Commercial banks

Commercial banks

ABSTRACT

Credit creation among the commercial banks plays a critical role in income generation. However, this activity is characterized by a number of risks which are a threat to both the lenders and the borrowers. Therefore, this study sought to determine the effect of credit risk on the financial performance of commercial Banks in Kenya. The independent variable included; credit risk, bank size, capital adequacy and the liquidity ratio. The dependent variable was financial performance as measured by ROA. 42 banks formed the population sample but data could only be collected for 42 banks. The study then employed descriptive statistics research design. The study utilized secondary data through which was obtained from the CBK annual reports and audited financial statements of the banks. Data was then analyzed through descriptive statistics and regression analysis via the Statistical Package for Social Sciences Software. The study found that there exist a strong negative relationship between credit risk and financial performance of the commercial banks in Kenya. Capital adequacy was also found to have a strong and positive association with the financial performance of the banks in Kenya. Bank size was discovered to have a weak and positive association with financial performance. Liquidity however was found to influence financial performance positively and significantly. The study recommends that policymaking entities and regulatory authorities in Kenya should develop effective prudential guidelines and polices to strengthen the management of credit risk.

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Credit creation is the fundamental income source for any financial institution. However, this activity is characterized by numerous risks to both the borrower and the lender (Li & Zou, 2014). At the point when banks issue loans, there is the risk of borrower defaulting. At the point when banks gather deposits and loans them to different customers, they put customers’ reserve funds at risk (Zewude, 2015). The default of handful borrowers can result into huge losses to the bank (Geste & Baesems, 2013). Commercial banks must hence ensure that the credit risks they are exposed to are mitigated since they affect their financial performance (Iqbal & Mirakhor, 2011). Any bank losses can prompt huge budgetary trouble influencing the entire economy (Bessis, 2003). Non-performing loans (NPLs) which is an indicator of credit risk has the potential of destabilizing the commercial bank’s general credit system and thus reducing the bank’s value (Afriyie & Akotey, 2012). Hence, credit risk needs to be properly managed (Bhattarai, 2016).

This study will be guided by the theory of liquidity Preference, which deals with the evaluation of credit worthiness of a borrower over a period of time (Huang, 2001). Theory of information Asymmetry which is concerned with how information reaches all the stakeholders in the lending industry and lastly the AST theory that describes the techniques that the institutions employs to distinguish risk-free customers from risk averse customers. AST theory proposes that banks, which give out loans, have inadequate information about their customers. Financial problems in the banking sector have led to a downfall of a number of banks in Kenya. The weaknesses of the methods used to manage risks in the sector have been cited to be the major cause of the collapse of banks in Kenya (Ongilo, 2012). The success of managing risks in lending out credit depends largely on the methodology applied in evaluating and awarding credit to borrowers (Ditcher, 2003). Credit decisions should therefore be based on thorough evaluation of borrower’s risks rather than achieving huge loan portfolios.

1.1.1 Credit Risk

Credit risk also known as default risk is the risk that the guaranteed loan and securities cash flows held by banks is not fully settled by the borrowers (Brown, 2012). In other words credit risk is the vulnerability related with borrowers’ credit reimbursements. This factor is the primary source of crises in the banking industry worldwide (Basel, 2010).When borrowers’ benefit value surpass their obligation they reimburse loans yet when borrowers’ benefit value are not as much as loan qualities, they don’t reimburse and they could in this manner practice their choice to default (Sinkey, 2002). Credit risk management is defined as the systems, controls and procedures, which are set by companies to ensure efficient payment collection from clients thereby minimizing potential of non- payment (Kalui & Kiawa, 2017).

When measuring credit risk, a lending institution must take into consideration three important factors; Probability of default which is the possibility that the borrower will not repay the loan within the set period, exposure of credit which is how big the debt will be should the default occur and estimated rate of recovery which is the amount of debt that can be recovered through collateral and freezing of assets. Therefore credit risk will be determined in this study by dividing the nonperforming loans by the total assets (Kithinji, 2010).

1.1.2 Financial Performance

Financial Performance measures the rate at which entities management can generate profit by utilizing assets efficiently while conducting its business (Pandey, 2008). Warsame (2016) described financial performance as the ability of an entity to make good use of it resources effectively to achieve its goals and objectives. Kagoyire and Shukla (2016) define financial performance as the organizations ability to operate efficiently, be profitable, expand and remain a going concern. All organizations strive to utilize their resources effectively in order to achieve a high performance level especially in financial terms. Thus, financial performance is the outcome of any of many different activities undertaken by an organization (Fujo & Ali, 2016). A firms’ financial performance is crucial in its existence. How effective and efficient a firm is in managing its resources for operations, financing and investing activities is clearly depicted in its high performance (Naser & Mokhtar, 2004).

Mishkin and Eakins (2012) identified three measures of firm performance: Return on Assets (defined as the proportion of net income to total assets), Return on Equity (defined as the proportion of net income to equity holders’ capital) and the net interest margin ratio. ROE measure is computed by dividing Net profit after Taxes by Total Equity capital. It shows the profitability level of a company in relation to the total sum of the shareholders capital invested. On the other hand, ROA indicates the return on all assets of the company and is frequently used by firms as an overall index of financial performance. It is computed by dividing Net Income after Taxes divided by Total Assets (Khrawish, 2011). As a result, ROA will be applied in measuring financial performance of commercial banks in Kenya.

1.1.3 Credit Risk and Financial Performance

It is the mandate of banks to maintain a loan loss provision account to safeguard against possible losses in case of default by its borrowers (Spaulding, 2017). The provisions are established to report incurred impairment losses either on specific loan assets or within a portfolio (Citi Group, 2015). As such, it is expected that an increase in credit risk increases the loan loss provision which is netted off against the interest income and loan assets. This translates to reduced income and profits for a bank (Aduda and Gitonga, 2011). It also increases the liability position of the bank which affects its ratios which investors use to assess its financial performance and position (Schroeck, 2002). For a commercial bank to maximize its risk-adjusted rate of return which is its core objective, credit risk must be managed with acceptable parameters (Zidan, 2014). The modern portfolio theory that was proposed by Markowitz (1952) also supports the association, linking the management of credit risks with financial performance by arguing that investors can combine different portfolios in order to achieve the maximum expected return given a certain level of portfolio risk which will improves the financial performance.

