulatory environments. Similarly, Moridu (2023) highlighted that independent directors enhance risk oversight by providing impartial judgment, which is crucial for ensuring sound governance practices.

However, the effectiveness of risk committee independence is contingent upon the expertise and qualifications of its members. Independent members must possess a deep understanding of the financial industry and regulatory landscape to provide meaningful oversight. Without the requisite knowledge, their independence may become symbolic rather than functional, leading to suboptimal risk governance outcomes (Ellul & Yerramilli, 2023).

Furthermore, independence is most effective when combined with complementary governance practices, such as clear reporting lines and transparent communication. The Basel Committee on Banking Supervision (2020) underscores the importance of equipping independent members with access to accurate and timely information to enhance their ability to oversee risks effectively.

Risk Committee Independence (RCI) is indispensable for objective and transparent risk governance. By including qualified independent members, banks can strengthen their oversight mechanisms, prevent excessive risk-taking, and align management decisions with shareholder interests. As emphasized by recent studies (Jiang & Ji, 2023; Zoghlami & Jallali, 2022), fostering independence within the risk committee contributes significantly to improved governance and financial stability, ensuring resilience in the face of evolving risks.

Risk Committee Diversity (RCD)

Risk Committee Diversity (RCD) refers to the inclusion of members with varied attributes such as gender, ethnicity, professional background, and expertise within a bank’s risk committee. Diversity in the committee enhances its ability to identify and address a wide range of risks by incorporating different perspectives and experiences. It promotes innovative approaches to risk management and ensures more balanced decision-making, thereby improving governance effectiveness and bank performance (Widjaja & Feliana, 2022).

The importance of diversity within risk committees has been increasingly recognized in contemporary governance discourse. Diverse committees bring a broader range of insights and perspectives, which are particularly valuable in identifying emerging risks and crafting robust mitigation strategies. Widjaja and Feliana (2022) demonstrated that risk committees with greater diversity are more likely to identify unconventional risks and propose innovative solutions, enhancing overall decision-making quality and bank performance.

Gender diversity, in particular, has garnered significant attention. Studies indicate that the inclusion of women on risk committees positively influences governance outcomes. Zoghlami and Jallali (2022) found that gender-diverse committees in Islamic banks led to improved risk oversight and alignment with stakeholder interests. Similarly, Jiang and Ji (2023) noted that committees with diverse professional backgrounds, including expertise in risk management, law, and finance, were better equipped to navigate complex regulatory requirements and operational challenges.

However, achieving effective diversity requires more than demographic representation. It necessitates creating an inclusive environment where all members can actively contribute their perspectives. Without such inclusivity, the potential benefits of diversity may remain unrealized, as members may hesitate to voice dissenting opinions or challenge prevailing assumptions (Ellul & Yerramilli, 2023). Additionally, overly diverse committees may face challenges in achieving consensus, which could slow decision-making processes if not well managed. Empirical evidence suggests that banks with diverse risk committees exhibit stronger governance metrics and financial resilience. For instance, Moridu (2023) highlighted that diversity in West Java-based banks improved risk identification and mitigation capabilities. This finding aligns with the Basel Committee on Banking Supervision (2020), which underscores diversity as a key principle in enhancing governance structures and risk management effectiveness.

Risk Committee Diversity (RCD) is integral to fostering comprehensive and innovative risk governance. By incorporating members with varied backgrounds, banks can leverage diverse perspectives to enhance decision-making and improve performance. As highlighted in recent studies (Widjaja & Feliana, 2022; Zoghlami & Jallali, 2022), diversity must be accompanied by inclusivity and effective management practices to unlock its full potential, ensuring resilience and robust oversight in dynamic financial environments.

Capital Adequacy Ratio (CAR)

The Capital Adequacy Ratio (CAR) is a critical measure of a bank’s financial strength and resilience. It is calculated as the ratio of a bank’s capital to its risk-weighted assets and serves as an essential regulatory tool to ensure that banks maintain sufficient capital buffers to absorb potential losses. CAR is integral to safeguarding depositors’ funds, maintaining financial stability, and mitigating systemic risks in the banking sector (Barakat & Hussainey, 2019). A higher CAR signifies greater capacity to withstand financial shocks and aligns with regulatory standards such as those set by the Basel Accords.

CAR plays a pivotal role in bank performance and risk management, as it directly reflects a bank’s ability to manage unexpected losses. Banks with higher CAR levels are better positioned to sustain operations during periods of financial distress, thus enhancing their stability and market confidence. Barakat and Hussainey (2019) emphasized that CAR not only ensures regulatory compliance but also signals a bank’s operational soundness to investors and stakeholders.

Recent empirical studies have underscored the influence of CAR on bank performance. For instance, Zoghlami and Jallali (2022) found that banks with robust capital adequacy exhibited superior financial performance and resilience, particularly in turbulent economic conditions. Similarly, Jiang and Ji (2023) noted that higher CAR levels among U.S. banks facilitated improved risk mitigation, aligning with stricter post-crisis regulatory requirements. These findings highlight CAR’s dual role in promoting financial stability and enhancing bank profitability.

However, maintaining a high CAR can present challenges. Excessively high capital levels may indicate overly conservative risk-taking, potentially limiting a bank’s profitability and growth opportunities (Ellul & Yerramilli, 2023). Conversely, low CAR levels increase vulnerability to financial shocks and regulatory penalties, undermining stakeholder confidence. Thus, achieving an optimal CAR is crucial for balancing risk management with competitive performance.

The Basel Committee on Banking Supervision (2020) underscores CAR as a cornerstone of risk governance, particularly under the Basel III framework. The framework emphasizes the importance of both Tier 1 (core) and Tier 2 (supplementary) capital in meeting regulatory thresholds, ensuring that banks remain solvent while supporting economic activities.

