THE CAPITAL ADEQUACY RATIO MODERATED ENTERPRISE RISK MANAGEMENT ON FINANCIAL DISTRESS

This research delves into the intricate dynamics of financial risks—specifically credit, market, and operational risks—within the banking, investment, and corporate sectors, with a focus on both global and Indonesian contexts. By examining the key factors contributing to credit risk, the impact of global market volatility on financial stability, and the operational risks associated with the digital transformation of the financial sector, the study seeks to offer a comprehensive analysis that is both theoretically robust and practically relevant. This research employs a qualitative systematic literature review (SLR) to explore credit, market, and operational risks within the banking, investment, and corporate sectors, focusing on global and Indonesian contexts. The SLR process includes formulating research questions, identifying and screening relevant literature from databases like Scopus and Google Scholar, and synthesizing findings into key themes: credit risk dynamics, market volatility, operational risks in the digital age, and integrated risk management. This research provides a comprehensive analysis of financial risk management in the banking, investment, and corporate sectors, with a focus on Indonesia and global perspectives. The study reveals that digitalization has a significant impact on operational risk, enhancing efficiency but also increasing vulnerability to cybersecurity threats and disruptions. This underscores the need for robust risk management frameworks to address technology-driven challenges. The research also highlights the importance of improving risk disclosure transparency, which can positively influence credit risk management. Liquidity risk is identified as having a greater short-term impact on financial stability than credit risk, necessitating proactive liquidity management strategies. Technological innovations in finance are found to correlate with increased risks, including failures and cybersecurity threats, which must be carefully managed. The study examines the risks associated with platform-based financing models and the influence of global market volatility on investment strategies. In Indonesia, the banking sector faces distinct credit risk challenges due to high market concentration and systemic shocks, as well as operational risks from rapid digital transformation. The research emphasizes the necessity for Indonesian financial institutions to implement comprehensive cybersecurity measures, maintain resilient IT infrastructure, and utilize advanced monitoring tools to address these emerging risks. The study also stresses the importance of adopting integrated risk management frameworks that account for the interdependencies between credit, market, and operational risks in a globalized market.

Purpose: To determine the effect of capital adequacy ratio, market risk, credit risk, liquidity risk, operational risk, bank size, and other variables on banking profitability in Indonesia. Methodology: Panel data regression analysis; purposive sampling; capital adequacy ratio, market risk, credit risk, liquidity risk, operational risk, bank size as instruments; 39 banks listed on Indonesia Stock Exchange; return on assets as dependent variable. Results: Capital adequacy ratio and bank size have a positive and significant influence on profitability. Credit risk and operational risk have a significant negative effect. Market risk and liquidity risk have no significant effect. Limitations: The study is limited to data from banks listed on the Indonesia Stock Exchange between 2018-2024, so it may not reflect conditions outside this period or region. Contribution: This research is useful in the fields of banking management, corporate finance, and investment strategy.

This research aims to analyze the determinants of credit risk factors, operational risk, liquidity risk, market risk, governance (GCG), earning, capital, bank size, and core capital that have a partial or simultaneous influence on the profitability of Bank Pembangunan Daerah (BPD) in Sulawesi for the 2016-2020 period using the risk-based bank rating (RBBR) method with a quantitative descriptive approach. Data collection used an annual financial report published through each bank at 4 (four) regional banks headquartered in Sulawesi. The factors tested are credit risk with NPL (Non-Performing Loan) proxy, an operational risk with BOPO proxy (Operational Income Operating Expenses ratio), liquidity risk with LDR (Loan to Deposit Ratio) proxy, a market risk with PDN ratio proxy (Position Net Foreign Exchange), governance (GCG), profitability with NIM (Net Interest Margin) proxy, capital with CAR (Capital Adequacy Ratio) proxy, bank size as proxy for total assets, core capital ratio (MI) and profitability with profitability return proxies on assets (ROA). Based on the results of the Simultaneous F test research, shows that credit risk, operational risk, liquidity risk, market risk, governance (GCG), profitability, capital, bank size, and core capital simultaneously have a significant influence on profitability for the 2016-2020 period with the profitability value of F-statistic is smaller than alpha (0.05) which is 0.0000 <0.05. The results of the Partial T-test show that there are 6 (six) factors, each of which has a partial influence on ROA profitability, namely NPL, BOPO, LDR, NIM, CAR, and total assets with a probability value of t-statistic smaller than alpha (0.05 ) that is equal to 0.0000 <0.05 and 3 (three) factors, each of which has no partial influence on ROA profitability, namely governance (GCG), PDN and MI probability value of t-statistic is greater than alpha (0.05 ) that is equal to 0.0000 > 0.05.
 Keywords: Risk Profile, Good Corporate Governance, Earning, Capital, and Risk-Based Bank Rating (RBBR)