Kolapo, Ayeni, & Oke (2012) found that credit risk plays a major role on banks’ financial performance due to an inverse relationship between the two variables. Consequently, these risks pose a greater influence on the financial performance of financial institutions consequently, necessitating for a prudent credit risk management Policy. Vamishan (2015) also agrees, in his study of Indian banks, he found that credit risk is the most deadly of the risks firms confront and it significantly affected the Indian banks income. Kauki (2013) in his study also affirmed that the management of credit risks considerably affected the financial performance of financial institutions negatively. Sufi and Qaisar (2015) carried out a study on importance of management practices of credit risk on the performance of loan when the credit terms are taken and policy, appraisal of clients and control of credit risk in Pakistan. The study established that credit terms and appraisal of clients had a positive and significant impact on performance of loan, whereas credit policy and control of credit risk had insignificant but positive effect on loan performance.

1.1.4 Commercial Banks in Kenya

The total number of commercial banks in Kenya as at January 2019 stands at 42. However, a total of 11 commercial banks are listed at the NSE. Of the banks, one is under statutory management (Charter House Bank) and two others are under receivership (Chase Bank & imperial Bank). Commercial Banks are further classified into three different classes depending on the market share by net assets, advances, customer deposits and pre-tax profits by Central Bank of Kenya. Large banks have asset size of over 15 billion Kenya shillings, medium more than 5 billion shillings and small with asset size of less than 5 billion shillings. Six banks are classified as large, fifteen as medium and twenty two as small (CBK, 2011). The CBK is the regulatory body of all the financial institutions but Capital Market Authority also oversees the operations of the quoted commercial banks. All banks are obligated to observe particular prudential guidelines for example the least emergency cash and liquidity set by the central bank. The new development in banking sector include credit information sharing systems which has stirred improved efficiency in the banks and enhanced competition which has positively impacted on their financial performance (CBK, 2017).

Kenya’s top banks have posted impressive profits for 2018, benefiting from the Central Bank’s one-year earnings “protection” window in the implementation of the International Financial Reporting Standard (IFRS 9) which demands higher provisioning for bad loans (Herbling, 2017). Analysts had predicted that the banks would report nearly flat revenue performance in the face of declining credit to the private sector and growth in non-performing loans. An analysis of the 2018 financial results of top commercial banks shows that although all the banks maintained profitability, the situation would have been different if the loan-loss provision coverage had taken full effect in January. Under the IFRS9, which replaced the International Accounting Standard (IAS) 39, banks are expected to provide for projected loan losses rather than those already incurred, thereby reducing their profitability and eroding their capital base. Figures from the Central Bank, show that the volume of bad loans in Kenya’s banking industry increased by Ksh63.8 billion ($638 million) to Ksh298.4 billion ($2.98 billion) in June 2018, from Ksh234.6 billion ($2.34 billion) in June 2017, largely blamed on delayed payments by government agencies and the private sector, business stagnation and a slow uptake of housing in the real estate sector.

1.2 Research Problem

Banking is listed as one of the key pillars of Vision 2030 in Kenya. Banking is among the key drivers for the Kenyan economic growth because of the role they play in an economy (MacPherson, 2016). However, the sector has experienced turmoil owing to the new regulations enshrined in the constitution, which required banks to increase their minimum core capital requirements to one billion Kenyan shillings by 2012 (Banker, 2015). Also, the recent events involving the collapse of a number of banks such as chase bank, served as a catalyst for concern about credit risk necessitating this study (Kimotho & Gekara, 2016). Additionally, the global crisis affected the mobilization of deposits and trade reduction (Banker, 2015). As such, the banking sector has not been profitable as anticipated owing to the changes that have taken place over time. Under the IFRS9, which replaced the International Accounting Standard (IAS) banks are expected to provide for projected loan losses rather than those already incurred, thereby reducing their profitability and eroding their capital base.

Lin (2015) determined the influence of management of credit risks on the performance of banks in Germany from 2010 to and concluded that credit risk had no significant relationship with both ROA and ROE. Saeed and Zahid (2016) assessed the effects of credit risk on banks’ profitability amongst UK’s top five commercial banks during the financial crisis period (2007 to 2015). The results revealed that despite the crisis, the credit risk indicators depicted a positive correlation with profitability. Jathurika (2018) investigated the impact that credit risk had on the financial performance of Sri Lankan Listed Commercial Banks. He found that credit risk significantly influenced the financial performance of listed commercial banks in Sri Lanka with relationship being positive.

Isabwa and Nelima (2019) assessed the effect of credit risk on the financial performance of banks listed at the Nairobi Securities Exchange. Credit risk was found to have a significant negative effect on banks financial performance. Jemutai (2016) explored the effect of credit risk management practices on the financial performance of commercial banks in Kenya and found that credit risk impacted on financial performance negatively and to a significant extent. Kajirwa & Katherine (2019) assessed the relationship between the banks financial performance and its credit risk for firms quoted at the Nairobi securities exchange. The findings indicated that there credit risk was found to be significantly negatively related to financial performance as measured by ROE. Omondi (2016) concluded that the management of credit risks had a positive considerable influence on the performance of investment companies.

Different studies on credit risk management and financial performance have been conducted both locally and internationally. Such studies have given different results and different recommendations on credit risk management and financial performance of different institutions. Isabwa and Nelima (2019) found a negative association in contrast to Omondi (2016) who found a positive association. Moreover, the contexts of the studies were different; Omondi (2016) looked at the performance of investment firms while Isabwa and Nelima (2019) investigated listed commercial banks in Kenya. Due to the contradictory results of the studies, the current study therefore intends to interrogate the relationships further in an attempt to resolve the conflicts. The current study intends to fill this knowledge gaps by answering the question; what is the effect of credit risk on the financial performance of commercial banks in Kenya?

1.3 Objective of the Study

The objective of the study was to determine the effect of credit risk on the financial performance of commercial banks in Kenya.

1.4 Value of the Study

The findings of the study is of significance to various policy makers, including the government of Kenya, CBK and monetary policy committees who are involved in generating policies used by Commercial Banks in Kenya. It assists policy makers in determination of whether they need to amend the existing policies or develop new policies to strengthen commercial banks performance for better policy requirements and prudential guidelines. The Policy makers can use the research findings in formulating relevant policy necessary to curb and regulate loan defaults and ensuring that the banking industry remains financially stable.

The research added value to the body of knowledge available on credit risk exposure at a time when uncertainties and volatilities in the world economy are increasing every day. Kenyan companies and in particular banks are aggressively expanding into the wider East and Central African Region and as such their customer base is growing. These banks have to manage their credit risk amidst the growth and innovation in provision of their services. The use of alternative banking channels such as loan lending through mobile banking introduces a new facet which is not covered under traditional credit risk mitigation.

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CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter will review the theoretical framework of credit risk, determinants of financial performance, empirical studies, which are studies carried earlier relating to this study, conceptual framework and summary of literature review.