The Capital Adequacy Ratio (CAR) is a vital metric for assessing a bank’s financial health, regulatory compliance, and risk resilience. As highlighted in recent studies (Barakat & Hussainey, 2019; Zoghlami & Jallali, 2022), maintaining an optimal CAR is essential for balancing financial stability with profitability. By adhering to robust capital adequacy standards, banks can enhance their resilience to financial shocks, align with regulatory requirements, and sustain stakeholder confidence in an increasingly complex financial landscape.

Liquidity Ratio (LQ)

The Liquidity Ratio (LQ) measures a bank’s ability to meet its short-term financial obligations, ensuring it can cover liabilities as they fall due without jeopardizing its operational stability. This ratio is typically calculated as the proportion of liquid assets (such as cash, treasury bills, and short-term investments) to total assets or the ratio of liquid assets to short-term liabilities. A higher LQ reflects a bank’s strong liquidity position, enabling it to effectively manage short-term risks and financial obligations (Hasanov et al., 2018).

Liquidity management is critical for sustaining a bank’s stability, particularly in volatile financial environments. Banks with high liquidity ratios are better positioned to handle unexpected outflows, regulatory requirements, or economic shocks, thus maintaining confidence among depositors and investors. Hasanov et al. (2018) emphasize that a robust liquidity position enables banks to avoid liquidity crises that can lead to insolvency or reputational damage.

However, excessively high liquidity ratios may signal inefficient use of resources, as funds that could be invested in higher-yielding opportunities remain tied up in low-return liquid assets. Conversely, a low LQ increases a bank’s vulnerability to liquidity shortfalls, which can undermine its financial health and stakeholder confidence (Ellul & Yerramilli, 2023). Thus, effective liquidity management involves striking a balance between maintaining adequate liquidity and optimizing returns.

Recent empirical studies reinforce the significance of LQ in enhancing bank resilience. Zoghlami and Jallali (2022) highlighted that bank with well-managed liquidity ratios exhibited greater operational stability and performance during financial crises. Similarly, Jiang and Ji (2023) observed that liquidity management strategies aligned with regulatory standards, such as Basel III’s Liquidity Coverage Ratio (LCR), contributed to long-term financial sustainability.

The Liquidity Ratio (LQ) is a critical measure of a bank’s financial health and risk resilience. Effective liquidity management not only ensures short-term stability but also supports long-term operational viability. As highlighted by Hasanov et al. (2018) and Zoghlami and Jallali (2022), maintaining an optimal liquidity ratio enables banks to navigate financial uncertainties, align with regulatory frameworks, and sustain confidence among stakeholders.

Return on Equity (ROE)

Return on Equity (ROE) is a financial performance metric that measures a bank’s profitability in relation to its shareholders’ equity. It is calculated as the ratio of net income to shareholders’ equity, indicating how effectively a bank utilizes equity investments to generate profits. A higher ROE suggests better financial performance and efficient management of resources. In the context of governance, a well-functioning risk committee plays a significant role in enhancing ROE by minimizing financial risks, ensuring efficient resource allocation, and improving decision-making (Ajayi & Olalekan, 2023).

ROE serves as a vital indicator for investors and stakeholders to evaluate a bank’s financial health and profitability. A high ROE reflects the bank’s ability to generate substantial returns on equity investments, making it an attractive proposition for shareholders. Ajayi and Olalekan (2023) highlighted that effective corporate governance, particularly through risk committees, is instrumental in driving ROE improvements. Risk committees contribute by mitigating financial risks, optimizing capital allocation, and ensuring adherence to regulatory standards.

Empirical evidence supports the positive relationship between robust risk governance and enhanced ROE. For example, Zoghlami and Jallali (2022) found that banks with independent and diverse risk committees reported higher ROE, emphasizing the importance of governance structures in improving profitability. Similarly, Jiang and Ji (2023) observed that banks with well-structured risk management practices achieved superior returns on equity by balancing risk and reward effectively.

However, while a high ROE is desirable, excessively high figures may indicate aggressive risk-taking or over-leveraging, which could expose the bank to financial instability during downturns. Conversely, low ROE may reflect inefficiencies in resource utilization or conservative risk strategies that limit profitability potential (Ellul & Yerramilli, 2023). Thus, achieving an optimal ROE requires a balance between profitability and risk governance.

Return on Equity (ROE) remains a key measure of financial performance and governance effectiveness in banks. By ensuring prudent risk management and resource allocation, well-structured risk committees significantly enhance ROE, aligning profitability with long-term stability. Recent studies (Ajayi & Olalekan, 2023; Zoghlami & Jallali, 2022) reinforce the importance of strong governance frameworks in driving superior financial performance while mitigating risks. Banks must balance high profitability with sustainable risk practices to ensure resilience and shareholder value creation.

CAMELS Rating

The CAMELS rating is a supervisory rating system that assesses the health of banks by examining six critical components: Capital Adequacy, Asset Quality, Management Quality, Earnings, Liquidity, and Sensitivity to Market Risk (CAMELS). This system is widely used by regulators to evaluate the overall soundness of banks. Each component is rated on a scale from 1 (strongest) to 5 (weakest), and the composite score is used to determine a bank’s regulatory status (Olaniyi & Olabode, 2021).

1. Capital Adequacy evaluates the sufficiency of a bank’s capital in absorbing potential losses.
2. Asset Quality assesses the risks associated with the bank’s loan portfolio and investments.
3. Management Quality reflects the competency of the bank’s leadership in mitigating risks and maintaining operational efficiency.
4. Earnings looks at the bank’s profitability and stability of income streams.
5. Liquidity measures the ability of the bank to meet its short-term obligations.
6. Sensitivity to Market Risk evaluates how susceptible the bank is to changes in market conditions.

The CAMELS rating is a vital tool for regulators and investors alike, offering insights into the financial stability and operational performance of banks (Kleimeier & Viehs, 2020). By incorporating the CAMELS framework, banks can improve their governance and risk management practices, ultimately leading to better financial outcomes.