At its core, commercial banking is a business deeply entrenched in the dynamics of risk and reward. Financial risks, encompassing credit, liquidity, price and operational risks, serve as inherent components of banking activities. These risks are not only omnipresent but also inherently interconnected, creating a complex web that directly influences financial performance of commercial banks. The purpose of the study was to determine the effect of financial risk on financial performance of listed commercial banks in Kenya. The study was grounded on extreme value theory, credit risk theory, asymmetry information theory and liquidity preference theory. Correlational research design was adopted in the study as it helps researcher explore and explain the effects of financial risks on the financial performance of commercial banks. Nairobi Securities Exchange (2023) indicates that there are 10 commercial banks listed in NSE as of 31st December 2023. The target population consisted of all 10 listed commercial banks in Kenya which formed the unit of analysis. Time Series secondary panel data was utilized. The data was obtained from published financial statements of 10 listed commercial banks, and CBK bank supervision reports from 2019 to 2023.The coefficient of determination (R2) was used to determine how much variation in dependent variable is explained by explanatory variables. Statistical Package for Social Science was used as data analysis tool. Results showed that credit risk, price risk and operating risk had negatively and significant effect on the financial performance of commercial banks in Kenya. However, liquidity risk had negative and insignificant effect on financial performance of listed commercial banks in Kenya. Also, majority of listed commercial banks had adequate liquidity to cover loans in the event of an economic downturn resulting in loan defaults. It was concluded that credit risk had a negative and statistically significant relationship with financial performance of commercial banks listed in NSE. This implies that when non-performing loans are increasing, performance is likely to be going down. It was also concluded that liquidity risk had a negative and insignificant effect on financial performance of commercial banks listed in NSE. . The study recommended that the managers of the bank to adopt the policies that will ensure debtors ratios does not increase at high ratios in relation to the total capital since this amounts to credit risk. The managers should minimize the credit risk by ensuring the credit worthiness of the clients is critically evaluated with collateral

To reinforce the stability of the financial system, policy makers and the Basel committee have proposed Basel accord to ensure that financial institutions maintain sufficient capital buffers. Basel III framework emphasizes on sustained increase in bank capital in order to absorb the potential credit, market and operational risks. The capital adequacy requirement under Basel III norms are directly linked to the PCA (Prompt Corrective action) framework which has disrupted the flow of credit in the economy. Market risk, Credit risk, Operational risk and deposits are some of the factors affecting the capital adequacy ratio (CAR) which influences the bank performances. This study aims at analysing the most important factor responsible for the shrinking liquidity due to adherence of stringent capital adequacy ratio imposed by RBI. Currently 11 public sector Banks out of 21 PSUs under PCA has sequentially shrunk their loan book including UCO Bank. The bank’s asset quality has worsened over the years. Using regression analysis, this paper seeks to study the major determinants of Capital Adequacy ratio using data sets for the period from 2009 to 2018 of UCO bank. The data was collected from the financial reports of the UCO bank for the aforesaid period. Among the parameters considered, it was found that deposits affect the CAR the most and market risk has the lowest impact on CAR.

