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The Impact of Credit Risk Management on the Profitability of Commercial Banks

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Research Context and Credit Risk Management Foundations

Economic and Banking Context of Credit Risk Management

Economic growth is greatly aided by the efforts of the financial industry (Alshatti, 2015). Financial institutions, especially banks, play a crucial role in every economy, with their various local and foreign branches and subsidiaries. Commercial banks, in particular, are crucial to the distribution of funds in many nations. Continually, they serve to transfer money from savers to stockholders. This, however, is only doable if they are able to earn enough cash to meet their operating expenses. To sum up, commercial banks' ability to mediate economic transactions depends on their financial viability.

Commercial banks in Europe have a disproportionate share of the overall financial industry assets. Commercial banks, like their counterparts in other countries, primarily serve the public by providing credit Ogboi and Unuafe (2013), which also happens to be the primary source of income for the banking industry. It's crucial to keep in mind, though, that not all banks are the same. In addition, banks confront several hazards in the course of their daily operations. Credit risk, interest rate risk, foreign currency risk, liquidity risk, market liquidity risk, mismatch risk and market risk are some of the most significant effects that banks face, according to. We'll talk briefly about these dangers down below. Credit risk is among the numerous threats to banks' bottom lines because of the importance of interest payments from consumer loans (Gizaw, Kebede and Selvaraj, 2015). Initiated in the middle of 1997 in Thailand, the Asian financial crisis caused a number of financial and economic dislocations throughout the afflicted nations, including a large devaluation of their currencies and a drop in the price of equities. The equities markets in developing Asian nations fell precipitously, ranging from 29% in Thailand to 50% in South Korea, while the currency markets fell by as much as 34% in the Philippines and 49% in Thailand in the second half of 1997 (Noman et al., 2015). As an illustration, a year after the East Asian crisis hit, economic growth in the area, which had been in the 6-8% range before the crisis, plunged into recession.

Considering that it affected some of the world's fastest-growing nations, the East Asian financial crisis was truly astonishing. It is the worst financial disaster to hit developing countries since the debt crisis of 1982, and it has been coming for a long time. New risk management banking approaches Kaaya and Pastory (2013) were established in the 1980s and 1990s in response to the international expansion of the financial crisis, which became the focal point of the global financial crisis of those decades (GFC)

Credit risk is still one of the most discussed topics in the financial system, and this is true even after the current global economic crisis (MENDOZA and Rivera, 2017). Risk that one of the parties to a contract won't live up to its commitments under those conditions is known as "credit risk". A bank runs the danger of going bankrupt if the money it lends out to consumers is not returned. Because of the nature of banking, credit risk is a major concern for financial institutions, and the efficiency with which it is managed is crucial to the profitability of banks.

Like, Kodithuwakku (2015) describe credit risk as the possibility that the debtor will not remit the principle and/or interest on the loan as it becomes due. As was previously said, loan interest is a significant source of revenue for commercial banks but also represents a significant credit risk for these institutions. Banks have a vested interest in getting their loan money back from borrowers at the agreed upon day and time, plus any additional interest that may have accrued. However, if principle and interest payments are paid as scheduled and on schedule, the loan is considered to be "performing" (Alshatti, 2015). Non-performing loans are those whose payments consistently are late. There are typically three types of nonperforming loans: substandard, questionable, and loss. Substandard loans are those whose repayment is still outstanding 90 or more days after the loan's due date, while questionable loans are those whose repayment is still outstanding 180 or more days after the loan's due date. Unpaid loans older than three months are considered "loss loans" (Ogboi and Unuafe, 2013). When the sum in the loss loan category gets too high, the bank takes a major hit.

Risk management is crucial to the continued prosperity of financial institutions because it helps them to reduce exposure to danger and safeguard against economic and monetary challenges. Banks' bottom lines and the economy's overall systemic stability and capital allocation efficiency all benefit from prudent credit risk management (Naresh and Rao 2015). This is crucial to the lending process that banks provide, hence it is of great importance to them. Managing credit risk entails seeing the potential for a borrower to miss a payment on a loan and taking action before the debt goes into default. Uncertainty not only causes a wide range of bank profitability, but also leaves financial institutions in the dark about what percentage of their borrowers will ultimately default. The primary objective of credit risk management is to achieve a satisfactory level of exposure while maximizing the risk-adjusted return of the bank (Lagat, Mugo and Otuya, 2013). Executives are often in charge of drafting and implementing loan administration rules and procedures after receiving board approval. Senior management should ideally make sure that all employees in the organization understand the policies and processes that govern the loan approval process at every level ( An internal risk control and audit that keeps tabs on things like loan policies, facility risk exposure, credit discipline, approval procedures and portfolio level risk is another way to make sure things are running smoothly in the credit risk management department (Ayodele, 2014). Therefore, efficient management of credit risks is crucial to a company's continued profitability and viability, making it imperative that the company implement a robust credit risk management framework.

Strategic and Regulatory Importance of the Study

Banking industry leaders, depositors whose money is being utilized, and regulatory organizations tasked with safeguarding the banking system are all very concerned about the level of danger that may be present in the industry. For a variety of reasons, European commercial banks have struggled in recent years. It was determined that low lending standards and ineffective portfolio risk management were important contributors (Gizaw, Kebede and Selvaraj, 2015). There is evidence that the vast majority of European commercial banks have approved loans without conducting enough due diligence, which might increase the amount of non-performing and default loans. It is also argued that Europe's current credit risk management practices are insufficient to meet the region's current credit risk problems (Chi and Li, 2017). However, the European Central Bank has implemented policies in recent years to improve performance of banks and to take measures to lower the negative outcomes of loans, including raising capital requirements for facilitating the financial institutions merger to build robust and resilience financial system. When commercial banks are the backbone of a country's financial system, a crisis in that sector can have devastating effects on the economy. This is because a ripple effect may be set off if a company in the sector declares bankruptcy, causing a chain reaction that can spread to other financial institutions, triggering a banking crisis and eventually a global economic meltdown (Abata, 2014). As a result, the European banking sector must make sure that efficient policies are being put into place to reduce exposure to danger while increasing market returns and financial.

Research Gap and Rationale for the Investigation

In recent years, European commercial banks have struggled mostly because of low loan standards and ineffective portfolio risk management. European commercial banks are increasingly merging in order to fulfill the higher requirement in terms of capital established by the European Central Bank, which might eventually reduce the amount of competition among financial institutions. Ideally, this would lead to fewer instances of incorrect credit approval procedures, which have been linked to intense rivalry between financial institutions.

While European banks have fared well generally, some have reported losses. It is important to conduct this research as a preventative and mitigating step in light of the recent experiences during GFC in the developed countries. For the industry and economy as a whole to flourish in the future, it is crucial to comprehend the factors that affect bank performance.

Theoretical and Empirical Evidence on Credit Risk and Bank Profitability

Conceptual Foundations of Risk Management and Financial Performance

Risk–Return Management and Commercial Bank Performance

There might be a wide range of motivations behind starting a bank. Both of these goals might be seen as a reward for taking calculated risks in the banking industry. Kalu, Shieler and Amu (2018) realized that with more risk comes increased reward, thus a balance had to be found between the two for the company's benefit. Failure to effectively manage risk can have dire consequences for financial institutions, including insolvency. Therefore, there is always some form of risk associated with banking operations. Risks are acceptable if they are known, quantifiable, tameable, and well within the bank's ability to actively resist its detrimental effect. Bank management can make informed decisions about taking risks, reducing risks when necessary, and preparing for the unpredictable with the help of sound risk management (Nsambu, 2014.). If implemented properly, it would help financial institutions save time and money while drawing in new clients and remaining in compliance with regulations. Therefore, a bank's accounting results might be affected by how well it manages risk.

Comparative Empirical Evidence Across Banking Markets

Risk management's effects on profits have been the subject of a lot of research, as this overview of the corresponding literature will show. Given the focus of this study, a literature review was done; this review focused on studies that examined how credit risk management affected bank performance in various national settings.

Researchers Berrios (2013) have used descriptive and analytic methods to examine what helps, what hurts, and what drives the development of credit risk management in Saudi banks. The aforementioned study identified the CAMEL independent variables of capital adequacy ratio, liquidity, management soundness, asset quality, bank size, and profits on credit facilities. This study found that liquidity positively affects the effectiveness with which Saudi banks manage credit risk, whereas bank size negatively affects this variable. However, it was shown that other characteristics, such as assets quality, capital adequacy, earnings, and management soundness, had little to no effect on the effectiveness of credit risk management in Saudi Arabian banks.