2.2 Theoretical Framework

The following are some of the theories concerning credit risk and performance in financial perspective that have been documented in financial literature. These theories are; Information Asymmetry Theory, Liquidity Preference Theory and Arbitrage Pricing Theory.

2.2.1 Information Asymmetry Theory

Akerlof proposed the theory of Information asymmetry in 1970. The theory is of the view that not all stakeholders in a market have access to important data (Eppy, 2005). He argued that in markets buyers tend to use market statistics when measuring the value of goods. Therefore, the buyer –most of the time –has a perception of averagely the entire market. On the converse, most often the seller possesses more detailed information regarding an item. Akerlof adds that this asymmetry proffers an incentive for the seller to offer less goods and services compared to the quality of goods in the market (Auronen, 2003). The less than average quality goods begin to dominate the market, which is referred to as adverse selection. He posits that this asymmetry can be reduced through intermediary market institutions, which allows the owners of goods that are above average to acquire full value for their products ensuring the market does not reduce to zero value (Auronen, 2003). Parrenas (2005) argued that Information Asymmetry gives lenders opportunity to sell their products or amenities for less than the regular marketplace price.

The theory further notes that it is difficult for financial institution to distinctly differentiate bad and good borrowers (Auronen, 2003); as a result, this may cause adverse selection of customers and also moral hazard problems The survival and growth of a bank is determined by its ability to deal with information asymmetry problems before, within and after the transaction has occurred (Uyemura & Deventer, 1993). Stiglitz and Weiss (1981) in his study noted that information asymmetry leads to credit rationing disadvantaging credit worth borrowers. It can clearly be seen that information asymmetry adversely affects efficient credit allocation. With the adoption of credit information sharing mechanism in both public and private sector through credit reference bureau (CDB) which allows access and sharing of information either voluntarily or compulsory has played a critical role in eliminating information asymmetry. He & Wang (2007) averred that reasonable financial institutions try to deal with asymmetry of information by incurring search expenses to obtain sufficient information about the borrower requesting for a loan.

In credit market, information asymmetry arises because the borrowers understand more facets allied to their investment projects compared to the lenders (H.C, 2016). It is worth noting that the asymmetry takes place as “ex post” or “ex ante.” According to H.C (2016), an ex-ante asymmetry is experienced if the lender is not in a position to understand borrowers with their different credit risks while providing loans. This can result in an adverse problem when it comes to selection. Notably, an adverse selection problem is experienced if an interest rates’ increase abandon risky borrowers in a market for funds. Thus, banks have a high probability of lending borrowers with high-risk due to their willingness to pay increased interest rates. On overage, however, such borrowers have more risks (Claus & Grimes, 2003). This theory is relevant to this study because adverse selection tends to increases the possibility of loans becoming bad credit risks.

2.2.2 Liquidity Preference Theory

This theory evaluates the demand for money, which is also known as the liquidity. The liquidity concept was developed for the first time by John Keynes in 1936. Keynes explained the process that is used to determine interest rates based on the supply and demand for money. He argued that money is the most liquid asset in the world. He also argued that the easier an asset can be changed into cash, the more liquid it is (Keynes, 1936). Liquidity preference theory claims that investors demand premiums for securities that have high maturities because they prefer holding cash that is less risky. According to this theory, the more liquid an asset is, the faster it is to dispose for its total worth (Shanken & Smith, 1996).

Three motives namely transaction, speculative and precautionary motives drive the demand for liquidity (Keynes, 1989). The transaction motive claims that people fancy liquidity because it assures them basic transactions when their incomes are not available. As a result, liquidity in this case is determined by income meaning that the higher the income the more money is required to cater for increased spending. The speculative motive claims that investors retain liquidity in the hope that bond prices will fall at one time (Pasinetti, 1997). Consequently, a fall in interest rate, leads to an increase in the demand for liquidity to hold unto until the interest rates increase (Reilly & Norton, 2006). The precautionary motive claims that people prefer to stay liquid in order to meet social unexpected needs that may call for unusual costs (Al-Khouri, 2011). The liquidity in this case is determined by the levels of incomes.

A commercial bank lending credit to investors may experience default problems if investors are unable to repay their debts when they fall due (Myers & Mjluf, 2004). This would force the banks to adopt risk rating measures to identify the credit risk exposed by the borrowers. The analysis would then help the banks to sort the risk depending on their importance. The higher the income of the borrower the lesser risky they are and vise vasa. In return, the bank’s management team would develop the necessary risk management practices to reduce the non-repayment rates. According to this theory, it is therefore in the interest of the lending institutions to reduce the levels of credit risks by making sure the loanees are credit worth before loan is advanced (Rogers, 1997).

2.2.3 Arbitrage Pricing Theory

This theory of arbitrage pricing was discussed by Ross (1977). According to this theory, there is a positive relationship between the risk of the asset and its expected returns. The arbitrage pricing theory was a modification of the capital asset pricing model and this model links returns to several variables in a linear form. The arbitrage pricing model is more robust than capital asset pricing model because it is easily extended to a multi period framework. The arbitrage pricing theory is founded on the assumption that investors in any market will always prefer more wealth to less wealth with certainty.

According to APT, despite the fact that a variety of forces can influence the return of firms, the effects eventually cancels out only on the formation of a portfolio which is well diversified. The arbitrage pricing model employs several factors in its multi variable model and each variable in the model is represented by a beta coefficient which measures the risk of each variable. The arbitrage pricing theory is comprised of the diversifiable risks and non-diversifiable risks in the market. The non-diversifiable risks are as a result of macroeconomic variables which cannot be diversified in the market (Ross, 1977).

2.3 Factors Affecting Financial Performance

This section discusses factors affecting financial performance which include; Credit risk, Size of the bank, Banks liquidity level, Capital adequacy, Interest rate and Managerial efficiency.

2.3.1 Credit Risk

Credit risk is possibility that a commercial bank loan client may fail in meeting his/her obligations as stipulated in the loan contract (Kwaku, 2015). Credit risk occurs whenever a lender (which in this case is the bank) is more exposed if the borrower or the counterparty fails to diligently honor his/ her loan repayment obligation (Warsame, 2016). As the core business of banking is lending, caution must be exercised to ensure that sound principles of lending are followed. Credit risk arises due to default, delayed repayments and reckless lending (Vadova, P. 2003). The process of controlling credit risk can be defined as the systems, controls and procedures that are set by companies to ensure prompt repayments are received from the clients; hence minimizing the potential non-payment (Kalui & Kiawa, 2015). Credit risk leads to financial failure if the borrower fails to honor his/her commitments under the contract and the failure has adverse impact on bank’s financial performance (Bhattarai, 2016). The credit risk position of a banking organization can be aggravated by insufficient institutional competence, ineffective credit strategies, inefficient management, low capital adequacy ratios and liquidity and also poor credit supervision by the central bank (Afriyie and Akotey, 2012).