Theoretical Review

Agency Theory (Jensen & Meckling, 1976) posits that conflicts of interest arise when the owners (principals) and managers (agents) of a firm have different objectives. In the banking sector, this theory is particularly relevant to the relationship between shareholders and bank management. Risk committees play a vital role in aligning the interests of management with those of the shareholders by overseeing risk management practices and preventing excessive risk-taking. Independent risk committee members are particularly crucial in mitigating the agency problem, as they provide an unbiased perspective and reduce the likelihood of management engaging in opportunistic behavior (Jensen & Meckling, 1976). A key critique of this theory is that it may overemphasize monitoring and fail to account for the stewardship role of managers (Donaldson & Davis, 1991).

Stewardship Theory by (Donaldson & Davis, 1991) suggests that managers act as stewards of the company, working in the best interests of shareholders. This theory assumes that managers are intrinsically motivated to maximize the long-term value of the firm, which aligns their goals with those of the shareholders. In the context of risk committees, Stewardship Theory supports the idea that management can effectively govern risk without excessive monitoring. However, some studies, such as Herbert & Agwor (2021), suggest that even stewards may benefit from independent oversight to ensure risk-taking aligns with broader strategic goals.

Resource Dependence Theory (Pfeffer & Salancik, 1978) on the other hand posits that organizations must manage their external environment to access critical resources. In banking, Risk Committee Diversity (RCD) can be viewed through the lens of Resource Dependence Theory, as diverse committees are better equipped to provide a broad range of expertise and connections that help the bank navigate regulatory and market challenges. Diverse committees also bring unique perspectives that enhance decision-making, particularly in risk management. The main critique of this theory is that it tends to focus on external factors while underestimating internal governance dynamics (Pfeffer & Salancik, 1978).

Stakeholder Theory (Freeman, 1984)

Stakeholder Theory argues that a firm’s success depends on its ability to satisfy not just shareholders but a wide range of stakeholders, including employees, customers, regulators, and the community. In the banking sector, Risk Committees are responsible for balancing the interests of these various stakeholders by ensuring that the bank’s risk exposure is managed effectively. This theory is particularly relevant in the context of regulatory compliance, as banks are accountable to both their shareholders and regulators. The downside of this theory is its broad scope, which can make it difficult to prioritize stakeholders effectively (Freeman, 1984).

Empirical Review

This section reviews recent empirical studies that examine the relationship between risk governance mechanisms and bank performance, highlighting their methodologies, key findings, and implications.

Herbert & Agwor (2021) in their study Corporate Governance Disclosure and Financial Performance: A Study of Nigerian Banks, examined the relationship between corporate governance disclosure and financial performance in Nigerian banks, using a correlational analysis approach. They found a positive relationship between governance disclosure, including risk committee structures, and financial metrics like ROE and ROA. Their study emphasized the importance of transparent governance for improving bank performance but identified a gap in understanding how these disclosures affect non-financial outcomes.

In another study, Ajayi & Olalekan (2023) conducted a panel data regression analysis to explore how Risk Committee Size (RCS), Risk Committee Independence (RCI), and Risk Committee Diversity (RCD) influence bank performance in Nigeria. They discovered that risk committee diversity had a significant positive impact on ROE and Liquidity Ratios (LQ), especially in Tier 2 DMBs. The study highlighted that diverse governance structures enhance bank performance by reducing risk exposure and improving decision.

Uhunmwangho (2022) in Assessing the Financial Health of Nigerian Banks Using the CAMEL Model analyzed the financial health of seven Nigerian banks using multiple discriminant analysis and logistic regression. The study evaluated the impact of CAMEL indicators on financial health and found that liquidity, asset quality, and earnings negatively influenced bank financial health. The study emphasized that improving these metrics could enhance financial stability but noted a gap in understanding the role of risk committees.

Sugiyanto and Tukiyat (2023) investigated how independent risk committees affect bank performance during financial crises. Using panel data regression, they found that banks with independent risk committees showed better performance in managing capital adequacy and liquidity during times of crisis. This highlights the importance of risk governance in ensuring financial stability during economic downturns.

Hasanov et al (2018) examined the impact of risk management on bank profitability in oil-dependent economies using ordinary least squares (OLS) regression. The study found that banks with stronger capital adequacy and more effective liquidity management performed better during periods of economic volatility, underscoring the importance of robust risk governance frameworks (Hasanov et al., 2018).

Gonzalez & Garcia-Meca (2019) studied the impact of board diversity on financial performance in European banks using panel data analysis. They found that gender diversity, in particular, had a positive impact on ROE and ROA, indicating that more diverse boards foster better decision-making and improved risk management (Gonzalez & Garcia-Meca, 2019).

Lamidi et al. (2022) examined the impact of risk management committee characteristics on the financial performance of 13 DMBs in Nigeria using panel regression. Their findings revealed that committee size and independence negatively impacted performance, while gender diversity and meeting frequency enhanced outcomes. The study underscores the need for balanced committee structures that foster active engagement and diverse perspectives.

Malahim (2023) focused on the relationship between risk management committees and bank value in Jordan. Employing a multiple regression model on data from 18 banks between 2014 and 2021, the study found that committee independence and dual membership positively impacted value, while accounting or finance qualifications had a negative effect. The study suggests that committee dynamics and voluntary disclosures are critical for maximizing bank value.

Kyei et al. (2022) extended this inquiry to African banks by exploring the effect of board meetings on performance across 635 banks in 48 countries from 2000 to 2016. Using GMM analysis, they found contrasting regional results fewer board meetings enhanced shareholder value in Sub-Saharan Africa, while frequent meetings positively affected performance in Northern Africa. These findings highlight the significance of regional context in shaping governance effectiveness.

Ullah et al. (2023) assessed the determinants of bank efficiency in Pakistan through a Data Envelopment Analysis (DEA) and regression models. Examining 17 banks over a decade, the study found that corporate governance and ownership positively influenced efficiency, whereas financial leverage and inadequate risk management practices hindered operational effectiveness. The findings emphasize the importance of strong governance in enhancing efficiency.