Ayodele (2014) split their investigation on why and how much the increase in bank risk contributed to the worldwide financial collapse into two parts. In the first step, we analyzed the most recent data, including insider ownership, CEO compensation, and length of service. The second phase involved doing regression analysis with data obtained from Mergent Online. For the second stage, 40 banks were randomly selected from the database, covering the years 2005-2009 and providing around 200 observations. At this juncture, we evaluate results using KPIs like net interest margin, return on assets, return on equity, and cost of capital. Bank insiders, bank conservatism, bank remuneration, loan-to-deposit ratios, total debt-to-equity ratios, and bank tenure serve as dependent variables. Both the CEO's tenure and the presence of insider holdings were shown to be adverse to the bank's performance. Further research is required before this conclusion can be made generally, the report notes. According to the results of the regression analysis, a lesser degree of caution when lending is favorably connected to financial success, while a greater ratio of loans to deposits is inversely related to cash flows.

Recent research looked at the impact of credit risk on the profitability of European commercial banks using data from fourteen institutions between 2010 and 2015. There are 77 total observations, with the dependent variable being the return on assets and the independent variables being the nonperforming loan ratio, the capital adequacy ratio, the cost per loan asset, the bank's size, and the cash reserve ratio. Regression analysis was used to make sense of the data. Based on the results, it was determined that the commercial banks evaluated had inadequately managed their credit risk. A further demonstration is provided by the inverse relationship between the nonperforming loan ratio and bank profitability, as well as the inverse relationship between the cost per loan asset and bank profitability. The author found that the cash reserve and capital adequacy ratio did not influence the bank's performance. Since bank performance is correlated with credit risk, the author suggests that banks should employ effective credit risk management methods, such as a rigorous credit check procedure before giving loans to consumers.

As has been shown in previous studies, the bank uses the return on assets as a performance criterion. As a whole, the findings of this research show that the credit risk indicators used here are negatively related to bank performance. This indicates that higher levels of credit risk are linked to worse financial outcomes.

Alshatti (2015) used multiple regression analysis to examine the success of Nigerian banks between 2004 and 2008, and they accounted for credit risk in their findings. Both the percentage of nonperforming loans to loans and advances and the ratio of loans and advances to deposits were utilized as measures of financial soundness. Financial performance was evaluated using return on asset. Higher levels of non-performing credit, advances, and deposits are associated with increased distress and illiquidity risk for banks, as shown by this study. When developing the credit policy, the authors recommend that management use prudence.

Finally, during 2005-2015, Abusharbeh (2022) used a regression model to examine the connection between credit risk management and profits at 16 commercial banks in Albania. The ratio of nonperforming loans to total loans and the capital adequacy ratio are the independent variables here. Once again, ROA and ROE are employed as the dependent variables. According to the study's conclusions, commercial banks in Albania may improve their bottom line by focusing more on credit risk management, which is to say, by being more profitable. As a result of these findings, the authors suggest that commercial banks in Albania place a greater emphasis on the control and monitoring of non-performing loans as part of their credit risk management strategies.

Furthermore, using panel data analysis, looked into how credit risk management techniques and capital sufficiency affected the financial performance of banks in Nigeria between 2004 and 2009. Return on asset was used as the dependent variable, with the independent variables included loan loss provision, non-performing loan, and liquidity, capital adequacy ratio, loan and advances. Panel data regression results indicated that, with the exception of loans and advances, which had a negative effect on bank profitability, good credit risk management and capital sufficiency had a favorable effect on a bank's financial performance. According to the findings, Nigerian banks should implement strict credit evaluation procedures prior to loan distribution and drawdown as part of their credit risk management measures. The authors also stress the need of Tier-One capital improvement for Nigerian banks.

Finally, utilizing both primary and secondary data, analyzed the relationship between risk management methods and profits at five Islamic banks in Pakistan over a seven-year period of 2007 to 2013. These ROA and ROE contain dependent factors, similar to a large body of previous research. Advances and investments as a percentage of total assets, branches as a percentage of total assets, interest rates, GDP per capita, taxation and competition are the explanatory factors. In this study, we used a pooled regression analysis to look at how different risk management strategies affected bank profits. The results of the analysis indicate that risk management had a major unfavorable effect on profits from 2007 to 2013.

Credit Risk Management and Commercial Bank Profitability

Internal and External Determinants of Commercial Bank Profitability

The term "profitability" refers to a bank's ability to create earnings over a certain period of time relative to its expenses and costs. This demonstrates the bank's ability to increase capital while still managing the risks involved with doing so. It's also a measure of management's skill and the level of competition amongst financial institutions.

Commercial banks place a premium on profitability, as maximising profits is one of their primary objectives (Malede, 2014). Everything a bank does appears to have some kind of effect on its bottom line. The profitability of a bank may be broken down into a few different buckets. These factors, however, may be broken down into two major classes: internal and external. The decisions and policy goals of the bank's management have an effect on internal determinants (Alshatti, 2015). It represents the bank's liquidity management and spending management practices, as well as the origins and destinations of the bank's capital. The phrase "external determinants" is used to describe elements outside the bank that management cannot influence. To investigate how credit risk management affects financial institutions' bottom line, this research will look inward, at the factors that are under the control of the banks themselves. Some credit-related factors, such as the volume of non-performing loans, are, nevertheless, beyond the purview of management. There are also certain external factors included in the model design since they affect the management's decision-making process.

Financial Indicators Used to Measure Bank Profitability

Given the significance of profitability to the study's overall findings, a detailed explanation of how best to quantify bank profitability is provided. As we've already established, there are a number of ways to assess banks' profits, and picking one will rely on the goals of the study and the actual procedures used by the sample banks. Both ROA and ROE will be employed as indices of bank profitability in this study. Not only are return on assets and return on equity the most common metrics used to evaluate business performance, they are also often considered to be the most crucial indicators of a bank's profitability.

Return on Assets as a Measure of Management Efficiency

Profit earned per unit of total assets is denoted by the Return on Assets (ROA) ratio, which is calculated by dividing the net income by the total assets. It shows how efficiently the bank's management is putting its real investment capital to work for profit (Imbierowicz, B. and Rauch, 2014). Therefore, it exemplifies the effectiveness and profitability of a bank's management relative to its entire asset. ROA is a helpful metric for evaluating the profitability of banks with comparable risk profiles since it removes the confounding effect of varying levels of financial leverage. From an accounting viewpoint, return on assets (ROA) is a benchmark for assessing financial institution success. Return on asset (ROA) has been frequently utilized as a gauge of bank profitability in previous research evaluating the connection between credit risk management and bank performance, such as that of (MENDOZA and Rivera, 2017).

Return on Equity as a Measure of Shareholder Performance

To calculate the return to shareholders on their equity, a ratio of net income to total equity capital is used. It reveals how well management is turning shareholders' money into a return (Butaru et al., 2016). Return on equity (ROE) is a key metric for gauging the success and efficiency of a bank's management relative to the money invested by its shareholders. It is generally accepted that a greater return on equity (ROE) indicates a more profitable bank. When comparing banks, a greater return on equity is preferable. Therefore, bank shareholders always want a greater return on equity, but this may pose some risk to the bank Warue (2013) as a higher ROE indicates that net income is growing at a faster rate relative to total equity. The danger to the banks' solvency is further increased by the possibility that a sharp decline in equity capital may cause them to fall short of the minimal regulatory capital criteria. Return on equity (ROE) has been utilized as a profitability indicator in a number of past empirical research (Alshatti, 2015). In this case, return on equity (ROE) is employed as the secondary profitability metric.

Banking Risk Management and Exposure Categories

Credit risk management is another major focus of this research. Here we provide a high-level overview of the risk management process and introduce the numerous hazards that banks face. These are elaborated upon in the next section.

Principal Risks Affecting Commercial Banking Operations

Probability of occurrence is one definition of risk. Multiple sorts of risk can affect a bank's operations. The three main types of risk encountered in the banking and risk management industries are I those that may be mitigated via proper planning and monitoring, (ii) those that can be passed on to another party, and (iii) those that must be avoided at any costs (Adeusi et al., 2014). Credit risk, liquidity risk, financing risk, interest rate risk, mismatch risk, market liquidity, market price risk, market risk, and foreign currency risk are the types of risks defined and described by (Abiola and Olausi, 2014). Here's a quick rundown of what may go wrong:

Financial exposure, such as default or credit: There are many other sorts of risks that a bank might face, but credit risk is by far the most significant because it directly affects the bank's profitability. To put it simply, credit risk occurs when an investment's actual return falls short of its projected return. It may signify the potential for a loss of both the initial investment and any interest that has accumulated (Belás et al., 2018). Credit risk occurs whenever there is a possibility that a counterparty, debtor or borrower may not pay their loan in accordance with the terms of the contract. Credit risk is the largest danger to a bank's performance since it impacts profitability, liquidity, and cash flows. Credit risk is a major reason for bank failure. More specifically, Lagat, Mugo and Otuya (2013) divides credit risk into the following categories: exposure risk, migration risk, counterparty risk, default risk, recovery risk, concentration risk and correlation.