Zidan (2014) posited that, sound management of credit risk improves practical oversight of asset quality and developing sound credit policies which in turn contribute to positive effect on financial performance. Kisgen (2008) further documented that credit rating as a consideration in the field of credit risk has a significant role because it indicates firm’s quality and has an effect on company cost of capital. Raqeeb, Zaidi and Cheema (2012) indicated that credit ratings have a bigger effect on capital structure along with size and ROA. An efficient measure for controlling credit risk balances the tradeoff between risks and reward thereby enhancing the future of an institution (Fameti & Fooladi, 2006). Shafiq and Nasr (2010) observe that banking institutions should not expose themselves to unnecessary risk.

2.3.2 Size of the Bank

The size of a firm may affect its goodwill, customer loyalty and stakeholder responsiveness (Foyeke, Odianonsen & Aanu, 2015).A bank’s size matters especially on its routine operations (Davis, 2012). Keeping all other factors constant, the size of the bank determines the level of risks its partners are exposed to. A big bank has more assets that will keep it going even in times of industrial crisis and this means that loans given out by larger banks are most likely to be repaid compared to loans advanced to customers by smaller bank loans. In addition, firms with large amounts of total assets have adequate collateral which they can pledge to access credit and other debt facilities compared to their smaller counterparts (Lee, 2009). There exists a direct relation between bank size and profits (Amato and Burson, 2007).

Some supporters for negative relationship between firm size and financial performance base their argument on agency theory (Chandrapala & Knápková, 2013). They argue that the difference between shareholders and managers leads to increased agency cost or information asymmetry. Black (2001) states that, when using scale and product mix there is a negative relation between returns and size of banks. On the contrary, Davis (2012) argued that there is inverse relationship between net return and bank size on small business lending. Large banks have ability to do business in a very different market sector than the small banks (Rayan, 2010), thus enabling a comparative advantage in market activities that may warrant incurring quite significant fixed costs but with an assurance that the bank will experience economies of scale. According to Davis (2012) activities that are market-based can cause unstable funding and increased leverage because the securities can be collateral in repos.

2.3.3 Banks Liquidity Level

Liquidity represents the amount of funds that is readily available to a firm at any given time, and the speed with which a firm can settle its short term liabilities when they fall due using the current assets (Alkihatib & Harasheh, 2012). Liquidity is therefore characterized by high turnover rate (Parrenas, 2005). The availability of large sums of money enables firms that are more liquid to exploit profitable investments faster than less liquid firms. Ferrouhi (2009) defines liquidity as the extent to which securities or assets or security can be sold or bought in a market without influencing the price of the assets. Note that one of the characteristics of liquidity is an increased level of trading activity (Gardner, 1986). He further asserts that, assets with the affinity to sell or be bought easily are referred to as liquid assets. Accordingly, Ferrouhi (2009) – in his study on the effects of liquidity on the banks financial performance, his findings clearly exhibit that adequate liquidity prevents the occasions of financial crisis in case of massive withdrawals by the public.

Assets are said to be liquid if such assets can be swiftly changed into cash. Whether a firm has or is coming up with readily available capital base to facilitate its operation, is a critical performance concern in relation to the firm’s liquidity. Liquidity of the firm is measured using liquidity ratios such as cash ratios, current ratios, quick ratios and the changes in the working capital of the firm (Brealey et al., 2001). The capability of the firm to pay its maturing obligations on a timely way is of vital importance and is closely related to firm’s performance and existence. The inability of the firm to maintain sufficient liquidity level can make the company insolvent and jeopardize its operations (Gitman, 2003). When external sources of funds are insufficient; an organization can finance its activities and investments using its liquid assets. Increased levels of liquidity permit an organization to transact with unexpected eventualities and achieve its responsibilities during times of low earnings (Onsomu, 2003). Liquidity ratio is measured by dividing Liquid Assets by Short-term Liabilities.

2.3.4 Capital Adequacy

Capital adequacy is among the major factors that affect the levels of financial performance and profitability of commercial banks. It represents the amount of cash that banks have at hand to support their financial activities. By so doing, it depicts the ability of a bank to undertake new investment opportunities and absorb effectively risks such as market and operational risks as well as credit risks. It therefore cushions a bank against adverse situations (Athanasoglou et al., 2005). The Capital Adequacy Ratio (CAR), which relates directly to the resilience of a firm to possible financial crises, measures the capital adequacy of a firm (Dang, 2011). Based on this fact, it affects directly the profitability of a bank. According to (Afriyie & Akotey, 2012), capital adequacy entails different kinds of financial capital that are well contemplated as liquid and reliable. Prudential guideline from CBK mandates all banks to adhere to the recommended CAR measured by core capital and total capital to total risk weighted average assets which are 10.5% and 14.5% respectively (CBK 2015). Ongore and Kusa (2013) discovered in their study that capital influenced the returns of commercial banks in Kenya positively. Also Yahaya, Mansor and Okazaki (2016) alluded to the fact that capital adequacy is a vital factor in the determination of risks being assimilated by financial institutions. They further argue that it plays a key part in the security of banks and portrays banks’ images as a whole, possibly drawing public assurance to invest in the bank. Sagmi & Tabassum (2010) concluded in their study that profitability and capital adequacy of commercial banks are positively correlated since the former informs investment decisions made by banks through consideration of various risks affecting probable future ventures. CAR ratio is measured by total capital divided by total risk weighted assets.

2.3.5 Interest Rate

Interest rate is a percentage of the principal the borrowers pay to use the money they borrow from creditors. In the context of financial institutions, commercial banks being inclusive, the borrower-lender relationship is arrived at from two angles. The first is the CBK that acts as a lender and the commercial banks that acts as borrowers; the second is that of commercial banks acting as the lender and clients as borrowers (Investor Words, 2015).

In Kenya, the Central Bank acts as a regulator by setting up the base lending rate from which commercial banks can lend at. The wider economy views the interest rate as a regulator of the level of the economy in terms of inflation levels. However, the rate from the perspective of commercial banks is that of a key determinant of their financial performance. The variables used in this case are normally the interest rate spread. Note that the interest spread refers to the difference between the borrowing rate of commercial banks from central bank and the lending rate to the clients. The general statement is that low interest rate spread affects the profitability of firms positively.

