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Credit risk is most simply defined as the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms. The goal of credit risk management is to maximize a bank’s risk-adjusted rate of return by maintaining credit risk exposure within acceptable parameters. Banks need to manage the credit risk inherent in the entire portfolio as well as the risk in individual credits or transactions. Banks should also consider the relationships between credit risk and other risks. The effective management of credit risk is a critical component of a comprehensive approach to risk management and essential to the long-term success to any banking organization. (Heinemann)
Financial institutions are just beginning to realize the benefits of credit risk management models. These models are designed to help the risk manager project risk, measure profitability, and reveal new business opportunities (Heinemann).
Risk management takes a firm wide view of the institution’s risks, profits and opportunities so that it may ensure optimal operation of the various business units. The risk manager has the advantage of knowing all the firm’s risks extending across accounting books, business units, product types, and counterparties. By aggregating the risks, the risk manager is in the unique position of ensuring that the firm may benefit from diversification. Risk management is a complicated, multifaceted profession requiring diverse experience and problem solving skills.
While financial institutions have faced difficulties over the years for the multitude of reasons, the major cause of serious banking problems continues to be directly related to lax credit standards for borrowers and counterparties, poor portfolio risk management, or a lack of attention to changes in economic or other circumstances that can lead to a deterioration in the credit standing of a ban’s counterparties. This experience is common in both G-10 and non-G-10 countries. (Heinemann)
Basel is an agreement that requires the imposition of risk-based capital ratios on banks in major industrialized countries. Considering the weaknesses of the simple capital-to-assets ratio, in 1988 U.S bank regulators formally agreed with other member countries of the Bank for International settlements (BIS) to implement two new risk-based capital ratios for all commercial banks under their jurisdiction. The BIS phased in and fully implemented these risk based capital ratios on January 1, 1993, under what has been known as the Basel Accord (now called Basel I).
The 1993 Basel Agreement explicitly incorporated the different credit risks of assets (both on and off the balance sheet) into capital adequacy measures. This was followed with a revision in 1998 in which market risk was incorporated into risk-based capital in the form of an “add-on” to the 8 percent ratio for credit risk exposure. In 2001, the BIS issued a consultative document, “The new Basel Accord” (now called Basel II). It proposed the incorporation (fully effective in 2007) of operational risk into capital requirements and updated the credit risk assessments in 1993 agreement. This agreement was adopted in June, 2004. (Cornett)
The Basel I capital accord focused only on minimum regulatory capital requirements. The Basel II capital accord broadens this focus by describing the supervisory process in the Basel II capital accord by “three pillars”:
Pillar 1- Minimal regulatory capital requirements.
Pillar 2- Supervisory review of capital adequacy.
Pillar 3- Market discipline and disclosure.
The sound practices set out in Basel document specifically address the following areas:
Establishing an appropriate credit risk environment;
Operating under a sound credit-granting process;
Maintaining an appropriate credit administration, measurement and monitoring process;
Ensuring adequate controls over credit risk
Although specific credit risk management practices may differ among banks depending upon the nature and complexity of their credit activities, a comprehensive credit risk management program will address these four areas. These practices should also be applied in conjunction with sound practices related to the assessment of asset quality, the adequacy of provisions and reserves, and the disclosure of credit risk, all of which have been addressed in other recent Basel Committee documents. (Heinemann)
The Pakistani banking industry, like its counterparts elsewhere in the world, has a deadline to meet: it has to implement, and comply with, the capital adequacy framework of new Basel II Accord by December 2006 (Pakistan Bank’s Association, 2005). The Basel II therefore replaced Basel I for international banks.
Basel II aims to build on a solid foundation of prudent capital regulation, supervision, and market discipline, and to enhance further risk management and financial stability, It is said that Banks/DFIs are required to establish an adequate setup and report to SBP the name and other particulars of the coordinator for Basel II implementation as soon as possible but not later than 31st May 2005.
We will study The impact of Basel II on the credit risk management by considering two parameters i.e. NPLR and CAR. By studying these ratios, we find out that how Basel II is useful in management and reduction of risk and finally determine the role of credit risk management in increasing the profits of banks.
As per the background discussed earlier, out task is to research:
The impact of credit risk management on the profitability of commercial banks in Pakistan.
Our research will find out the importance credit risk management in the profitability of commercial banks in Pakistan and how Basel II helps in reduction of credit risk and management by using some techniques and methods that will control the amount of non-performing loans.
The purpose of the research is to explain the impact of credit risk management on profitability of commercial banks in Pakistan, that what is the role of BASEL-II in the management and reduction of credit risk by controlling the amount of non performing loans through methods, Processes and limits imposed in BASEL II.
Our research will explain the influence of credit risk management on the profitability of commercial banks. This research will be very helpful for the banking industry in Pakistan as it is directly related to the profitability of banks. It will provide them with the guidelines that how they could manage and minimize the credit as per the rules and regulations provided in Basel document.
Our research is significant and important in a way that it will determine the dependency of profitability on credit risk management and it will study Basel I and Basel II and determine their difference and whether the regulations in Basel II puts any betterment in managing the risk.