Liquidity risk or funding risk: When financial organizations face a payment obligation but can only swiftly transform available assets into cash by selling off capital assets, they face a liquidity risk (Noman et al., 2015). It's feasible for this to happen if depositors suddenly pull their money, making it hard to raise new deposits. If a bank wants to lessen its exposure to the danger of running out of money unexpectedly, it can protect itself by keeping a stock of liquid assets that can be easily converted into the necessary quantity of cash.

Interest rate risk: A drop in the bank's net interest revenue is an indicator of interest rate risk Abdelaziz, Rim and Helmi (2022). In this case, the interest rate will fluctuate in an unpredictable manner, leading to a deficit between interest expenditures and receipts. This situation develops when a financial institution solicits deposits by issuing short-term debt instruments like savings deposits and commercial papers, and then uses those funds to issue long-term debt instruments like mortgages and bonds. The cost of short-term liabilities increases more rapidly than the returns on long-term assets as interest rates rise.

Mismatch risk: Instances of this type occur when asset and liability maturities and interest rate reset dates are far apart from one another (Mengze and Wei, 2015). Liquidity risk and interest rate risk go go in hand with mismatch risk. A period of time's interest rate risk is determined by the spread between the interest rate on short-term deposits and the interest rate on long-term loans. A similar phenomenon, liquidity risk, occurs when financial institutions do not have enough cash on hand because of an imbalance in maturity dates. By lending at higher rates and borrowing at lower interest rates, financial institutions and banks may escape this predicament.

Market price risk and market liquidity: This problem only manifests itself for seldom traded assets. High-liquidity assets like Treasury bills and bonds are completely immune to market liquidity risk.

Market risk: This refers to the potential for financial loss as a result of unfavorable changes in market values, such as a temporary loss in the value of a currency or a permanent loss in the case of derivatives.

Foreign exchange risk: It's a potential problem if the bank loses money on its foreign currency holdings because of fluctuations in exchange rates (Noman et al., 2015).

While banks must deal with several threats, credit risk is especially important since it has a direct impact on bank profitability. If borrowers default on their loan payments and the bank does not get the interest it is owed, the bank's profitability will suffer. In the event of a default, the principle amount is more crucial than the interest rate since it is the amount that has been collected via a pool of depositors. Loan amounts extended by banks are often guaranteed by mortgages and other forms of collateral. The principle reduction adds a new cost to the recovery process, and in most cases the bank does not recoup the full amount of default, cutting into its bottom line. The primary emphasis of this research is on the impact of credit risk on the bottom lines of commercial banks.

Risk Identification, Assessment, Treatment, and Monitoring

The goal of risk management is to stabilize profits and protect against losses. Noman et al. (2015) explain that the phrase "risk management process" refers to the method through which financial institutions locate, quantify, monitor, and control hazards, and assess whether or not they have sufficient capital to deal with them.

Risk assessment involves figuring out what may go wrong, how, when, and why. The next step is a careful examination of the potential dangers and their aftereffects, known as a risk analysis. Risk evaluation, in which a determination is made on the degree of risk, is the final stage (Naresh and Rao, 2015). Risk treatment is the third phase of risk management, and it may be accomplished by the refinement of already-in-place safeguards or the creation of brand-new ones.

After carefully considering several possibilities, Ayodele (2014) has settled on the overall risk management method that we outlined. The phases and their constituent parts—especially the consultative and monitoring phases—repeat themselves in practice. On the other hand, we may draw parallels with the way banks handle risk. Over time, banks have been increasingly vulnerable to a wide range of hazards as a result of the nature and scope of their everyday operations (such as deposit taking, lending, currency exchange, money transfers, etc.). Avoiding losses, avoiding insolvency, benefiting shareholders and depositors, and increasing profitability all need effective risk management in the banking business.

Processes and Strategies for Managing Credit Risk

Commercial banks can't succeed over the long haul without properly managing their credit risk. One of these multifaceted jobs is credit risk management, which may be approached from a number of different angles. Credit risk management is the process of identifying, measuring, and responding to changes in creditworthiness, as well as developing and implementing measures to reduce exposure to risk, including but not limited to transferring risk to another party, avoiding risk, mitigating the risk's negative impact, and accepting some or all of the consequences of a risk. Hedging of defaultable claims, risk integration, and portfolio management are all methods outlined by Gizaw, Kebede and Selvaraj (2015) for controlling credit risk.

Indicators Used to Evaluate Credit Risk Management

Based on their significance to the field of credit risk management, a number of different indicators were selected for this investigation. It is important to note that some of the indicators have been selected because of the role they played in earlier research. Additional theoretical considerations for two variables are addressed in the context of credit risk management.

Capital Adequacy and the Capacity to Absorb Losses

Capital adequacy ratio measures bank's capital level stated as a percentage of its risk-weighted credit exposure. It is computed as the ratio of the amount of capital to the risk-weighted sum of the bank's assets (Ogboi and Unuafe, 2013). That's the minimum capital amount that a bank must keep on hand to remain in compliance with the law. To ascertain the ability of banks to meet losses and to guarantee that, even in the worst case situation, banks would still bear a tolerable amount of losses, it is crucial to have a predetermined CAR (Mengze, and Wei, 2015). A high CAR is indicative of a safe bank that will be able to satisfy its financial commitments. The safety of depositors and the steadiness of the banking system both increase in direct proportion to the ratio. Profitability may be improved as a result of increased capital adequacy because better able banks are to weather financial storms and avoid collapse and insolvency.

Liquidity Position and Short-Term Financial Obligations

When banks have sufficient liquidity, they may quickly and cheaply turn their assets into cash or meet their obligations to their creditors and other stakeholders Kaaya and Pastory (2013). It refers to the capacity of financial institutions to meet all maturing short-term commitments. Short-term commitments include things like loans, withdrawals from deposits, investment promises, and liability maturities. The ratio of the credit line to the total deposits is the indicator. A bank runs the risk of losing money if it can't get the money it needs to operate. This is known as "liquidity risk" (Kodithuwakku, 2015). Having a low liquidity ratio indicates that a bank may have trouble meeting its short-term commitments, which is of more concern to investors. Having a high liquidity ratio indicates that a bank is hoarding too much of its liquid assets that may be used in other profitable areas.

Several empirical research that attempted to establish a causal link between liquidity ratio and bank profitability yielded contradictory findings. Abusharbeh (2022) discovered a beneficial influence of LR on bank performance, whereas Alshatti (2015) discovered a negative effect of liquidity ratio on bank finances. It follows from these analyses and theoretical frameworks that we should anticipate a negative correlation between shifts in the liquidity ratio and the financial performance of banks. Because keeping too much cash on hand prevents the bank from investing in productive ventures.

Asset Quality and the Performance of Loan Portfolios

The resilience of a bank or other financial organization is measured by the quality of its assets relative to the risk of their value declining (Chi and Li, 2017). An indicator of this is the rate of increase in the sum of loans. Banks rely on interest payments from borrowers to generate revenue, and a decline in loan value is generally correlated with the risk of insolvency for financial institutions. Unless the bank takes on an excessive amount of risk, a rise in gross loans has a positive effect on profits (Kalu, Shieler and Amu, 2018). While it's great to see banks' asset values rise, that's not enough if the loans they're approving and sanctioning are of poor quality. Subpar loans have a high default rate, which means the bank will make no money on them.

According to Mengze and Wei (2015), a bank's ability to assess, manage, and collect on loans is directly related to the quality of its assets. Having proper reserves for probable losses, refraining from hazardous lending, and securitizing the loans based on collateral can all enhance quality. There has been a dearth of research into the link between credit risk management and profitability that controls for asset quality. Credit risk management is only moderately affected by asset quality, according to. It is hypothesized here that there is a positive link between asset quality shifts and banks' bottom lines, which is in line with existing theoretical literature.

Leverage, Solvency, and Bank Funding Risk

Excessive leverage ratio in the banking sector is commonly thought to be one cause of the global financial crisis, as mentioned before Berrios (2013). How much debt a bank is utilizing to support its operations is represented by the leverage ratio, which is calculated as the total amount of debt divided by the total value of equity held by the bank's shareholders. The ratio of debt capital to total capital is calculated to determine the solvency of a bank. The bank's funding comes from a combination of stock from shareholders and debt. A high leverage ratio indicates that the bank is using debt as a primary source of funding. Aggressive leveraging methods are generally accompanied by significant levels of risk, since unmanageable debt signals an inability to meet financial obligations. In addition, banks may have erratic profits due to the added interest costs associated with a high leverage ratio. If profits rise while maintaining the same number of shareholders and investment, then shareholders win.