Kenya commercial banks’ financial performance is influenced by myriad of challenges that can impact their profitability, operational efficiency and overall stability. Therefore, this review endeavored to ascertain financial risks impacts on Kenya’s commercial banks financial performance, specifically targeting operational, credit, liquidity and market risk impacts. Theories of Miller and Modigliani, financial distress, financial intermediation and modern portfolio underpinned the review. Employing descriptive research, all 22 tier III banks formed the target populace and census was used. Financial data was collated from existing published statements using secondary designated collection sheet from 2019-2023. Data collected was analyzed using descriptive techniques (mean, median and standard deviation) and inferential statistics (multiple regression. The study revealed that operational, credit, liquidity and market risks had a positive significant effect on Tier III Kenya’s commercial banks’ financial performance. The research concludes that operational risks have the potential to result in operational failures, which may incur additional costs for mitigation and compliance, thereby placing financial strain on banks. The credit risk may occur in a situation whereby the bank does not effectively evaluate the borrower’s creditworthiness resulting to increased number of loan defaulters which also leads to more provisioning costs for bad debts affecting negatively the bank's profitability. The existence of liquidity risk can hinder a bank's ability to effectively manage its liquidity, leading to increased costs related to borrowed funds necessary to meet its obligations, which ultimately diminishes profit margins and impacts the bank's overall financial performance. Fluctuations in interest rates have an impact on the net interest income of banks, which constitutes their revenue stream. This can lead to a reduction in their ability to adapt to such changes, thereby influencing their financial performance. The research suggests that Tier III banks ought to improve their investment in technology by upgrading their information technology systems and implementing more advanced security protocols to reduce the risk of cyber threats. The Tier III banks should adopt a diversified loan portfolio to minimize more reliance of certain industries that could bring higher risks upon economic fluctuations. Tier III banks can properly manage liquidity risks through maintenance of a wider range of financing base such as mix of deposits, loans and other sources of capital. The Tier III banks should adopt a comprehensive structure managing risks, carry out a frequent stress test and have a more diversified lending portfolio to solve the possible losses.

Risks and losses arising from system failure, unautho-rized activity, fraud and other operational errors are postulated to be one of the primary banking risks. Ex-pert-based scenario analysis aims at describing the fre-quency and severity of extreme operational losses. Ex-perts are not exact in predicting quantitative estimates for a distant future. The predicted scenarios are de-scribed by range estimates and qualitative storylines. This study introduces and applies the methodology for evaluation of the operational risk levels derived from the experts opinions collected from scenarios. The me-thodology is based on the use of fuzzy numbers to ex-press subjective probability of expert estimates. Keywords : operational risk, scenario analysis, fuzzy decision making, fuzzy number 1. Introduction In the light of the global financial crisis, both media, leading policy makers, economists and practitioners are questioning the appropriate levels of risk in the interna-tional financial system. Decision makers and banking practitioners are struggling to evaluate the risk attri-buted to the globalization of modern banking, the fast evolving information technology, new economic theo-ries, opaque financial instruments, and complex ma-thematical modeling. International banking industry had been trying to balance the existing risks in the financial systems for quite some time by regulating the ap-proaches to measure and manage risks. Although the latest financial crisis made us question some regulatory requirements, it had accentuated the necessity to eva-luate risks more holistically. The Bank for International Settlements, an interna-tional organization which fosters international monetary and financial cooperation and serves as a bank for cen-tral banks, had realized that developing banking prac-tices such as securitization, outsourcing, specialized processing operations and reliance on technology had introduced substantial risks which need to be reflected in credible capital assessments. As a result, Basel II Ac-cord that includes risks other than credit and market was proposed to be “a comprehensive framework for improving bank safety and soundness by more closely linking regulatory capital requirements with bank risk, by improving the ability of supervisors and financial markets to assess capital adequacy”, according to Fed-eral Reserve Chairman Ben S. Bernanke in a May 2006 speech at the Federal Reserve Bank of Chicago’s 42nd Annual Conference on Bank Structure and Competition. All the largest banks in the world that carry internation-al component are mandated to become Basel certified. Resulting international risk management practice for financial institutions focuses on three main risk catego-ries: Market Risk, Credit Risk and Operational Risk (OR). OR is a relatively newly defined category of risk which includes many different types of risk, from the simple operations risks of transaction processing, unau-thorized activities and systems risks to other types of risk that are not included in market or credit risks, such as human, legal, informational and reputational risks with the exception of strategic risk [2]. By contrast with credit and market risks where acceptable models and measurements exist, it is relatively difficult to identify and evaluate levels of operational risk due to the com-plexity of its origin, lack of existing historic quantita-tive data and explanation. One of the best publicized OR risk events are rouge trading in the French Societe Generale bank, BP oil spill, and American Maddoff “ponzi” scheme causing billions of dollars to a global financial system. Scenario analysis as a decision making tool has been in existence for several decades and has been used in various disciplines, including management, engineer-ing, defense, medicine, finance and economics [6]. Sce-nario analysis is one of the four required data elements that banks must incorporate into their Advanced Model-ing Approach to adequately measure their operational risk levels. Scenario analysis brings a forward looking element into OR assessment since it describes future plausible severe OR loss events. The current range of practice published in 2009 by Basel Committee of Banking Supervision [16] identifies a lack of consistent controls to address scenario analysis bias noting that scenario analysis is subjective in nature. Chosen and estimated scenarios become an effective technique for stimulating management to identify key strategic opera-tional risks facing their businesses. Scenario workshops are used as the preferred method to develop a set of scenarios. During scenario workshop, experts are sup-plied with a library of plausible extreme operational events that have or may have happened in peer financial organization and are asked to assess the likelihood and severity of similar events if they occur in their business, as described by the Bank of Japan [25]. Scenarios as a framework offer the ability to bring qualitative business intuition data into a systematic assessment of OR which is increasingly important in an era of stress testing of international financial sector. Making point estimates in