Irungu (2013) looked at a number of variables related to interest rate spread such as gross domestic growth rate, liquidity risk and the savings/deposit rate. The results revealed that there is a positive correlation between the performance and interest rate spread. However, Macro think institute (2014) research established that interest rates affect the banks interest income negatively. As such, if banks increase or decrease the interest rate value (X), the bank’s profitability value (Y) will automatically be increased or decreased depending on the direction of change. This outcome differs from the suggestions made in the literature because the Pakistan’s banking sector had huge banking spread by the time of the research.

Langat (2013) sought to identify the effect allied to interest rates spread and performance in the Kenyan banking industry. Further, he sought to establish the effect of banking regulations and credit risk on interest rates spread as well as their probable effects on the performance of banks. The findings of the research established that credit risk, CBK regulations, and various macro-economic variables affect the interest rates spread. In fact, the spread proffered enough margins for commercial banks to proceed with their operations in the market. As a result, the performance of the banking industry was equally affected. The study established a positive correlation between the performance of Kenyan commercial banks and the interest rates spread.

2.3.6 Managerial Efficiency

Dahigaard et al., (2012) posits that management commitment, focus on clients and employees, attention to facts and details, consistent perfection and improvement and everyone’s participation are the main principles of quality management and control. Poor management of expenses contributes to low profitability (Sufian & Chong, 2008).

In his study on the effects of corporate governance on the liquidity and credit risk of the Ethiopian commercial banks, Abate (2014) discovered that Central Bank’s regulations affected measures of risk negatively. Management efficiency had a positive impact on both risks. Board of directors meeting frequency had a negative impact on both risks, firm size and inflation had significant impact on credit risk but insignificant for liquidity. The research concluded that corporate governance had an impact on bank risk control.

The default rate is a major indicator of risk management and control in the banking industry. Consequently, it can be used to predict the financial performance of a bank. Managerial efficiency will determine how the risk will be managed and more emphasis should be put on efficiency of the managers as this contributes significantly to bank performance (Poudel, 2012).

2.4 Empirical Review

Mukhtarov, Yüksel & Mammadov (2018) investigated factors influencing the increase in credit risk of Azerbaijani banks. Azerbaijan banks with the largest asset size in ascending order which were 10 in number were selected as the population for the study. 2010 to 2015 period covered the study. Panel logit methodology was used in data analysis. The study concludes that capital adequacy ratio, interest rate and total assets had an inverse relationship with credit risk and were the main factors influencing the credit risk of the 10 Azerbaijan banks. However, unemployment rate was found to be positively related to credit risk. Consequently, Azerbaijani banks can reduce the negative effects that capital adequacy ratio and total assets have on credit risk by increasing these ratios amount.

Jubouri (2018) explored the impact of credit risk management in financial market indicators (Iraqi Market for Securities). 8 private banks in Iraq formed the population of the study. The period for the study was 2007 to 2016. Data was collected from the annual reports of banks and official publications from the Iraqi Stock Exchange. Microsoft Excel 2013 and SPSS were employed to analyze data. The results showed a positive association between credit risk management and banks strategies in achieving their objectives. The study recommends that credit risk managers should double their efforts in credit risk management, in particular pay special attention when granting loans through reliance on information that accurately represents the borrower in terms of their ability to repay the loan in order to improve on their decision making abilities.

Lin (2015) determined the influence of management of credit risks on the performance of firms in Germany from 2010 to 2014. The study population was 500 firms; the sample of the study was 200 firms. CRM was measured by management efficiency, firm size and liquidity management while financial performance was measured by ROA. Secondary data was utilized in the paper and was analyzed by inferential and descriptive statistics. He made conclusion that the management of credit risks had no significant influence on the performance of the firms.

Kajirwa & Katherine (2019) assessed the relationship between the banks financial performance and its credit risk for firms quoted at the Nairobi securities exchange. The period used was 2014 to 2018. 11 commercial banks formed the population sample and the longitudinal research design was used. Credit risk was found to be significantly negatively related to financial performance as measured by ROE. The context of the study was different as it focused on listed banks at the NSE while the current study focuses on all commercial banks in Kenya. Moreover, the study used the longitudinal design while the current study will employ the descriptive design in addition to using ROA to measure financial performance as opposed to ROE.

Muigai & Maina (2018) investigated how credit risk management practices impacts on the performance of commercial banks in Kenya. The period for the study was 2017. Loan appraisal, lending requirements, credit management tools and loan recovery process were the credit management practices that formed the independent variable for the study. 39 commercial banks formed the population. Commercial banks credit officers and finance managers formed the observation unit totaling to 78 respondents. All the credit management practices were found to have a significant positive relationship with financial performance of banks in Kenya. The study however focused on credit risk management practices while the current study focuses exclusively on the credit risk component.

Jemutai (2016) explored the effect of credit risk management practices on the financial performance of commercial banks in Kenya. Data was collected for the period 2012 to 2016 and the study used the descriptive design. 30 banks formed the population sample for the study. Secondary data from published reports and audited financial statements was relied upon in the study. Data was analyzed using descriptive and regression analysis. The relationship between non-performing loans and ROE was found to be negative and significant. The study recommends that banks should adopt a good credit risk management system so as to minimize risk exposure for profits to be maximized. The context of the study was different as ROE was used as the financial performance measure.

Githaiga (2015) determined the relationship between credit risk management and the financial performance of listed commercial banks in Kenya. The period covered was 2010 to 2014. 11 banks in Kenya formed the population sample. Data was collected using the secondary technique. Regression analysis results led the researchers to conclude that credit risk led to decreased return on asset for the banks. The study focused on listed banks while the current study will look at commercial banks in Kenya.

2.6 Research Gap

The synopsis of literature review emphasizes that different realities of financial performance have been exuded in relations to credit risk. Kajirwa & Katherine (2019) finds a negative relationship while Muigai & Maina (2018) finds a positive relationship. Moreover, majority of the earlier studies were carried out in European and Asian countries which are mostly developed economies compared to Kenya, Lin (2015) carried out a study of Germany while Jubouri (2018) carried out a study of Iraq. Even for the studies done locally, the concept and context is different, Githaiga (2015) focused on listed banks while Jemutai (2016) focused on 30 banks. The current study intends to fill the gap with regards to the mixed conclusions obtained in the previous studies by attempting to establish the effect of credit risk on the financial performance of commercial banks in Kenya.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

The research methodology chapter explains the research methodology to be used in the research study. This chapter covers research design, population of study, data collection methods to be used and data analysis technique to apply in the study.