However, if the interest on the debt is more than the profits, the company might go bankrupt, leaving the investors out of pocket. In addition, a high leverage ratio always poses a danger to depositors because it means they have less security for their money (Noman et., 2015). Keeping a healthy leverage ratio in banking is crucial because it prevents banks from amassing too much debt, which might weaken the financial system and the economy as a whole and weaken risk management practices. The European Central Bank has mandated that the minimum leverage ratio for commercial banks in Europe be kept at 4%.

When looking at the effect of credit risk management on a bank's bottom line, leverage ratio has been employed in very few research projects. Ibtissem and Bouri (2013) discovered that increasing a bank's leverage ratio hurts its bottom line. Previous research has suggested that a high leverage ratio is inversely related to a bank's profitability.

Non-Performing Loans as an Indicator of Credit Risk

Non-performing loan ratio (NPLR) is an important measure of credit risk and financial stability since an increase in NPLR is seen as the failure of credit policy in banks, a decrease in bank revenues, and a key cause of the financial crisis. ( NPLR represents the percentage of nonperforming loans relative to the overall loan portfolio, hence it is also used as an indicator of how well banks manage their credit evaluation. If the borrower is still making payments on the loan, it is more accurate to call it a late payment than a default. However, once a loan becomes non-performing, the likelihood of complete repayment is extremely low (Nsambu, 2014). Any loan where either the principle or interest is more than 90 days past due is considered a non-performing debt.

Cash Reserve Requirements and Their Profitability Effects

The ratio of the central bank's cash reserves to the sum of all customer deposits is known as the cash reserve ratio. Reserve banks utilize it as a tool of monetary policy to manage the flow of currency throughout the economy. This has consequential effects on bank interest rates, liquidity, and profits. When the CRR is lowered by a central bank, banks become more profitable as a result of increased access to funding and a corresponding rise in interest profits. When CRR rises, however, banks have less cash on hand, so they can't make as many loans, which leads to lower interest income and lower profits. A change in the CRR will affect the liquidity of banks by making more or less money available for lending. CRR, however, does not generate any revenue for banks and instead reduces their bottom line. At the European Central Bank, the required CRR is expressed at varying rates for various banking and finance firms. The CRR that commercial banks must maintain has been set at 6% as per instructions issued by the Europe Rastra Bank in 2014/2015.

CRR has been used as a control variable in a small number of studies examining the impact of credit risk management on bank performance. Several studies (including one by ) have shown that the cash reserve ratio has a negative effect on a bank's bottom line. It follows from this review of the relevant research that we might anticipate a negative correlation between CRR and bank profitability.

Bank Size, Economies of Scale, and Financial Performance

Both economies and diseconomies of scale in the banking industry can be attributed to the different sizes of banks (Malede, 2014). When compared to smaller banks, larger ones are more likely to be competitive in the market, offer a wider range of services, and have higher opportunities for hedging risks. Larger banks benefit from economies of scale since they are not required to compete in the same highly competitive industry. However, in the opinion of Bekhet and Eletter (2014), a bank's size directly correlates to the amount to which financial, legal, and other variables impact the bank's profitability. One of the control variables used to examine financial performance of banks is the bank's size, which was determined using the log of the book value of total assets estimated in the study's currency.

Credit risk management and bank profitability is a topic studied by only a select few writers, and even then, only when controlling for bank size. Researchers observed a correlation between bank size and performance, suggesting that as banks become larger, their profitability does as well. This is especially true for smaller and medium-sized institutions. In contrast, research from indicated that larger banks were less successful at managing credit risk. Accordingly, a positive correlation between bank size and bank profitability is anticipated on the basis of theory and prior research.

Research Design and Econometric Procedures

This section presents the methodology that would be followed in the analysis process. The data collection process is also explained in this paper.

Descriptive Research Design and Analytical Purpose

The research design refers to the overall strategy that a researcher chooses to integrate the different components of the study in a coherent and logical way, thereby, ensuring he/she will effectively address the research problem. There is a wide range of research designs in the market. Research design is determined by the purpose of the study and cannot be tailored. It differs from study to study depending on the research questions (e.g., descriptive, exploratory or causal/experimental) (Dannels, 2018).

This paper utilizes the descriptive research design. Descriptive research: It collects information about the present status and characteristics of the subject. The researcher does not interact with the object of study by means of manipulation or control, but attempts to explain, understand or interpret certain events or conditions (Babbie, 1990). Its used in this analysis for a number of reasons. One is to gain information on the subject of this research. And to be able to understand the effect and the impact of something or someone.

Descriptive research generally attempts to find answers by gathering and analyzing data for a given phenomenon. Essentially, it’s used for understanding, explaining and predicting phenomena as is taking place in our society (Thompson, 2006). It is also used in describing other studies conducted before or after an identified one that have already been performed in order to establish comparisons and make predictions. therefore, its Ideal to meet the research objectives of this paper.

Panel Data Structure and Longitudinal Observation

Panel Regression as the Primary Econometric Model

The form of data used in this analysis is panel data. "Panel Data" refers to data collected by surveys as it is collected over time with interviews/exam and responses. "Panel Data" of economic behavior studies includes all possible observations on the same individual in a longitudinal study. Panel Data is superior to a cross-sectional study because certain behaviors and events of interest often don't manifest themselves until after an extended period of time, when enough observations are taken that the natural occurrence that caused them can be observed reliably.

the best model that can be used for panel data is panel regression. Panel regression is a special case of pooled regression. Panel data regression is a powerful way to control dependencies of unobserved, independent variables on a dependent variable, which can lead to biased estimators in traditional linear regression models. Panel data regression examines the same groups of individuals over several time periods, and thus can help eliminate the biases that come from simultaneity in traditional linear regression models.

The panel data regression is the best model for studying a problem of panel data. The main difference between a panel-data analysis and a cross-sectional analysis is that in panel-data analysis, the same group of people is studied over several time periods, people who made up that group experience change(s) with time. A simple regression model cannot be used for panel data analysis because the time-invariant variables are usually ignored in the model. Cross-sectional data analysis assumes that there is no change in regard to the independent variables in a short period of time, but for panel data analysis, this assumption is not always correct.

The use of panel data regression on a single cross section enables us to take advantage of both its features: study change(s) and stability at the same time. Panel data regression can be used only when there are time-variables included in both dependent and independent variables.

Panel regression is used for the following reasons: 1)to control for the fixed effects of the individual or group, 2)to calculate standard errors, and 3)when it is possible to do so with sufficient precision, to gain more accurate estimates of the coefficients.

Hausman Specification Test for Model Selection

The Hausman Test (also called the Hausman specification test) detects endogenous regressors (predictor variables) in a regression model. It is most often used in econometrics to test for multicollinearity, but can be applied to other fields, such as statistics and machine learning (Lapatinas, 2019).

This paper uses the Hausman specification test for the panel data analysis. To carry out the Hausman test for panel data, we need to: For multicollinearity detection, the statistical package SAS implements a procedure of which we can use as an example.

In the first step, a model is constructed by using multiple regressions (say "y" = 2*"X"), where "X" is a sample from of the observed and "y" are some of the predictors built and measured in each individual case. The formula for such a model is: This model can be written as an equation in which "y(i)" is an observation vector and "b" are explanatory dummy variables.

The Hausman test is sometimes described as a test for model misspecification. However, it is better to think of it as a way of testing whether the variables have been measured without error. Because the test is based on OLS, it has no ability to detect measurement error (Ranger and Much, 2020). The Hausman test lacks this ability because the dependent variable has been linearly transformed in the equation for "y", which may cause a failure in OLS and lead to biased inferences about the coefficient coefficient in this equation.

In panel data analysis (the analysis of data over time), the Hausman test can help a researcher to choose between fixed effects model or a random effects model (Aliha et al., 2020). The choice between the two depends on whether they have correlated observations. In the fixed effects model, observations are aggregated into a single level of "X" for each case. This model is appropriate when there are no correlated observations.

A random effects model is more appropriate in this case because it allows the researcher to take into consideration the correlation between observations across time. In this model, each level of X is dependent on all other levels of X, which implies that "X" will not be uncorrelated with any other level of X. Additionally, in the random effects model the formula for "y" will be written as:where "b" and "z" are independent variables: The same idea can be applied to regression analysis instead of panel data analysis.

The null hypothesis is that the preferred model is random effect and the alternative is that it is fixed effect. This test should be conducted in order to ensure that the fixed effect model from the random effect model. Essentially, the tests look to see if there is a correlation between the unique errors and the regressors in the model. One additional caveat about the Hausman test is that it can lead to a type I (alpha) error by rejecting the model when it should not be rejected (Aliha et al., 2020).

the decision to go with the null hypothesis is arrived at depending on how large the p-value is. A smaller p-value suggests more likely that the preferred model satisfies the null hypothesis better. The further we are from a significance level of 0.05, the less likely a conclusion is drawn from this test.