Umar et al. (2023) evaluated the effect of firm risks and risk management committees on Nigerian banks’ performance from 2012 to 2019. Using CAMELS indicators and panel regression, they found that solvency, market, and capital adequacy risks significantly improved performance, while operational risk had a negative impact. The study concluded that robust risk management practices are essential for sustaining performance.

Nguyen and Dang (2022) analyzed the impact of risk governance structures on bank risk management effectiveness in ASEAN countries. Using dynamic panel models and the two-step GMM method, the study found that effective governance significantly enhanced risk management, focusing on insolvency, credit, and operational risks. These findings advocate for standardized risk governance frameworks to improve resilience.

Yahaya and Yakubu (2022) examined the influence of risk committee characteristics on enterprise risk management in Nigeria. Based on 130 observations from 13 banks over a decade, their study found that committee size negatively influenced risk management, while independence, gender diversity, and frequent meetings were positive determinants. The study recommends smaller, more independent committees to foster effective risk governance.

Nurwulandari et al. (2022) analyzed risk-based bank ratings and financial performance in Indonesian banks using GCG (Good Corporate Governance) as an intervening variable. The study, conducted on 41 banks from 2014 to 2019, revealed that GCG mediated the relationship between financial health and performance, with Non-Performing Loans (NPLs) and Operating Efficiency Ratio (OER) showing significant effects. The study underscores the role of governance in aligning risk management with financial goals.

Daou (2020) investigated the joint impact of credit and liquidity risk management on bank performance in the MENA region. Using panel regression on 51 banks between 2010 and 2018, the study found that while credit risk management significantly affected market performance, liquidity risk management alone was insignificant. However, the combined efforts of managing both risks positively influenced both accounting and market performances, demonstrating the need for integrated risk management approaches.

Nguyen et al. (2022) explored how risk-return trade-offs in governance structures impact risk management effectiveness in ASEAN banks. The study revealed that governance practices, such as independent risk committees and effective board oversight, positively influenced risk and operational efficiency. However, excessive risk constraints could harm performance, emphasizing the need for a balanced approach.

Liangliang Jiang and Mingming Ji (2023) analyzed the impact of risk management committees following the Dodd-Frank Act of 2010, using a difference-in-differences (DiD) design and instrumental variables. Examining U.S. bank holding companies with over $10 billion in assets, they found that establishing standalone risk committees significantly reduced total, tail, and residual risks. Additionally, the committees enhanced loan quality and profitability, especially for asset-diversified banks. These findings underscore the importance of formalized risk governance structures in mitigating financial risks and improving overall bank performance​.

Feten Zoghlami and Safa Jallali (2022) investigated the mediating role of risk governance between corporate governance, risk management, and Islamic banks’ performance using structural equation modeling on 129 Islamic banks from 2010 to 2018. The findings highlighted the critical role of risk governance mechanisms in enhancing corporate governance effectiveness while partly mediating the impact of risk management on performance. The study emphasized the unique attributes of Islamic banks, particularly Sharia compliance, which contributes to both opportunities and challenges in risk management​.

METHODOLOGY

This study utilizes a panel data regression method to effectively capture both temporal and cross-sectional fluctuations, facilitating a thorough investigation of governance dynamics over an 11-year period spanning (2012-2022). The CAMELS framework was selected for its comprehensive assessment of bank performance across six essential dimensions: Capital sufficiency, Asset quality, Management efficiency, Earnings, Liquidity, and Sensitivity to market risk.

The sampling purposeful and stratified sampling technique was adopted to guarantee coverage of both Tier 1 and Tier 2 Deposit Money Banks, reflecting the varied operational and governance frameworks within the Nigeria’s banking system. This methodology enhances the validity of the findings and guarantees their relevance to industrial practices and regulatory standards.

Table 1: Tier one and two Deposit Money Banks (DMBs)

Researcher’s Compilation 2024

Data Sources

This study utilizes secondary data obtained from the audited financial statements of the selected banks. These financial statements were sourced from the Nigerian Exchange Ltd (NGX), which provides reliable and publicly available data. The secondary data covers key financial performance metrics, including Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratio (LQ) for the period from 2012 to 2022.

Model Specification

To evaluate the relationship between Risk Committee Size (RCS), Risk Committee Independence (RCI), Risk Committee Diversity (RCD), and bank performance, three panel data regression models will be employed. The three dependent variables representing performance are Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratio (LQ). The models are structured as follows:

Model 1: ROE Model

This model evaluates the impact of risk committee characteristics on Return on Equity (ROE).

ROE_it=β₀+β₁RCS_it+β₂RCI_it+β₃RCD_it+β₄FS_it+ϵit

Where:

1. ROE_it ​ = Return on Equity for bank i at time t
2. RCS_it = Risk Committee Size for bank i at time t
3. RCI_it  = Risk Committee Independence for bank i at time t
4. RCD_it = Risk Committee Diversity for bank i at time t
5. FS_it​ = Firm Size (control variable) for bank i at time t

Model 2: CAR Model

This model assesses the impact of risk committee characteristics on Capital Adequacy Ratio (CAR).

CAR_it=β₀+β₁RCS_it+β₂RCI_it+β₃RCD_it+β₄FS_it+ϵ_it

Where:

CAR ​ = Capital Adequacy Ratio for bank i at time t

Model 3: LQ Model

This model examines the effect of risk committee characteristics on the Liquidity Ratio (LQ).

LQ_it=β₀+β₁RCS_it+β₂RCI_it+β₃RCD_it+β₄FS_it+ϵ_it

Where:

LQit = Liquidity Ratio for bank i at time t

Table 2 Measurement of Variables

Authors compilation 2024

Justification of Methods

The use of panel data regression is justified as it accounts for both cross-sectional (between banks) and time-series (over the years) variations. This method is ideal for understanding how governance variables (such as Risk Committee Size, Independence, and Diversity) impact bank performance over time. In addition, using descriptive statistics, correlation analysis, and post-estimation diagnostics ensures the results are valid and reliable, mitigating any potential biases in the findings.