This study discusses the effect of risk and capital on profitability of banks issued on the Indonesia Stock Exchange in the period of 2013-2017. The data used is panel data, which is a combination of time series data and cross section data. The method of taking data uses purposive sampling. Technical analysis was performed using multiple regression analysis. The results of the study show that credit risk (NPL) and operational risk (BOPO) have a negative and significant effect on profitability (ROA). While liquidity risk (LDR), market risk (NIM), and capital (CAR) do not affect on profitability (ROA). Furthermore credit risk (NPL), operational risk (BOPO) and capital (CAR) have a negative and significant effect on profitability (ROE), while liquidity risk (LDR) and market risk (NIM) do not affect on profitability (ROE).
 Keywords: profitability, liquidity risk, credit risk, market risk, operational risk and capital.

ECONOMISTS have long agreed that the rate of interest on a loan depends on the risks the lender incurs. But how lenders estimate these risks has been left largely to conjecture. This paper presents and tests a hypothesis about the determinants of risk premiums on corporate bonds. By risk premium is meant the difference between the market yield on a bond and the corresponding pure rate of interest. My hypothesis is as follows: (1) The average risk premium on a firm's bonds depends first on the risk that the firm will default on its bonds and second on their marketability. (2) The "risk of default" can be estimated by a function of three variables: the coefficient of variation of the firm's net income over the last

Risk management is part of a comprehensive business strategy with the aim of contributing to protecting and increasing shareholder value. An increase in stock value indicates an increase in stock returns obtained by investors. This study examines the effect of risk management implementation on bank stock returns as seen from the bank book group, namely bank book group 1, bank book group 2, bank book group 3 and bank book group 4. The application of risk management is seen from credit risk, liquidity risk, risk. operational and market risk. The research was conducted on all commercial banks that went public and were active from 2015 to 2019, as many as 44 banks. The results of the study state that: overall (for all bank book groups) the application of risk management, namely credit risk, liquidity risk, operational risk and market risk does not affect stock returns, except for bank book group 1, credit risk and operational risk and market risk for book group 4 has a significant effect on stock returns.