3.2 Research Design

The study adopted a descriptive research design which is defined as a design that is used when the researcher needs to depict specific behavior as it occurs in the environment (Khan, 2008). Zikmund (2003) notes that, the main quality of this design is that the variables cannot be controlled by the researcher as he can only describe what is occurring or has occurred.

3.3 Target Population

The target population is the whole set of elements with shared visible attributes, which the researcher is interested in studying in order to make some inferences (Cooper & Schindler, 2008). The study focused on the 42 commercial Banks that operate in Kenya (CBK, 2019). See (Appendix I) attached.

3.4 Sample

The target study sample comprised of 38 commercial banks operating in Kenya. The commercial banks that are under statutory management and receivership for the period of year 2018 did not form part of the study (Chase bank, Imperial bank, Dubai bank and Charterhouse bank). Statistically, in order for generalization to take place, a sample of at least 30 must exist (Cooper & Schindler, 2003).

3.5 Data Collection

Data was obtained from published financial reports of the commercial banks for the period 2009 to 2018. The financial reports were obtained from audited financial statements of the banks and CBK reports, and website. Data that was collected included; Total non-performing loans, Total gross loans, Total assets, Total capital, Total deposits and Total liquid asset.

3.6 Data Analysis

To analyze data, the research used descriptive statistics and regression analysis using the SPSS software. Descriptive statistics was used to summarize the collected data using the mean, standard deviation and the coefficient of variation. Multivariate regression and correlation analysis were used to establish the relationship between the dependent variable and the independent variables.

3.6.1 Analytical Model

The study applied the following regression model:

Y = β0 + β1X1+ β2X2+ β3X3+ β4X4 + ε

Where;

Y= Financial performance as measured by ROA

β0 = Estimated value of Y when all the other variables are zero

βi (i= 1, 2, 3, 4) = Coefficients of regression

X1=Credit risk as measured by Non-performing loans / Total loans

X2= Size, as given by; Natural logarithm of total gross loans

X3= Liquidity, as given by Net liquid loans divided by total deposits.

X4= Capital adequacy as given by the total capital divided by total risk weighted assets

ϵ= Error Term

3.6.2 Tests of Significance

The study used the F test and the t-test to test for statistical significance. T-test was used to establish the significance of individual variables. The F test was be used to test the statistical significance of the regression model.

CHAPTER FOUR

DATA PRESENTATION, ANALYSIS AND DISCUSSION OF FINDINGS

4.1 Introduction

This chapter discusses findings that were obtained in the analysis, using the methodology that was discussed in chapter three above. The chapter discusses the summary statistics of the variables that were used and the other statistical measures of the variables. Although the study looked at the 42 banks it only managed to gather data for 32 commercial banks in Kenya. For some banks complete data could not be obtained for the entire study duration hence the 32 banks. The period for the study was 2009 to 2018.

4.2 Descriptive Statistics

Descriptive statistics gives a presentation of the mean, maximum and minimum values, skewness, kurtosis and standard deviation of the variables applied in this study.

Table 4.1: Descriptive Statistics of Selected Variables

Minimum Maximum Mean Std. Deviation Skewness Kurtosis

ROA -.157 .563 .028 .060 6.196 47.134

Credit Risk .002 2.588 .099 .172 10.039 137.716

Capital Adequacy -.291 .970 .230 .109 1.957 11.216

Liquidity .004 3.067 .771 .378 1.833 7.703

Bank Size 6.012 12.413 7.704 .997 2.814 11.016

Bank size and liquidity had the highest mean amongst all the variables, higher than the mean of ROA, Credit risk and Capital adequacy. This implies that financial performance relies heavily on both the size of the bank and the liquidity of the bank. Hence, the larger the bank, the better the financial performance and the more liquid a bank is the higher the financial performance. The variables seemed not to be normally distributed since their skewness were more than zero. All the variables were positively skewed. Additionally, all the variables seemed to have a more peaked than normal distribution since their kurtosis was greater than 3. Bank size and liquidity had the highest standard deviations of 0.997 and 0.378 respectively. This shows that the two variables have very high volatility; this is because the two variables are dependent on several other macro and micro-economic factors. This suggests that on average, bank size deviates from the mean by about 0.997 while liquidity deviates from the mean by 0.378. Financial performance as measured by ROA had the lowest standard deviation of 0.006, implying that on average, ROA will deviate from the mean by about 0.006 units. It also shows that ROA had the lowest volatility during the period under consideration. Credit risk and Capital adequacy had standard deviations of 0.172 and 0.109 respectively, which are relatively small.

4.2 Correlation Analysis

In order to find out the strength and pattern of the connection between the study variables, the researcher conducted correlation analysis. Strength of the relationship between the variables is either weak, moderate or strong, while the direction is either positive or negative. Strength of the connection between the variables is determined by Pearson coefficient r while the p values signify whether this relation is significant. Pearson correlation was employed to analyze the level of association between financial performance as measured by ROA and the independent variables for this study credit risk and the control variables; bank size, capital adequacy and the liquidity of the bank).

Table 4.2 Correlation Analysis

ROA Credit Risk Capital Adequacy Liquidity Bank Size

ROA Pearson Correlation 1 -.090 .204** .190** .085

Sig. (2-tailed) .108 .000 .001 .127

N 320 320 320 320 320

Credit Risk Pearson Correlation -.090 1 -.013 .108 -.074

Sig. (2-tailed) .108 .811 .054 .188

N 320 320 320 320 320

Capital Adequacy Pearson Correlation .204** -.013 1 -.035 -.036

Sig. (2-tailed) .000 .811 .529 .519

N 320 320 320 320 320

Liquidity Pearson Correlation .190** .108 -.035 1 .284**

Sig. (2-tailed) .001 .054 .529 .000

N 320 320 320 320 320

Bank Size Pearson Correlation .085 -.074 -.036 .284** 1

Sig. (2-tailed) .127 .188 .519 .000

N 320 320 320 320 320

**. Correlation is significant at the 0.01 level (2-tailed).

Source: (Research Findings, 2019)

Table 4.2 above indicates that ROA is positively correlated with capital adequacy, liquidity and bank size. The correlation between ROA and capital adequacy is positive and significant at 5% significance level (r = 0.204, p = 0.00 < 0.05). The correlation between ROA and liquidity is also positive and significant at 5% significance level (r = 0.190, p= 0.001 < 0.05). However, ROA relationship with bank size was found to be positive but non-statistically significant (r = 0.085, p= 0.127 > 0.05). Credit risk relationship with financial performance as depicted by ROA was found to be negative but non-statistically significant (r = – 0.09, p= 0.108 > 0.005). Multicollinearity is of two forms namely; perfect and imperfect where imperfect multi-collinearity causes errors and variation of variables to rise sharply. The correlation coefficient is not supposed to exceed 0.9 as it relates to problems of multi-collinearity. Although the independent variables in our study had an association to each other, the association was not strong to cause Multicollinearity as all the r values were less than 0.9. This implies that there was no Multicollinearity among the independent variables and therefore they can together be used as determinants of financial performance of commercial banks in regression analysis.