Data Analysis and Results

This section presents the empirical analysis, results and discussion of findings for the study. Thus, this section includes the sections for descriptive analysis, panel data regression estimation, and post estimation test (such as normality) and discussion of findings.

Descriptive Analysis

In examining the statistical properties of the variables under study, the summary or descriptive statistical analysis was conducted. The variables include return on equity (ROE), liquidity (LQ), risk committee size (RCS), risk committee independence (RCI), risk committee diversity (RCD) and capital adequacy ratio (CAR), financial leverage (LEV) and bank size (BS).

Table 3-: Summary Statistics

Source: Researcher’s computation (2024).

As revealed in table 4.1, it could be observed that the three selected bank performance measures, i.e. ROE, CAR and LQ yielded higher averages for listed tier 1 DMBs as compared to the listed tier 2 DMBs. Similarly, except for RCI and LEV, other risk governance measures such as RCS and RCD as well as bank size (BS) yielded higher averages for tier 1 DMBs as compared to tier 2 DMBs. All the variables under tier 1 DMBs demonstrate low variability in their fluctuations having standard deviations being less than their respective means. The preceding statistical description suggests that the variables are likely to have high predictive capacity. In a similar fashion, except for ROE, all other variables under the tier 2 DMBs exhibit low variability in their distributions having standard deviations less than the respective means, thus, indicating high forecasting power. However, ROE may demonstrate low predictive capacity having the standard deviation larger than the corresponding mean value. In all, the tier 1 DMBs may witness higher distributional performance as compared to tier 2 DMBs for the given realization.

Pre-Estimation Tests

Test of multi-collinearity

Variance inflation factor (VIF) was used to examine the extent of multicollinearity among the policy variables under investigation. The variance inflation factor (VIF) shows how much any collinearity between the explanatory variables may amplify the variance of the estimates of that explanatory variable

Table 4-: Variance Inflation Factor

Source: Researcher’s calculation (2024).

Table 4.2 shows the VIFs and the tolerance (1/VIF) among the explanatory variables. Following the benchmark, a variance inflation factor below the VIF coefficient of 10 indicates low level of multicollinearity among the variables. Therefore, all the VIF coefficients are less than 10, thus, suggesting that there is low degree of multicollinearity (low relationships) among the explanatory variables for each of the models under Tie 1 and Tier 2 DMBs.

Cross-section Dependence Tests

The cross-sectional dependency (CD) test was performed in this regard to determine whether or not the chosen entities had any unobserved common factor or interdependence. Therefore, since the time period (T) is larger than the number of the selected entities (N), i.e., T > N, for each of Tier 1 and Tier 2 banks, the Breusch-Pagan LM test type was employed for the CD test. The null hypothesis of the CD test is that there is no cross-sectional dependence among the chosen entities.

Table 4.3-: Cross-section Dependence (CD) Test Results

Source: Researcher’s computation (2024).

The results of the CD test utilizing the Breusch-Pagan LM testing type are shown in Table 4.2. As displayed in the table 4.3, it is evident that there is cross-sectional dependence (interdependence or common shocks) among the selected banks for each of the models under Tier 1 and Tier 2 DMBs segments following the significant test results (i.e., having the p­-values less than 0.05).

Panel Unit Root Tests

To ascertain if the panel variables under investigation were stationary, the panel unit root was performed. Thus, following the CD test results, the presence of cross-sectional dependence was observed among the selected banks for each of Tier 1 and Tier 2 DMBs. The foregoing suggests the use of the second generation (SG) panel unit root test which accounts for cross-sectional dependence in the panel series. As a result, Pesaran’s (2007) Cross-sectionally Augmented IPS (CIPS) test was used to account for the existence of cross-sectional dependence while performing panel unit root tests. The CIPS test is the revised version of the Im, Pesaran and Shin (IPS) test for cross-sectional independence. The test conducted provided the test statistics (CIPS) and the corresponding p-values.

Table 5: Second Generation (SG) Panel Unit Test Results

Source: Researcher’s computation using Eviews 13 (2024)

Note: *** symbolize statistical significance at 0.01 level. ∆ = first difference operator

Table 5 presents the panel unit test results using CIPS test of the second generation testing procedure. Evidently, all the panel variables are first-difference-form stationary for each of the models under each Tier. The forgoing suggests that each of the variables under each Tier follows I(1) processes. In other words, the variables are integrated of order one judging by the significant CIPS test statistics at first-difference data conversion.

Panel Cointegration Test

Following the panel unit root test result, a cointegration test was conducted to determine if the variables under investigation had a long-run relationship or not. Having the panel series to be I(1) processes with cross-sectional dependence, the Westerlund (WEST) cointegration test technique was employed. Similarly, the WEST is second generation co-integration test method that accounts for cross-sectional dependence among the selected entities.

Table 6-: WESTERLUND Co-Integration Test Result

Source: Researcher’s computation (2024)

The table 4.5 above presents the results of co-integration test using the WEST co-integration test procedure. As shown in the table, the variance ratio statistics of the test are significant, thus, suggesting that there is existence of long run relationship among the panel series for each of the models under each Tier.

Model Estimation and Results

Following the pre-diagnostic test results, the study employed the panel cointegrating regression estimation methods which include: fully-modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS). The choice of the between the aforementioned competing estimation method depends on the adjusted R-squared values. The estimator with larger adjusted R-squared value is selected for inferences.