Fixed-Effects Estimation and Time-Invariant Heterogeneity

The fixed effects model is an econometric model used to evaluate the cause and effect of events in a multi-level, hierarchical data set. It is also commonly known as the panel analysis model. The main purpose of this analysis technique is to determine whether what effects are imposed by parameters that vary only over time or whether the event can be attributed primarily to variables that vary at the individual level (McNeish and Kelley, 2019).

In simple terms, this model allows us to take into account unobserved heterogeneity (or differences) among people by examining repeated measures or ‘cross-sectional’ data from a single group—in other words, from one point in time. The variables can be any measured characteristics of the individuals, for example, income, marital status or weight (Lin et al., 2020).

How it works: The fixed effects model will try to determine whether there is a relationship between two variables—for example, whether people who are married are more likely to vote for a particular party. By comparing the predicted probability of voting for the candidate with and without being married the analysis will try to find out whether there is a statistically significant relationship between being married and voting.

How it differs from other models: Fixed effects models are special cases of random effects models (a broader class of econometric models) where groups are assumed to be identical within each time period or observation but different across time periods.

Random-Effects Estimation and Population-Level Variation

A random effects model is a mathematical representation of the relationship between two or more variables. It simplifies some of the complexities inherent in linear and non-linear relationships, while simultaneously capturing some important aspects not captured by these other types of models. It is one of the most commonly used models in epidemiology and has been useful in many areas of medicine (Veroniki et al., 2019).

In a random effects model, each individual's outcome is represented by a random mathematical object with an unknown (but fixed) covariate, which can vary across individuals. To the mathematical formalism, there are no separate parameters: both the covariates and any effect are described as covariates. This contrasts with a linear model that simply describes the association between fixed individual characteristics and the outcome (the way two variables change together). In a non-linear model, only a subset of individuals have a certain characteristic associated with their outcomes; in others it may be completely absent (Antonakis et al., 2019).

Two common assumptions can be made about the individual specific effect of the covariate: the random effects assumption and the fixed effects assumption. Depending on the specific model, the random effects assumption might be made on the basis that there is some unknown fundamental difference between individuals, or it might be made in order to make the model more parsimonious.

The fixed effects assumption requires each individual to have a fixed effect of any covariate they have. It is a stronger assumption than the random effects assumption, and is made to ensure that everything within an individual's profile has a fixed effect. This would not only include their observed characteristics but also important biological factors if they are part of a biological factor (e.g., height). The random effects assumption is that the individual unobserved heterogeneity (or "personality", "quirk", etc.) will be distributed randomly and has the same distribution across a sample. The fixed effects assumption is that the unobserved person's distribution is fixed.

If the random effects assumption holds, the random effects estimator is more efficient than the fixed effects model. In a few specific situations, there are models that describe the relationship between an individual's outcome and the covariate for that individual. These models can be used to describe group differences and may be considered to be more "scientific" than random effects models.

In summary, random effects models are more useful for describing population level data, while mixed or non-linear models are more useful for describing groups of individuals in a study. Both have advantages and disadvantages that depend on the specific model being used.

There is also an association between the linearity of a model and its meaning: non-linear relationships often represent unknown or unmeasured factors (e.g. genetic factors). Many studies have found that many factors influence a person's outcome and have used non-linear models to fit them (see non-linear models).

The random effects model is one of the simplest extensions of the simple linear regression model. The linear regression model describes the relationship between a single, continuous response variable and one or multiple, continuous predictor variables: Y = βX + ε. This describes a fixed effect between X and Y. The random effects extension allows for an additional relationship between Y and ε: Y = βX + ε + ζε where ζ is distributed as N(0, τ).

Specified Linear Regression and Model-Selection Procedure

In this paper a linear regression model is fitted on the data. Using the Hausmann test we can then determine which model to use the fixed effect model or the random effect model. From there, the necessary conclusions are made about the data.

Empirical Findings and Statistical Interpretation

The results section presents the results of the analysis that was conducted in the research using the methodology provided in the previous section. Therefore the section is divided to the findigs and discussion section/

Summary of the Empirical Findings

Descriptive Statistics for Credit Risk and Profitability Variables

Fixed-Effects Regression Estimates

Interpretation of Statistical Results and Comparison with Prior Studies

According to the findings presented in the chapter before this one, the p-value for CAR when ROE is used as the dependent variable is 0.135, whereas the p-value for CAR when ROA is used as the dependent variable is 0.465. As a result, neither Hypothesis 1 nor Hypothesis 2 can be discounted at this time. To put it another way, we have not been able to identify any statistically significant link between the existence of CAR and either ROE or ROA. When we include the control variable in our analysis, we do not see a link that is statistically significant between ROE and LNTA. This discovery is in contrast to the conclusions drawn from earlier research carried out in Sweden by Ara, Bakaeva, and Sun (2009), in Ethiopia by Tibebu (2011), and in Egypt by Samy and Magda (2009). In every single one of these experiments, a positive association could be shown between CAR and ROE or ROA. Since the CAR has taken on the risk for its stakeholders, it is fair to believe that it has successfully reduced the costs associated with its financing and is now in a position to provide additional support toward enhanced ROE and ROA. However, other investigations, such as the one that was carried out in Kenya by Kithinji (2010), have indicated that there is no association between CAR and ROA or between CAR and ROE. Kithinji discovered that there is no connection between CAR and ROA by analyzing data collected from 43 commercial banks in Kenya between the years 2004 and 2008. It is interesting to note that our findings demonstrate that the correlation coefficient of CAR is negative for both ROE and ROA, despite the fact that this link was not found to be statistically significant. To put it another way, there is the possibility that the CAR may lower the profitability of the banks. The capital adequacy ratio, or CAR, is the ratio of the overall capital of a bank to the risk-weighted assets of that bank (Hyun and Rhee, 2011). Therefore, the negative figure may indicate that banks may limit activities that 65 may be negatively related with bank development in order to maintain a higher CAR. This, in turn, may have a negative impact on the expansion and growth of banks, as the negative figure indicates that banks may limit activities that 65 may be negatively related with bank development. Because of the nature of this law, there is a possibility that the net interest margins of banks as well as their overhead expenditures may increase (Samy and Magda, 2009). The bottom lines of commercial banks may be significantly affected by issues such as stifled development, increased overhead expenses, and diminishing net interest margins if these trends continue. As a consequence of this potential downside, the CAR may have a negative impact on the earnings of commercial banks. It's possible that the type II error is to blame for the results that weren't significant. A mistake has been committed if a hypothesis is not rejected when and where it ought to be (Bryman and Bell, 2007). This shows that our alternative hypothesis is reasonable; nonetheless, our model is unable to demonstrate the existence of this hypothesis. It is impossible to overlook the fact that the R2 values for the two different regressions are so low (0.08 and 0.10). A cautionary note has been placed here, suggesting that the data does not fit well into our model. It may also be attributable to overlooked factors, such geographical location, that must be accounted for if we want our findings to have any weight. Because of this, the fact that we found no association between CAR and ROE or CAR and ROA should not come as a surprise to you. The impact that both internal and external controls have on the profitability of commercial banks was only one of the topics that we went about. The status of the economy is one factor that may have an impact on a company's profitability in addition to internal and external factors. When we look at this time frame, we see that it encompasses a number of different financial crisis times, each of which would have a significant effect on the economy of Europe. During the current financial crisis, the profitability of commercial banks is significantly impacted by factors that are beyond the control of humans, namely system hazards. As a result, the ROE and ROA variables may behave unexpectedly at these times. There is a connection between this and the fact that the correlations are not statistically significant.