4.4 Regression Analysis

Return on Asset was regressed against four predictor variables; credit risk, capital adequacy, liquidity of the bank and the size of the bank. The study obtained the model summary statistics as illustrated in table 4.3 below.

Table 4.3: Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson

1 .305a .093 .082 .057534 .858

1. Predictors: (Constant), Bank Size, Capital Adequacy, Credit Risk, Liquidity
2. Dependent Variable: ROA

Source: (Research Findings, 2019)

The research arrived at an adjusted R squared of 0.082. This means that 8.2 % of the total variance in financial performance of the commercial banks in Kenya can be attributed to; credit risk, capital adequacy, bank size and liquidity of the bank. Other variables not included in the model justify for 91.8 percent deviations in financial performance. The model summary shows that there is a significant (R=0.305) relationship between performance of commercial banks and the independent variables. A durbin-watson statistic of 0.858 indicated that the variable residuals were serially correlated since the value was less than 1.5.

Table 4.4: Overall Model Significance

Model Sum of Squares Df Mean Square F Sig.

1

Regression .107 4 .027 8.102 .000b

Residual 1.043 315 .003

Total 1.150 319

1. Dependent Variable: ROA
2. Predictors: (Constant), Credit risk, Capital adequacy, Bank size, Liquidity

Source: (Research Findings, 2019)

In order to determine the goodness of fit of the regression model an analysis of variance was sought. The results of this analysis are shown in table 4.4 above. From the results, the F statistic is 8.102 is greater than F critical (2.4). P value was 0.000 smaller than the critical p value of 0.05. Therefore, the model was significant at 95% confidence level and thus there is at least one significant independent variable. Hence, for ROA, independent variables are good joint predictors.

Table 4.5: Regression Coefficients

Model Unstandardized Coefficients Standardized Coefficients T Sig.

B Std. Error Beta

1 (Constant) -.033 .027 -1.238 .217

Credit Risk -.037 .019 -.107 -1.968 .050

Capital Adequacy .116 .030 .211 3.924 .000

Liquidity .032 .009 .201 3.562 .000

Bank size .002 .003 .028 .498 .619

1. Dependent Variable: ROA

Source: (Research Findings, 2019)

Results tabulated in table 4.5 above indicate the coefficients, t values and significance levels of variables under study. The constant has a coefficient of -0.033, non-statistically significant at 95% confidence level (p value = 0.217 > 0.05). Therefore, holding all other variables at zero level, ROA will be equivalent to constant based on the model. Credit risk has a negative coefficient of -0.037 indicating that, an increase in credit risk will lead to a decrease in ROA. Also, credit risk relationship with financial performance (ROA) was found to be statistically significant at 95% confidence level as indicated by the p value of 0.05 < 0.05. Capital adequacy has a positive coefficient of 0.116, statistically significant at 95% confidence level (p value = 0.000 < 0.05). Therefore, capital adequacy as a predictor of ROA, its increase contributes to an increase in ROA and vice versa. Liquidity also has a positive coefficient of 0.032 and p value of 0.000 < 0.05 which is statistically significant. Thus, liquidity as a predictor, its increase contributes to increase in ROA and vice versa. Bank size cannot predict ROA at 95% confidence level. This is inferred from the coefficient results where has a positive coefficient of 0.002 and p value of 0.619 >0.05. This implies that, an increase in bank size will result to an increment in ROA but not to a significant extent and vice versa.

Using only the significant variables, the fitted model becomes:

Y = – 0.033 – 0.037X1 + 0.032X3+ 0.116 X4

Where;

Y= Financial performance as measured by ROA

X1=Credit risk as measured by Non-performing loans / Total loans

X2= Size, as given by; Natural logarithm of total gross loans

X3= Liquidity, as given by Net liquid loans divided by total deposits.

X4= Capital adequacy as given by the total capital divided by total risk weighted assets

4.5 Discussion of the Results

Regression results revealed a negative and significant association between Credit risk and financial performance as measured by ROA. The results portray that an increase in non‐performing loans influences the financial performance of commercial banks in Kenya negatively. When non-performing loans are on an upward trajectory in a bank then there will be a delay in the expected or projected cash inflows or rather no cash will be received at all should the delinquent loan become a default. This will negatively impact on the financial performance of a financial institution. These findings are also in conformity with the results of Abiola and Olausi (2014) plus those of Adeusi, Akeke, Adebisi and Olandunjoye (2013) who stressed that credit risk management impacted on the performance of banks negatively and significantly. Boahene, Dasah and Agyei (2012) and Fan and Yijun (2014) however disagree, they observed that credit risk impacts on financial performance negatively.

Capital adequacy relationship with financial performance was found to be positive and statistically significant. Capital provides cushion against loss thus ensuring safety and dependability of the banking institutions (Wachiuri, 2012). Dang (2014) observed that a high bank capital reduces the chance of credit risk because the adequacy of capital is judged based on Total Risk Weighted Ratio (TRWR). This reflects the inner capability of financial institution to bear losses over difficulty. It also boosts depositors’ confidence by protecting them and promoting the stability and efficiency of financial system (Sangmi & Nazir, 2010). The findings are consistent with those of Ongore and Kusa (2013) and Yahaya, Mansor and Okazaki (2016) who discovered in their studies that capital adequacy ratio influenced the returns of commercial banks in positively. Berger and Patti (2006) however, find contradictory results, using the sample of US banking industry; they found that lower capital ratios are linked with higher bank performance.

Liquidity was also found to be positively related to financial performance and to a significant extent. Liquidity is fundamental for banks as it helps to eliminates costs of maintaining cash deficits. (Chiu, Tombazzi & Leung, 2010). Pandey (2010) posits that excessive liquidity is detrimental to a financial institution because idle funds generate nothing but also appear as assets in a firms books hence banks should find a balance between high liquidity and lack of liquidity. Dang (2012) concurs, he found that profitability and liquidity are positively related. Similarly, Otieno, Nyagol, and Onditi (2016) were able to establish that organizational success could also be attributed to having sufficient liquid cash in the firm to meet daily expenses and long term obligations. However, Marinković and Radović, (2014) found otherwise, they research on the connection between liquidity and profitability of banks yielded inconclusive results.