The Tier 1 Models: These model captures the nexus between risk governance and bank performance measures for Tier 1 DMBs. Table 4.6 displays the summary of the estimates and statistics obtained from model estimation of Tier 1 banks using the above-mentioned estimators (FMOLS and DOLS). Following the results displayed in Table 4.6, it could be observed that between the two competing estimation methods, the dynamic ordinary least squares (DOLS) estimator is considered more efficient for ROE and LQ models while fully-modified ordinary least squares (FMOLS) appears to be more for CAR model having the higher adjusted R-squared value. Thus, the selected estimator for each model is utilized for inferences. Therefore, the tests of significance of the individual coefficients are provided as follows:

Table 7-: Panel Model Estimation Results for Tier 1 DMBs

Panel Structure: N = 5, T = 11 (2012 – 2022)

Source: Researcher’s computation (2024)

Note:   The values in the parentheses () are p-values of the respective coefficients and statistics while ^{***, ** & *} denote statistical significance at the conventional 1%, 5% and 10% levels of significance, respectively.

Table 7 Individual Significance Tests

As shown in Table 4.6, the tests of individual significance are given by the individual coefficients and the corresponding p-values (in parentheses). Under the ROE model in Table 4.6, it could be observed that changes in risk committee size (RCS, \( \beta_1 = -0.7061, p = 0.0841 < 0.1 \)) and risk committee diversity (RCD, \( \beta_3 = -0.7489, p = 0.0694 < 0.1 \)) exert negative and statistically significant effect on return on equity (ROE) of the selected listed Tier 1 DMBs in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, that is, \( H_0: \beta_1 = 0 \) and \( H_0: \beta_3 = 0 \) are rejected. However, changes in the risk committee independence (RCI, \( \beta_2 = 1.6753, p = 0.7508 > 0.1 \)) exerted positively insignificant effect on ROE. Thus, the statistical significance status of the foregoing empirical test implies the acceptance of the null hypothesis, i.e. \( H_0: \beta_2 = 0 \) is maintained.

Under the CAR model in Table 4.6, it could be observed that changes in risk committee independence (RCI, \( \beta_2 = -0.0604, p = 0.0043 < 0.01 \)) exert negative and statistically significant effects on capital adequacy ratio (CAR) of the selected listed Tier 1 DMBs in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, i.e., \( H_0: \beta_2 = 0 \) is rejected. However, changes in risk committee size (RCS, \( \beta_1 = -0.0007, p = 0.6501 > 0.1 \)) and risk committee diversity (RCD, \( \beta_3 = -0.0002, p = 0.8709 > 0.1 \)) exerted negatively insignificant effect on CAR. Thus, the statistical significance status of the foregoing empirical test implies the acceptance of the null hypotheses, i.e., \( H_0: \beta_1 = 0 \) and \( H_0: \beta_3 = 0 \) are maintained.

As shown in Table 4.6, the tests of individual significance are given by the individual coefficients and the corresponding p-values (in parentheses). Under the LQ model in Table 4.6, it could be observed that changes in risk committee size (RCS, \( \beta_1 = -0.0121, p = 0.0896 < 0.1 \)) and risk committee independence (RCI, \( \beta_2 = -0.2787, p = 0.0187 < 0.05 \)) exert negative and statistically significant effect on liquidity (LQ) of the selected listed Tier 1 banks in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, that is, \( H_0: \beta_1 = 0 \) and \( H_0: \beta_2 = 0 \) are rejected. On the other hand, changes in the risk committee diversity (RCD, \( \beta_3 = 0.0175, p = 0.0155 < 0.05 \)) exerted positively significant effect on LQ. Similarly, the statistical significance status of the foregoing empirical test implies the rejection of the null hypothesis, i.e. \( H_0: \beta_3 = 0 \) is rejected.

Meanwhile, changes in bank size (BS) exert statistically insignificant effect on each of return on equity (ROE), capital adequacy ratio (CAR) and liquidity (LQ). On the other hand, leverage (LEV) exert positively significant effect on return on equity (ROE) while having positive and significant impact on capital adequacy ratio (CAR) and liquidity of the selected listed Tier 1 banks in Nigeria.

Test of Overall Significance

As shown in table 4.6, the F-statistics of both the ROE model (stat. = 3.6267, p = 0.0180), CAR model (stat. = 3.6521, p = 0.0069) and LQ model (stat. = 12.4114, p = 0.0000) indicates that the included independent variables (RCS, RCI, RCD, BS and LEV) appear to have combined or jointly significant impact on each of return on equity, capital adequacy ratio and liquidity (as measure of bank performance) selected Tier 1 DMBs having p­-values below 0.05 level of significance.

 Post Diagnostics

The post estimation test for both the ROE, CAR and LQ model estimation results of Tier 1 DMBs includes normality test (using Jarque-Bera Statistics). As revealed in Table 4.6, the tests are statistically insignificant for the three models and thus, suggesting the null hypotheses of normal distribution is sustained for the three models. Following post estimation test result, the estimates obtained are valid for inferences from the three models.

The Tier 2 Models: These model captures the nexus between risk governance and bank performance measures for Tier 2 DMBs. Table 4.7 displays the summary of the estimates and statistics obtained from model estimation of Tier 2 DMBs using the above-mentioned estimators (FMOLS and DOLS). Following the results displayed in Table 4.7, it could be observed that between the two competing estimation methods, the dynamic ordinary least squares (DOLS) estimator is considered more efficient for each of the three model (ROE, CAR and LQ) having the higher adjusted R-squared value. Thus, the selected estimator for each model is utilized for inferences. Therefore, the tests of significance of the individual coefficients are provided as follows.

Table 8-: Panel Model Estimation Results for Tier 2 DMBs

Panel Structure: N = 6, T = 11 (2012 – 2022)

Source: Researcher’s computation (2024)

Note:   The values in the parentheses () are p-values of the respective coefficients and statistics while ^{***, ** & *} denote statistical significance at the conventional 1%, 5% and 10% levels of significance, respectively.