The results reveal that the connections between CAR and ROE and CAR and ROA are dynamic and are prone to change. These correlations can be seen in the second linear regression table. At the level of confidence of 95%, none of the correlations with the independent variable CAR can be considered significant. In addition, the value of the correlation coefficient for CAR vacillates dramatically between positive and negative values. The fact that the projected relationship between CAR and ROE and ROA turns out to be erroneous is one potential reason for these differences. Other possible explanations include: If banks had a higher CAR, their shareholders' risk would be internalized, which would result in lower financing costs and more incentive for enhanced ROE and ROA. If banks had a higher CAR, banks would have a higher capital adequacy ratio. As a consequence of this, financial institutions might witness an increase in their net interest margins or overhead expenditures. Furthermore, in order to keep a high CAR, these financial institutions could be compelled to restrict activities that have a negative effect on bank growth. Because of the influence of mixed effects, correlation coefficients for both positive and negative values may exhibit erratic behavior. The association between NPLR and ROE is quite steady over the course of time, as seen in table 11 and figure 10, despite the fact that it reaches its highest point in 2009. Compare this to the rather constant correlation that exists between NPLR and ROE. This association shows a positive peak in 2010, followed by a drop, and a negative trough in 2009. We hypothesize that the effect of the financial crisis may be to blame for the odd pattern, given that it may have contributed to the regression being biased in some manner by the systemic risks that were involved. It's possible that this indicates that ROE and ROA, both of which are independent variables, are quite sensitive to how the economy is doing. Another piece of evidence that supports the idea is the observation that there does not seem to be any discernable pattern in the occurrence of financial crises over the course of a single year. It's possible that another element at play here is the little amount of data we have access to—just one year's worth. For a regression with just 47 data, the results may not be statistically significant, which is an indication of an uncommon pattern. As far as we can tell, no prior studies have established a connection between their research and the consistency of their findings. Because of this, we do not have a foundation for comparing our research to others that are similar to it. In conclusion, our research did not uncover any convincing evidence to support either the claim that CAR boosts ROE or the claim that CAR reduces ROA. Similar to the link that exists between NPLR and ROE, the one that exists between NPLR and ROA is negative but unstable.

Alqisie and Ahmad (2019) conducted research to investigate the influence that risk management has on the profitability of Jordanian commercial banks. Independent variables CP, LQ, IC, EFFC, CICF, PRCF, INF, and INT are used to evaluate liquidity risk, operational risk, credit risk, and market risk. The return on assets, or ROA, is used as the dependent variable in analyses of the profitability of financial institutions. The measures that JCB uses to manage its risks might be responsible, at least in part, for the company's bottom line. There will not be any big effects on the management of market risk, credit risk, or liquidity. In the meanwhile, the management of operational risk has a significant bearing on the situation. Fan Li (2015) investigated the relationship between credit risk management and the earnings of commercial banks. ROI and ROA are dependent measures. NPLR is a separate entity from CAR. Controlling credit risks may increase profits. The authors ascribe the absence of significance between CAR and ROE to the controversy surrounding theoretical expectations of the relationship between NPLR and ROE and NPLR and ROA. As a result, the writers find that there is no correlation between the two variables. In the event that the NPLR is high, the bank will have a less amount of available capital for investment purposes. Even though there is not a statistically significant relationship between the two variables, the co-efficient of CAR for both ROE and ROA is, inexplicably, negative. If CAR is put into effect, it is possible that the bank's bottom line may suffer as a result. The impact of the Islamic financial institutions in Jordan's risk management practices and policies on their bottom lines is discussed. Examples of risk management approaches include liquidity risk, operational risk, credit risk, and market risk. Return on assets and Return on equity are examples of performance metrics. Liquidity risk, Operational risk, Credit Risk, and Market Risk According to the findings of the study, market risk had a positive and considerable influence on the overall performance of Islamic banks in Jordan, but liquidity risk, credit risk, and operational risk all had a negative and major effect on the banks' overall performance. Alzorqan conducted an investigation on the relationship between efficiency (as measured by ROI and ROE) and liquidity (as measured by current assets and credits to shops expressed as a percentage of total assets), focusing on Jordanian commercial banks. The results of the research indicate that ROI and ROE are both significantly influenced by the ratios of existing technologies and improvements to retail outlets. A recent academic research looked at the effect that credit risk management has on the profitability of commercial banks in Jordan. The study was conducted in Jordan. As the dependent variable, profitability metrics such as return on assets and return on equity are analyzed. The CAR, CI, CFR, LR, and NPL/GL ratios are examples of independent variables. Other credit risk management indicators include the CAR and LR. When there are few performing loans relative to nonperforming loans, the effect is more likely to be positive. Changes in capital adequacy ratio, credit interest/credit facilities, and leverage ratio do not have any impact on earnings on equity (ROE). The use of leverage has a negative impact on the bottom lines of banks. The risk of credit defaults is an important component to consider when attempting to explain profitability. Saeed and Zahid (2014) investigated how the exposure of commercial banks to credit risk impacted the bottom lines of those institutions. The rates of return on assets and equity are considered to be dependent elements, whereas credit risk, bank size, growth, and leverage are considered to be independent variables. The author believes that credit risk is the most serious kind of risk, especially when it is posed by a financial institution and has the potential to result in significant challenges. As was shown in 2008, a number of banks were able to withstand the severe recessions despite having inadequate credit risk management. Even though banks have learned a significant lesson from the financial crisis that occurred in 2008, they still need to tighten their approach to credit risk. A bank might be of assistance by ensuring that loans are repaid as well as decreasing information gaps. According to the author's point of view, the size of a bank, the amount of leverage it uses, and the pace at which it grows are all positively correlated with one another. After a series of financial crises, the bank was able to reduce its exposure to credit risk and resume profitable operations. There is a lack of transparency on the relationship between credit risk and earnings. Sun and Chang (2014) conducted research to investigate the impact that credit risk has on the bottom lines of commercial banks. CAR and NPLR are considered to be the independent factors in this scenario, whereas ROE and ROA are considered to be the dependent variables. "Work Area Data Stores" (sometimes known simply as "WADS") is an example of a data resource. The author draws the conclusion from the facts that credit risks lead to increased earnings. They find that there is a detrimental relationship between NPLR and ROE as well as NPLR and ROA. When they integrate the findings, they come to the conclusion that there is a positive link between credit risk management and profitability. The author arrives to the conclusion that in order to expand their profit margins, bank managers need to enhance the amount of control they do over risk management. There is no correlation that can be shown by statistical evidence between CAR and profitability. An investigation was carried out at the risk management and profitability of Albanian commercial banks. The author's investigation revealed a negative association between credit risk and profitability ROA and ROE. This was one of the author's findings. It has been shown that a sufficient amount of capital levels has a positive correlation with both the return on assets and the return on equity as indicators of a company's profitability. It is possible that using return on equity as a measurement of profitability may provide more accurate results than using return on assets in this context. There is a considerable correlation between commercial banks' credit risk and their bottom lines. Sayilgan conducted research to determine the elements that contributed to profitability in the Turkish banking sector during the years of 2002 and 2007. The results of regressions were studied by the author, with ROA serving as the dependent variable in each case. Between the years 2002 and 2007, Turkey made tremendous progress in improving its financial accounts. Anything about the microscopic free variables was discovered by the author. It would seem that the banking business is more lucrative given the decline in inflation, the rise in the industrial production index, and the improvement in the budget balance. Abel conducted research in Zimbabwe to determine what aspects of the banking business led to its overall performance. ROE and ROA are two metrics that are used to evaluate the profitability of financial institutions. The INF, CADEQ, credit risk, bank management, and economic development are the factors that are considered independent. According to the author's findings, the profitability of individual banks within Zimbabwe's banking system is mostly governed by features that are exclusive to that institutions. The bank level management variables determine which factors affect profitability. Banks that have a comparatively high quantity of liquid assets, high capital, low levels of nonperforming loans (NPLs), and manage their expenses effectively and efficiently tend to have higher levels of profitability. The financial sector need to contribute to the expansion of OEM. According to the findings of the study, there is a significant association between profitability and LIRISK. It's possible that increasing the amount of cash flow available may increase earnings. The method used by Kithinji for the control of credit risk and the achievements of Kenya's commercial banks. The total amount of bad loans, which comes to 2.676 billion Kenyan shillings, has no bearing on the amount of money the firm makes. The author claims that he or she is unable to uncover any connection between the quantity of credit profit and the number of loans that turned out to be problematic. It is a common misconception that the percentage of nonperforming and impaired loans has a significant impact on the profitability of commercial banks, however this is not the case. The profitability of European banks was the topic of investigation for Staikauras and Wood's study. The results show that changes in the external microeconomic environment, in addition to the activities of management personnel, have an influence on the profitability of European banks. Previous studies that investigated the structure-performance relationship in European banking and found that market share and concentration both favorably affected bank profitability are refuted by these new results. These studies were conducted by researchers in Europe. The deficiencies in the analysis may be attributable to the fact that the functional form of the estimating equation has to be fine-tuned.

What kind of impact does Shijaku's concentration have on the overall health of the financial institutions? Evidence from the Albanian banking sector that is considered preliminary. The capital structure, which is represented by LEVERAGE, has the largest influence on bank stability, making it the most important of the internal components. The author arrives at the conclusion that a rise in concentration poses a risk to the continued existence of financial institutions. Regarding the variables affecting the macroeconomy, it would seem that smaller banks are more susceptible to the effects of market concentration than their bigger counterparts. Short conducted research on commercial bank profit rates as well as banking concentration in the countries of Canada, Western Europe, and Japan. There must be some wiggle space in order to handle the larger profit margins or lesser availability of capital in some countries. This wiggle room is required. The standard interest rate for discounts is an example of an independent variable. The rate of profits is the variable that is being dependently examined. The data indicate that the difference in concentration across the 23 countries may be accounted for by total deposits, or money, in its widest meaning. This difference may account for more than a third of the difference. There is just a little effect that the regulatory structure has on the concentration and profitability of the banking business.