This study examines the hierarchical structure of risks influencing the financial distress of banks using the Interpretive Structural Modeling (ISM) method. Financial distress, regarded as the stage preceding bankruptcy, arises from the complex interaction of internal and external factors affecting banks and has far-reaching implications for financial stability and economic growth. In this regard, key risk components—including liquidity risk, credit risk, market risk, and operational risk—were first identified, and their interrelationships were analyzed using ISM. The research findings revealed that the ratio of net profit to total bank assets and the ratio of net working capital to total bank assets, with impact coefficients of 99% and 94%, respectively, play the most significant roles in reducing the financial distress risk of listed banks. Furthermore, market risk, the market-to-book value ratio, and the total debt-to-total assets ratio, each with an impact coefficient of 91%, showed a similar effect in mitigating this risk. Other variables, such as operational risk, interest rate risk, net profit to equity, and total asset size, also demonstrated approximately 80% influence and ranked in subsequent positions. The power–dependence analysis showed that liquidity risk, with 100% driving power and 17% dependence, is positioned in the independent zone, whereas credit risk and operational risk, characterized by high driving power and medium dependence, are situated in the linkage zone. Variables such as net profit, working capital, and market risk, with 100% dependence but low driving power, are placed in the dependent zone.

This study uses GCG self-assessment as a moderating variable to investigate how risk management, in particular operational, credit, and liquidity risk, affects financial performance. utilizing secondary and quantitative data from the websites of the Financial Services Authority (OJK) and each Rural Bank (BPR) for the 2019–2023 timeframe. Purposive sampling was the approach utilized to acquire the study's sample. There are 17 rural banks included in the sample size. The Structural Equation Modeling (SEM) approach and the SmartPLS 3.2.9 analysis tool are used in research data processing. The findings of the study demonstrate that operational risk significantly and negatively impacts financial performance. Liquidity risk has a positive but not significant effect on financial performance, while credit risk has a negative impact. This study also discovered that while GCG self-assessment was able to moderate the association between credit risk and financial performance, it was unable to moderate the relationship between financial performance and operational risk or liquidity risk.

This study was conducted on five digital banks in Indonesia, examining the effects of third-party funds (TPF) and bank risks, including credit, market, and operational risks, on profitability. This research employs a quantitative-explanatory approach and uses 96 observational data from five Indonesian digital banks' websites using quarterly financial data from 2019-2023. This study presents novel empirical evidence that the CAR has a positive effect on digital banks' profitability (ROA), and operational risk (OCOI) has a negative effect on profitability (ROA). Using panel data regression, this study finds that TPF and market risks do not significantly affect profitability. The study underscores the crucial role of digital banks' operational and credit risks in profitability while also revealing that TPF and market risk are not among the main drivers of digital banks' profitability.

The study investigates the role of International Basel Capital Regulation in taming the financial capital via improving risk management in banking. The study examines if capital adequacy ratios of commercial banks calculated under Basel Capital Accord reflect credit risk, market risk, operational risk, liquidity risk, and economic impact in Pakistan. The study employed dual methodology utilising both primary and secondary data. A Liker-scale questionnaire was administered in addition to deploying panel data approach. The results of the study show that Credit risk and operational risk along with the size of bank and bank profitability show significant impact on capital requirements of the commercial banks of Pakistan. Credit risk showing significant negative relationship with the capital adequacy ratio of the commercial banks of Pakistan.

This study examines the effect of credit risk, market risk, operational risk, and liquidity risk on profitability of banks listed on the Indonesia Stock Exchange in 2010-2014. The method used is descriptive and verification methods, with a sample of 30 banks and using multiple regression analysis. The results showed that credit risk does not partially affect profitability. Market risk, operational risk, and liquidity risk partially have positive effect on profitability. It simultaneously shows that credit risk, market risk, operational risk and liquidity risk have effect on the profitability of banks amounted to 67.1%. Improvement of Non-Performing Loan, Net Interest Margin, Operating Expenses to Operating Income Ratio, and Loan to Deposit Ratio will increase the Profitability.