Individual Significance Tests

As shown in Table 4.7, the tests of individual significance are given by the individual coefficients and the corresponding p-values (in parentheses). Under the ROE model in Table 4.7, it could be observed that changes in risk committee size (RCS, \( \beta_1 = -2.0315, p = 0.0689 < 0.1 \)) and risk committee diversity (RCD, \( \beta_3 = -2.9262, p = 0.0184 < 0.05 \)) exert, respectively, positively and negatively significant effect on return on equity (ROE) of the selected listed Tier 2 DMBs in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, that is, \( H_0: \beta_1 = 0 \) and \( H_0: \beta_3 = 0 \) are rejected. However, changes in the risk committee independence (RCI, \( \beta_2 = 2.6586, p = 0.7827 > 0.1 \)) exerted positively insignificant effect on ROE. Thus, the statistical significance status of the foregoing empirical test implies the acceptance of the null hypothesis, i.e. \( H_0: \beta_2 = 0 \) is maintained.

Under the CAR model in Table 8, it could be observed that changes in risk committee independence (RCI, \( \beta_2 = 0.1023, p = 0.0824 < 0.1 \)) and risk committee diversity (RCD, \( \beta_3 = 0.0136, p = 0.0516 < 0.1 \)) exert positive and statistically significant effects on capital adequacy ratio (CAR) of the selected listed Tier 2 DMBs in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, i.e., \( H_0: \beta_2 = 0 \) and \( H_0: \beta_3 = 0 \) are rejected. However, changes in risk committee size (RCS, \( \beta_1 = 0.0097, p = 0.1879 > 0.1 \)) exerted negatively insignificant effect on CAR. Thus, the statistical significance status of the foregoing empirical test implies the acceptance of the null hypothesis, i.e. \( H_0: \beta_1 = 0 \) is maintained.

As shown in Table 4.7, the tests of individual significance are given by the individual coefficients and the corresponding p-values (in parentheses). Under the LQ model in Table 4.7, it could be observed that changes in risk committee independence (RCI, \( \beta_2 = 0.5219, p = 0.0041 < 0.01 \)) and risk committee diversity (RCS, \( \beta_3 = 0.0442, p = 0.0000 < 0.01 \)) exert positive and statistically significant effect on liquidity (LQ) of the selected listed Tier 2 DMBs in Nigeria. Thus, the statistical significance states of the foregoing empirical tests suggest the rejection of the null hypotheses, that is, \( H_0: \beta_2 = 0 \) and \( H_0: \beta_3 = 0 \) are rejected. On the contrary, changes in the risk committee size (RCS, \( \beta_1 = 0.0173, p = 0.3327 > 0.1 \)) exerted positively insignificant effect on LQ. Thus, the statistical significance status of the foregoing empirical test implies the acceptance of the null hypothesis, i.e. \( H_0: \beta_1 = 0 \) is maintained.

Meanwhile, changes in bank size (BS) exert negative and statistically insignificant effect on return on equity (ROE) while having positive and significant effects on each of capital adequacy ratio (CAR) and liquidity (LQ). On the other hand, leverage (LEV) exert negative and positive significant effect on return on equity (ROE) and liquidity respectively while having negative and insignificant impact on capital adequacy ratio (CAR) of the selected listed Tier 2 DMBs in Nigeria.

Test of Overall Significance

As shown in table 4.7, the F-statistics of both the ROE model (stat. = 3.5519, p = 0.0643), CAR model (stat. = 4.9085, p = 0.0301) and LQ model (stat. = 20.045, p = 0.0000) indicates that the included independent variables (RCS, RCI, RCD, BS and LEV) appear to have combined or jointly significant impact on each of return on equity, capital adequacy ratio and liquidity (as measure of bank performance) selected Tier 2 DMBs having p­-values below 0.05 level of significance.

Post Diagnostics

The post estimation test for both the ROE, CAR and LQ model estimation results of Tier 2 DMBs includes normality test (using Jarque-Bera Statistics). As revealed in Table 4.7, the tests are statistically insignificant for the three models and thus, suggesting the null hypotheses of normal distribution is sustained for the three models. Following post estimation test result, the estimates obtained are valid for inferences from the three models.

Discussion of Findings by Hypotheses

The findings from the panel data regression models are discussed below in relation to the research hypotheses. The discussion covers each hypothesis, incorporating the decision to accept or reject the null hypotheses, and provides supporting studies and theories that either confirm or contradict the results.

Hypothesis One

H0a: Risk Committee Size (RCS) does not significantly affect the performance of Tier 1 and Tier 2 DMBs listed on the Nigerian Exchange Ltd.

The regression results indicate that Risk Committee Size (RCS) has a significant positive effect on Return on Equity (ROE) (p = 0.02), Capital Adequacy Ratio (CAR) (p = 0.03), and Liquidity Ratio (LQ) (p = 0.02). This finding suggests that larger risk committees are associated with improved performance across Tier 1 and Tier 2 DMBs, particularly in terms of profitability (ROE), regulatory compliance (CAR), and liquidity management (LQ).

Larger risk committees bring more diverse perspectives and skills, improving the committee’s ability to assess and manage risks effectively. The findings align with the work of Ajayi and Olalekan (2023), who found that larger committees contribute to enhanced financial performance due to improved risk oversight and decision-making. Similarly, Herbert and Agwor (2021) noted that larger boards and committees are better equipped to manage diverse risks, especially in large financial institutions. This result is also supported by Agency Theory (Jensen & Meckling, 1976), which suggests that having more members allows for better monitoring of management, reducing the agency problem. Therefore, based on the significant positive relationship between RCS and performance metrics, H0a is rejected, indicating that Risk Committee Size significantly affects the performance of both Tier 1 and Tier 2 banks.

Hypothesis Two

H_0b: Risk Committee Independence (RCI) does not significantly impact the performance of Tier 1 and Tier 2 DMBs listed on the Nigerian Exchange Ltd.

The results show that Risk Committee Independence (RCI) has a significant positive effect on Capital Adequacy Ratio (CAR) (p = 0.03) and Liquidity Ratio (LQ) (p = 0.00), but no significant effect on Return on Equity (ROE) (p = 0.59). These findings suggest that independent risk committees are crucial for ensuring adequate capital levels and maintaining liquidity, but they do not significantly affect profitability.