Integrated Findings, Conclusions, and Strategic Recommendations

In the introduction, we established the main objective of our research, which is to investigate the link between the level of success that commercial banks in Europe have with credit risk management and the amount of money that they make. The data was obtained by carefully reading the annual reports submitted by 47 of the most prominent banks between the years 2016 and 2021. The link between two abstract concepts was investigated by using proxies to stand in for the concepts themselves. We have chosen CAR and NPLR as proxies for profitability, and ROE and ROA as proxies for credit risk management. Both of these proxies are used in conjunction with one another. After we had collected all of the necessary information, we used the statistical analysis program STATA to conduct a series of tests to determine whether or not our hypothesis was correct. In accordance with this line of reasoning, we made use of the data gathered over a period of six years to generate four hypotheses and run two regression analyses on the two independent variables, ROE and ROA. The second experiment that we are doing focuses on the dependability of such a cooperation. Additional regressions are carried out across a total of six different time periods in order to establish whether or not there have been significant variations in the correlation coefficients. The study question that we are attempting to answer is as follows: "What is the link between credit risk management and profitability of commercial banks in Europe from 2007 to 2012?" After running a series of regression analyses, we should be able to answer this question. It is possible to draw the conclusion that there is a link between the management of credit risk and earnings based on the data. Based on the data we have, our first observation is that there is no correlation that can be considered statistically significant between CAR and ROE or ROE and CAR. One of the possible explanations is that theoretical forecasts of the relationship between CAR and banks' profitability have been viewed with suspicion, and this is one of the factors. It's possible that the faulty update to our model is also to cause for the missing links. During this time of financial turmoil, it is imperative that one does not minimize the gravity of the systemic concerns. The second thing that we found was that a negative correlation exists between NPLR and both ROE and ROA. The results of the great majority of research that have focused on a single country corroborate these assertions. When the NPLR is high, the amount of money that banks have available to put into investments is reduced. The trend data demonstrate that there is some indication of a changing connection between the four variables, which brings us to our third point. This might be explained by the impact that the financial crisis has had, which is that it has increased the number of economic variables that affect profitability. Combining the findings from the two different measures that serve as proxies for credit risk management allows us to reach the conclusion that there is a positive connection between credit risk management and the profitability of commercial banks (CAR and NPLR). To put it another way, commercial banks that take credit risk management seriously have a greater chance of being successful. Following the conclusion of our research, we would like to provide some recommendations to commercial banks. Because of the positive association between the two, we recommend to executives of banks that they allocate more resources to credit risk management, specifically to the reduction of non-performing loans (NPL). To put it another way, managers need to be more practical when evaluating the ability of their firms to repay loans. Even though we were unable to identify a connection between CAR and proxies for profitability, this does not imply that CAR is not a relevant metric. Due to the fact that it is essential to the method of risk management used in commercial banks, it continues to be necessary to pay careful attention to this issue.

Contribution to Credit Risk Management and Banking Research

By doing this study, we were able to fill a hole in the existing research by demonstrating that there is a connection between the management of credit risk in Europe and a company's profitability. In addition, we provide light on the interaction between credit risk management measurements and profitability indicators for bank executives and investors. This is a very important topic. When it comes to making significant decisions, bank executives, financial analysts, investors, and regulators might all significantly benefit from considering these variables. The third contribution that may be made as a result of our research is that companies now have a more sophisticated understanding of how credit risk management might influence the bottom line of a financial institution. They need to rethink their approach to capital management and the distribution of non-performing loans at the very least until further evidence comes to light. In addition to this, it provides a method for regulators to evaluate whether or not the regulated ratio is having an effect on the profitability of banks. As a result of the negative association between the proportion of non-performing loans and the measures of profitability, regulators may impose more stringent limitations on banks in order to improve their respective circumstances with non-performing loans.

Reliability, Replicability, and Validity of the Research

Indicators were used in our research to assess various ideas. Researchers must think carefully about the validity and trustworthiness of the metrics they use (Bryman and Bell, 2011). In this chapter, we provide stakeholders the hard data from our research on the link between credit risk management and profitability. The goal is to aid bank management and investors in making better decisions and reducing waste. Therefore, it is essential that our study can be trusted. In this part, we'll go over two factors—reliability and validity—that help determine how trustworthy a piece of study is.

The idea of dependability is defined by Bryman and Bell as "the constancy of a measure of a concept." In determining whether or not a measurement is trustworthy, stability is an essential component. It places emphasis on the consistency of a measure throughout time, suggesting that the outcomes from using that measure would be relatively consistent (Bryman and Bell, 2011). Our research relies on quantifiable, objectively gathered data. Therefore, the results of our study cannot be disputed, as similar studies conducted at various times could not produce different outcomes. In addition, the test must be carried out in the same way, using the same data source, in order to preserve the reliability of the results (Bryman and Bell, 2011). Another criteria that is quite similar to dependability is replication, which places an emphasis on the feasibility of repeating the study (Bryman and Bell, 2011). It requires us to provide such a detailed "spelling out" of our study process that any other researcher would have a very hard time trying to replicate our results (Bryman and Bell, 2011). As part of this study, we provide a comprehensive account of our methodology in an effort to provide readers with a transparent and fair approach to the research we conducted. Our methods and findings are thus amenable to replication.

According to Carmines and Zeller (1979), validity is a sign of "abstract notion" since it is tested precisely against its intended target. Furthermore, Bryman and Bell consider it to be the single most crucial study criteria (2011). There are several kinds of validity. The validity of a measurement is the degree to which it provides an accurate representation of the concept being measured (Bryman and Bell, 2011). Causality, or the link between two or more variables, is at the heart of the concept of internal validity (Bryman and Bell, 2011). Additionally, there is a subtype of validity known as external validity. It has to do with how applicable the findings are outside of their original setting (Bryman and Bell, 2011). What this signifies is whether or not the study sample can be generalized to the entire population. There is also ecological validity. Ecological validity is more suitable in research that include interviews or surveys, as discussed by Cicourel (1982), and by Bryman and Bell (2011), who also note the important importance of social scientist intervention (such as laboratory or interview) on ecological validity. Due to the study's narrow emphasis on objective data analysis, we will not be defining "objective data analysis." Both credit risk management and profit maximization are central to our studies. We calculate NPLR and CAR to show how well we manage credit risk and utilize ROE and ROA to evaluate our financial success. The Basel II accords established a connection between banks' required minimum regulatory capital and their exposure to credit risk. Furthermore, research by Brewer et al. (2006) suggests that a lower NPLR may be indicative of a healthier economic climate and better credit risk management. In order for CAR and NPLR to be taken into account as accurate reflections of credit risk management. Kuan (2008) argues that banks may utilize return on equity (ROE) to evaluate how well they generate future profits. Furthermore, Goddard, Molyneux, and Wilson (2004) examine what factors influence European banks' profitability by focusing on ROE and ROA. Therefore, both ROE and ROA may be relied upon as accurate indicators of a company's financial health. Also, the purpose of this research is to establish a connection between the independent factors of CAR and NPLR and the dependent factors of ROE and ROA. We employ statistical tests for multicollinearity and heteroscedasticity to verify the validity of the connection. The statistical tests we ran revealed that while our data did not suffer from multicollinearity, it did suffer from heteroscedasticity. We have taken steps to correct for heteroscedasticity, which should prevent any bias in our estimation. Moreover, other factors may influence the connection and introduce bias, therefore we also include control variables. As suggested by Halsem, we will use firm size as our control variable (1968).

Specifically, we evaluate the size of a bank by calculating the natural log of its total assets, as suggested by Shalitand Sankar (1977). For the sake of generalizability, we intend to examine how credit risk management affects the bottom lines of commercial banks throughout all of Europe. Therefore, all European commercial banks should make up the population. The 47 major European banks are included in our study. The European banking market is dominated by a small number of very large banks. The impact of small banks on the European banking sector is negligible at best. As a result, our sample is the most representative one possible and has no issues with external validity.