Regression results also revealed that bank size and financial performance had a positive relationship. However, the relationship was found to be weak. Large banks should take advantage of economies of scale to get cost advantages which coupled with improved operational efficiency will lead to more profits (Adusei, 2015). Goddard et al., (2004) agrees, he observes that the association between bank size and financial performance is also positive and weak. The findings contradict that of Sufian and Habibullah (2009) who discovered a negative relationship between profitability and bank size. The insignificant link between bank size and bank performance confirms the agency theory of the firm. A growth strategy in the Kenyan banking sector may not be to the best interest of the shareholders.

The Pearson correlation coefficients between the variables revealed strong positive correlation existing between capital adequacy and financial performance. The relationship between liquidity and financial performance was also found to be strong and positive. The research also exhibited existent of a weak positive relationship between the size of the bank and the financial performance of commercial banks in Kenya while Credit risk was found to have a negative and weak relationship with financial performance.

The model summary revealed that the independent and control variables: credit risk, capital adequacy ratio, bank size and liquidity ratio explains 8.2 % of changes in the dependent variable as indicated by the adjusted value of R2 which implies that the are other factors not included in this model that account for 91.8% of changes in financial performance. The model is fit at 95% level of confidence since the F-value is 8.102.

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter summarizes the findings, draws conclusion based on the study objectives and the researcher makes recommendations for further research. The limitations of the study are also highlighted.

5.2 Summary of Findings

This study examined the effect of Credit risk on the financial performance of commercial banks in Kenya. The study centered on 42 commercial banks governed and licensed in Kenya. However, out of the 42 banks, data could only be obtained for 32 banks. Secondary data was collected from the audited banks financial statements and the Central Bank of Kenya for the period 2009 to 2018. To determine the associations between the variables under study, a multiple linear regression model was put to use.

Correlation analysis found a negative and insignificant association between Credit risk and ROA. Capital adequacy ratio, Liquidity and Bank size were all found to be positively correlated with financial performance. However, the association was insignificant for bank size but significant at 95% confidence level for both the liquidity and the capital adequacy ratio.

The co-efficient of determination R-square value was 0.082 which implies that about 8.2 percent in financial performance deviation can be expounded by the four selected independent variables while 91.8 percent in the variation of ROA was associated with other variables not covered in this research. The study also found that the independent variables had a strong correlation with ROA (R=0.305). ANOVA results show that the F statistic was significant at 5% level with a p=0.000. Therefore the model was fit.

The regression results revealed that when all the independent variables selected for the study have zero value, ROA of the banks will be decrease by 0.033 units. A unit change in credit risk holding other factors constant will result in a decrease in ROA by 0.037 units; A unit change in capital adequacy ratio holding other factors constant will increase ROA by 0.116 units; a unit change in liquidity holding other factors constant will increase ROA by 0.032 unit; a unit change in bank size will increase ROA by 0.002.

5.3 Conclusion

The research concludes that credit risk has a negative and significant relationship with financial performance of commercial banks in Kenya. Therefore, it can be implied that increased credit risk results into a decrease in the financial performance of banks in Kenya. Abiola and Olausi (2014), Adeusi, Akeke, Adebisi and Olandunjoye (2013) also concluded in their study of Nigerian firms that credit risk and financial performance have a positive and significant.

The study concludes that capital adequacy ratio impacts on financial performance of banks in Kenya positively and to a significant extent. Similarly, Yahaya, Mansor and Okazaki (2016) concluded in their studies that capital adequacy ratio influenced the returns of commercial banks in positively.

The study also concludes that bank size has a weak and positive relationship with financial performance of commercial banks in Kenya. This suggests the absence of significant economies of scale in the Kenyan banking sector. The results confirm the findings of Heffernan and Fu, (2008) in their study of profitability of different Chinese banks for the period 1996-2006. The insignificant link between bank size and bank performance confirms the agency theory of the firm.

The study also concludes that liquidity ratio positively and significantly influenced the financial performance of commercial banks in Kenya. Aremu and Ajibike (2015) also studied the effect of liquidity on Nigerian banks Performance. They also found that liquidity had a strong positive association with the financial performance of Nigeria commercial banks.

5.4 Recommendations

From the outcome of this study, the paper recommends the adoption of credit risk management by the policy makers. This is due to the fact that credit risk management is aimed at mitigating the financial losses caused by default risk. Mitigating the financial losses caused by default risk is key especially when a bank is faced with adverse situations. The effect of credit risk management can be made more significant if sufficient changes are made in terms of the adoption and full implementation of this practice.

Management of commercial banks in Kenya ought to strive to minimize as much as possible the Non-performing loans since they negatively affect financial performance. The researcher further recommends capital adequacy and liquidity effects to be managed aptly as these affect the financial performance positively.

Credit policy and practices by commercial banks should be checked properly. Through this there would be reduction in loss on Non-Performing Loans that increases their expenditure and subsequent improving their financial performance. Each and every bank should have in place established Credit Policies (“Lending Guidelines”) that stipulates clearly top administrator’s business growth significances and loans guidelines that should be adhered for loans approval. The guidelines on lending should be updated once in a year in order to reflect variations in the economy and banks loan portfolio evolution and be spread to all credit officers.

5.5 Limitations of the Study

The findings of this study are limited to commercial banks in Kenya and not to other financial institutions like SACCOS and Microfinance institutions, which deal with lending as they apply different credit risk management strategies and also lend under different terms and conditions.

The investigation was conducted within a period of 10 years. It made use of the secondary data that actively involved the accounting ratios. The problem of using accounting ratios is that they are historical in nature hence they do not reflect the current situation in the financial market.

The process of data collection from published financial statements was very time consuming and data was incomplete hence impossible to include all 42 commercial banks. The assumption was that the auditor’s report gave a true and fair view, but it could have been prone to errors and misstatements.

The data that was employed in this study was only the secondary data which was not able to capture, the qualitative aspects of financial performance which are also significant for example offering goods and services of high quality to the customers.

5.6 Suggestion for Further Research

The paper makes recommendations that similar studies be conducted but now in the Eastern Africa region which involves the incorporation of the commercial banks in Kenya, Uganda, Tanzania, Burundi and Rwanda to compare the outcome with the Kenyan banking industry.

The paper suggests that a study be carried out to assess the effect of risk management on the economic growth this will be interesting to see whether a connection exists between credit risk and economic development.

This study recommends that in the near future, a research to be conducted which should incorporate the primary data for example the use of qualitative aspects of financial performance which were not captured by the study model.