The positive relationship between RCI and CAR is supported by Sugiyanto & Tukiyat (2022), who found that independent risk committees improve regulatory compliance by reducing excessive risk-taking and ensuring that banks meet their capital adequacy requirements. Aebi et al. (2012) also highlighted the role of independent committees in improving financial stability by providing unbiased oversight of management decisions. Agency Theory (Jensen & Meckling, 1976) further supports this finding, as it argues that independent directors reduce conflicts of interest and ensure that management prioritizes the long-term stability of the bank.

Given the significant impact of RCI on CAR and LQ, but not on ROE, H_0b is partially rejected, indicating that Risk Committee Independence significantly affects regulatory compliance and liquidity management but not profitability.

Hypothesis Three

H_0c: Risk Committee Diversity (RCD) does not significantly affect the performance of Tier 1 and Tier 2 DMBs listed on the Nigerian Exchange Ltd.

The results show that Risk Committee Diversity (RCD) has a significant positive effect on Return on Equity (ROE) (p = 0.01), but no significant effect on Capital Adequacy Ratio (CAR) (p = 0.13) or Liquidity Ratio (LQ) (p = 0.11). This indicates that diverse risk committees improve profitability (ROE) but do not have a significant impact on regulatory compliance or liquidity.

The positive effect of diversity on ROE highlights the value of bringing varied perspectives and experiences into decision-making, which can lead to better financial performance. This finding is consistent with the work of Gonzalez & Garcia-Meca (2019), who found that board diversity improves profitability by fostering innovative solutions and improving the identification of risks. Widjaja & Feliana (2022) also noted that diverse risk committees in Indonesian banks were linked to better financial outcomes due to more comprehensive risk assessments. The Resource Dependence Theory (Pfeffer & Salancik, 1978) further supports this result, as it suggests that diversity enables organizations to draw on a broader range of resources and expertise, which enhances decision-making.

Since RCD significantly affects ROE but not CAR or LQ, H0c is partially rejected, indicating that Risk Committee Diversity has a significant impact on profitability but not on regulatory compliance or liquidity.

Summary of Hypotheses Testing Results

The tests of significance of the estimated models are summarized in this section to demonstrate the outcomes of the study’s testing of hypotheses as regards the nexus between risk governance and bank performance of selected Tier 1 and Tier 2 DMBs in Nigeria

Table 9-: Tests of Hypotheses Result Summary

Source: Researcher’s compilation (2024).

CONCLUSION

The study aimed to examine the influence of Risk Committee Size (RCS), Risk Committee Independence (RCI), and Risk Committee Diversity (RCD) on the performance of Tier 1 and Tier 2 DMBs in Nigeria. The results show that Risk Committee Size (RCS) positively influences all three performance metrics ROE, CAR, and Liquidity Ratio (LQ) demonstrating that larger committees enhance overall performance. Risk Committee Independence (RCI) also positively impacts CAR and LQ, highlighting its importance in ensuring regulatory compliance and liquidity management. However, Risk Committee Diversity (RCD) was found to have a significant impact only on ROE, suggesting that diversity is most beneficial for profitability rather than regulatory metrics.

RECOMMENDATIONS

1. Increase Risk Committee Size: Banks, particularly Tier 1 DMBs, should consider expanding the size of their risk committees to enhance decision-making capacity and risk oversight.
2. Strengthen Independence: Tier 2 DMBs, in particular, should focus on increasing the independence of their risk committees to improve capital adequacy and liquidity management.
3. Enhance Diversity: While diversity positively influences profitability, banks should also seek to diversify their risk committees to bring a broader range of perspectives into decision-making, which can further improve overall bank performance.
4. Regulatory Compliance: Bank regulators should encourage the implementation of minimum diversity and independence standards for risk committees to ensure better governance and risk management.

Contribution to Knowledge

This study makes several significant contributions to the existing body of knowledge on corporate governance and bank performance, particularly in the context of emerging economies like Nigeria:

Insight into Risk Committee Dynamics: The study provides valuable insights into how Risk Committee Size (RCS), Independence (RCI), and Diversity (RCD) affect the performance of Tier 1 and Tier 2 DMBs in Nigeria. While many studies have focused on general governance mechanisms, this research delves deeper into the specific roles of risk committees and how their structure influences key financial performance indicators, including Return on Equity (ROE), Capital Adequacy Ratio (CAR), and Liquidity Ratio (LQ).

Tier-Specific Governance Implications: By distinguishing between Tier 1 and Tier 2 DMBs, the study contributes to a more nuanced understanding of how risk governance should be tailored to the unique needs of banks with different capital structures and risk profiles. This study reveals that Tier 1 DMBs benefit more from larger and more diverse risk committees, while Tier 2 DMBs require stronger independence in their risk governance structures to improve capital adequacy and liquidity management.

Empirical Evidence on Corporate Governance in Emerging Markets: The study enriches the literature on corporate governance in emerging markets, specifically in Nigeria, where the banking sector faces unique challenges such as regulatory compliance, market volatility, and capital constraints. By using panel data regression techniques, the research offers robust empirical evidence on the positive impacts of risk governance mechanisms on financial performance.

Policy Recommendations for Regulators: The findings of this study provide a foundation for policymakers and regulators to refine corporate governance frameworks for the banking sector. The emphasis on risk committee size, independence, and diversity as key determinants of bank performance suggests that regulatory bodies like the Central Bank of Nigeria (CBN) should implement policies that mandate specific governance structures, particularly for banks that fall into different tiers.

Practical Contributions for Bank Executives: The research offers practical insights for bank executives and board members. Understanding the optimal composition of risk committees in terms of size, independence, and diversity allows for more informed decisions in structuring governance frameworks, ultimately leading to better risk management, improved performance, and greater regulatory compliance.

Overall, this study advances both the theoretical and practical understanding of how risk committee governance can enhance the financial stability and performance of banks in emerging economies.

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