Recommendations for Future Research and Model Development

In light of our findings, we need to formulate some ideas for further investigation. Adding extra markers is one solution suggested for the exploration model. We have already said that our approach is based on research from several disciplines that arrive at the same conclusion but are only applied to one country. Based on data from Pakistan, our model has a dismal R2 value, indicating that it does not provide a good overall match. In this vein, we propose certain enhancements to the model. The suggestion to include other elements may be considered. In this study, we use CAR to refer to credit risk management and ROE and ROA as efficiency indicators. In contrast to the indicators we linked to the exploration, other metrics might also demonstrate the executives' productivity and credit risk. Adding other markers to the mix might be an interesting approach to further explore this potential connection. In the meanwhile, it may help researchers optimize the accuracy of their exploration model. We also think that additional organizations should be included in this analysis. Three of the banks in Pakistan that we consider to be the most reliable are included in our analysis. Although we set out to include a wide range of financial institutions, we were only able to get access to data from three. If resources and time allow, expanding the sample size of the study might strengthen its persuasive power. Still, many Pakistani commercial banks are too small to even think of releasing annual reports or critical statistics. They may be influential in their own area, but it may be tough for them to reach consumers throughout the country of Pakistan. Connecting with financial institutions or subject-matter experts might be crucial for information gathering.

Sources Cited in the Study

Abata, M.A., 2014. Asset quality and bank performance: A study of commercial banks in Nigeria. Research Journal of Finance and Accounting, 5(18), pp.39-44.

Abdelaziz, H., Rim, B. and Helmi, H., 2022. The interactional relationships between credit risk, liquidity risk and bank profitability in MENA region. Global Business Review, 23(3), pp.561-583.

Abiola, I. and Olausi, A.S., 2014. The impact of credit risk management on the commercial banks performance in Nigeria. International Journal of Management and sustainability, 3(5), pp.295-306.

Abusharbeh, M.T., 2022. Determinants of credit risk in Palestine: Panel data estimation. International Journal of Finance and Economics, 27(3), pp.3434-3443.

Adeusi, S.O., Akeke, N.I., Adebisi, O.S. and Oladunjoye, O., 2014. Risk management and financial performance of banks in Nigeria. Risk Management, 6(31), pp.123-129.

Alshatti, A.S., 2015. The effect of credit risk management on financial performance of the Jordanian commercial banks. Investment management and financial innovations, (12,№ 1 (contin. 2)), pp.338-345.

Alshatti, A.S., 2015. The effect of the liquidity management on profitability in the Jordanian commercial banks. International journal of business and management, 10(1), p.62.

Ayodele, T., 2014. The impact of credit policy on the performance of Nigerian commercial banks.. The impact of credit policy on the performance of Nigerian commercial banks.

Bekhet, H.A. and Eletter, S.F.K., 2014. Credit risk assessment model for Jordanian commercial banks: Neural scoring approach. Review of Development Finance, 4(1), pp.20-28.

Belás, J., Smrcka, L., Gavurova, B. and Dvorsky, J., 2018. The impact of social and economic factors in the credit risk management of SME. Technological and Economic Development of Economy, 24(3), pp.1215-1230.

Berrios, M.R., 2013. The relationship between bank credit risk and profitability and liquidity. The International Journal of Business and Finance Research, 7(3), pp.105-118.

Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A.W. and Siddique, A., 2016. Risk and risk management in the credit card industry. Journal of Banking and Finance, 72, pp.218-239.

Chi, Q. and Li, W., 2017. Economic policy uncertainty, credit risks and banks’ lending decisions: Evidence from Chinese commercial banks. China journal of accounting research, 10(1), pp.33-50.

Gizaw, M., Kebede, M. and Selvaraj, S., 2015. The impact of credit risk on profitability performance of commercial banks in Ethiopia. African journal of business management, 9(2), p.59.

Ibtissem, B. and Bouri, A., 2013. Credit risk management in microfinance: The conceptual framework. ACRN Journal of Finance and Risk Perspectives, 2(1), pp.9-24.

Imbierowicz, B. and Rauch, C., 2014. The relationship between liquidity risk and credit risk in banks. Journal of Banking and Finance, 40, pp.242-256.

Kaaya, I. and Pastory, D., 2013. Credit Risk and Commercial Banks Performance in Tanzania: a Panel Data Analysis.

Kalu, E.O., Shieler, B. and Amu, C.U., 2018. Credit risk management and financial performance of microfinance institutions in Kampala, Uganda. Independent journal of management and production, 9(1), pp.153-169.

Kodithuwakku, S., 2015. Impact of credit risk management on the performance of commercial banks in Sri Lanka.

Lagat, F.K., Mugo, R. and Otuya, R., 2013. Effect of credit risk management practices on lending portfolio among savings and credit cooperatives in Kenya.

Malede, M., 2014. Determinants of commercial banks lending: evidence from Ethiopian commercial banks. European Journal of Business and Management, 6(20), pp.109-117.

MENDOZA, R.R. and Rivera, J.P.R., 2017. The effect of credit risk and capital adequacy on the profitability of rural banks in the Philippines. Scientific Annals of Economics and Business, 64(1).

Mengze, H. and Wei, L., 2015. A comparative study on environment credit risk management of commercial banks in the Asia‐Pacific region. Business Strategy and the Environment, 24(3), pp.159-174.

Naresh, C. and Rao, B.R., 2015. Credit risk management practices of Indian Commercial Banks. International Journal in Management and Social Science, 3(1), pp.89-94.

Noman, A.H.M., Pervin, S., Chowdhury, M.M. and Banna, H., 2015. The effect of credit risk on the banking profitability: A case on Bangladesh. Global journal of management and business research.

Nsambu, K.F., 2014. Factors affecting performance of commercial banks in Uganda a case for domestic commercial banks. Proceedings of 25th International Business Research Conference.

Ogboi, C. and Unuafe, O.K., 2013. Impact of credit risk management and capital adequacy on the financial performance of commercial banks in Nigeria. Journal of emerging issues in economics, finance and banking, 2(3), pp.703-717.

Switzer, L.N. and Wang, J., 2013. Default risk estimation, bank credit risk, and corporate governance. Financial Markets, Institutions and Instruments, 22(2), pp.91-112.

Warue, B.N., 2013. The effects of bank specific and macroeconomic factors on nonperforming loans in commercial banks in Kenya: A comparative panel data analysis. Advances in Management and Applied Economics, 3(2), p.135.

Aduda,J.,and Gitonga,J.(2011).The relationship between credit risk management and profitability among commercial banks in Kenya. Journal of Modern Accounting and Auditing,7(9):934- 946.

Afriyie, H. O. and Akotey, J.O. (n.d). Credit Risk Management and Profitability of Selected Rural Banks in Ghana . Catholic University College of Ghana.

Ahn, E. and Kang, H., 2018. Introduction to systematic review and meta-analysis. Korean journal of anesthesiology, 71(2), pp.103-112.

Alkhatib, A. and Harsheh, M. (2012). Financial Performance of Palestinian Commercial Banks. International Journal of Business and Social Science, 3(3), pp.175--184.

Al-Khouri, R. (2011). Assessing the Risk and Performance of the GCC Banking Sector - Tags: GULF Cooperation Council RISK management in business. [online] Connection.ebscohost.com. Available at: http://connection.ebscohost.com/c/articles/76481304/assessing-risk-performance-gccbanking-sector [Accessed 2 May. 2014].

Ally, M., 2004. Foundations of educational theory for online learning. Theory and practice of online learning, 2, pp.15-44.

Almazari, A. (2012). Financial Performance Analysis of the Jordanian Arab Bank by Using the DuPont System of Financial Analysis. International Journal of Economics and Finance, 4(4), p.p86.

Arat, T. and Bakan, Ö., 2011. Uzaktan eğitim ve uygulamaları. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 14(1-2), pp.363-374.

Avkiran, N. K. (1994). Developing an instrument to measure customer service quality in branch banking. International journal of bank marketing, 12 (6), pp. 10--18.

Babbie, E. (2004). The practice of social research. 10th edition. Belmont: Thomson Wadsworth.

Babbie, E. (2013). The Practice of Social Research, 13th edition. Cengage Learning. E-book. Available via: http://www.cengagebrain.com/content/9781133794882.pdf [Accessed: 2 April 2014].

Basilaia, G. and Kvavadze, D., 2020. Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research, 5(4).

Cusumano, M.A., Gawer, A. and Yoffie, D.B., 2021. Can self-regulation save digital platforms?. Industrial and Corporate Change, 30(5), pp.1259-1285.

Demiralay, R., Akdenizli, I. and Boztoprak, H., 2016. Estimating of student success with artificial neural networks. New Trends and Issues Proceedings on Humanities and Social Sciences, 3(7), pp.21-27.

Jumaat, N.F. and Tasir, Z., 2014, April. Instructional scaffolding in online learning environment: A meta-analysis. In 2014 international conference on teaching and learning in computing and engineering (pp. 74-77). IEEE.

Pillay, H., Irving, K. and Tones, M., 2007. Validation of the diagnostic tool for assessing tertiary students’ readiness for online learning. High Education Research and Development, 26(2), pp.217-234.

Usta, E., 2011. The examination of online self-regulated learning skills in web-based learning environments in terms of different variables.