Limitations of the study
We are conducting our research on the private commercial banks of Pakistan based on the conventional banking system. It will help us on concentrating and focusing only on one sector of banking industry and determine valid and authentic results. Public sector banks, Islamic banks, investment banks, micro-finance banks are included in the research. Basel II was put into account from December 2006 that is why we have included the data from financial statements of 2007 to 2009 as we have studying the relation between profitability and credit risk management after Basel II is implemented.
The study is limited to two independent variables for measuring credit risk management that are NPLR and CAR, and one dependent variable for measuring profitability which is ROE, the reason for choosing the above mentioned variables will be discussed in the methodology
ROE – profitability indicator
ROE (Return on Equity) is defined as the ratio of Net Income to the Total equity capital.
It measures the amount of net income after taxes earned for each dollar of equity capital contributed by the bank’s stakeholders. Generally, bank stakeholders prefer ROE to be high. It is possible, however that an increase in ROE indicates increased risk. For example, ROE increases if total equity capital decreases relative to net income. A large drop in equity capital may result in a violation of minimum regulatory capital standards and an increased risk of insolvency for the bank. An increase in ROE may simply result from an increase in a bank’s leverage- an increase in its dept – to – equity ratio.
To identify potential problems, ROE can be decomposed in to two component parts as follows:
ROE = x
ROE = ROA x EM
ROA= Return on Assets ( a measure of profitability linked to the asset size of the bank)
EM= Equity multiplier (a measure of leverage)
ROA determines the net income produced per dollar of assets; EM measures the dollar value of assets funded with each dollar of equity capital (the higher this ratio, the more leverage or debt the bank is using to fund its assets)
High values for these ratios produce high ROEs, but as noted, managers should be concerned about the source of high ROEs. For example, an increase in ROE due to increase in the EM means that the bank’s leverage and therefore its solvency risk, has increased. (Cornett)
Credit risk management indicators
To understand how an FI’s equity capital protects against insolvency risk, we must define capital more precisely. The problem is that equity capital has many definitions: an economist’s definition of capital may differ from an accountant’s definition, which in turn may differ from a regulator’s definition. Specifically, the economist’s definition of an FI capital, or owner’s equity stake, is the difference between the market values of its assets and liabilities. This is also called an FI’s net worth. This is the economic meaning of capital, but regulators and accountants have found it necessary to adopt definitions that depart by a greater or lesser degree from economic net worth. The concept of an FI’s economic net worth is really a market value accounting concept. With the exception of the investment banking industry, regulatory-and accounting-defined capital and required leverage ratios are based in whole or in part on historical or book value accounting concepts.
We begin by looking at the role of economic capital or net worth as a device to protect against two of the major types of risk described i.e. credit risk and interest rate risk. We then compare this market value concept with the book value concept of capital. Because it can actually distort an FI’s true solvency position, the book value of capital concept can be misleading to managers, owners, liability holders, and regulators alike. We also examine some possible reasons FI regulators continue to rely on book value concepts when such economic value transparency problems exist. (Cornett)
Risks in banks
Financial risk in a banking organization is possibility that the outcome of an action or event could bring up adverse impacts. Such outcomes could either result in a direct loss of earnings / capital or may result in imposition of constraints on bank’s ability to meet its business objectives. Such constraints pose a risk as these could hinder a bank’s ability to conduct its ongoing business or to take benefit of opportunities to enhance its business.
Credit risk management
Capital Adequacy Ratio (CAR) is a ratio that regulators in the banking system use to watch bank’s health, specifically bank’s capital to its risk. Regulators in the banking system track a bank’s CAR to ensure that it can absorb a reasonable amount of loss
Regulators in most countries define and monitor CAR to protect depositors, thereby maintaining confidence in the banking system.
Capital adequacy ratio is the ratio which determines the capacity of a bank in terms of meeting the time liabilities and other risk such as credit risk, market risk, operational risk, and others. It is a measure of how much capital is used to support the banks’ risk assets. Bank’s capital with respect to bank’s risk is the most simple formulation, a bank’s capital is the “cushion” for potential losses, which protect the bank’s depositors or other lenders.
How is the Capital Adequacy Ratio CAR calculated?
The ratio is calculated by dividing Tier1 + Tier2 capital by the risk weighted assets.
Capital Adequacy Ratio = ————
Tier1 + Tier2 capital
Capital = —————————–
Risk Weighted Assets * 8%
Two types of capital are measured for this calculation. Tier one capital is the capital in the bank’s balance sheet that can absorb losses without a bank being required to cease trading.
Tier two capital can absorb losses in the event of a winding-up and so provides a lesser degree of protection to depositors.
Advantages of using the Capital Adequacy Ratio CAR
In early phases of Basel implementations, bank’s capital adequacy was calculated as assets times ratio. This approach did not take risk profiles of assets into account. It is obvious that a bank should keep more capital in reserves for riskier assets.
Since different types of assets have different risk profiles, CAR primarily adjusts for assets that are less risky by allowing banks to “discount” lower-risk assets. So, for example, in the most basic application, government debt is allowed a 0% “risk weighting”. This also means that government debt is subtracted from total assets for purposes of calculating the CAR.
On the other hand, investments in junior tranches of instuments collateralized with subprime mortgages are very risky, and woudl be assigned 100% risk weighting. (http://www.maxi-pedia.com/capital+adequacy+ratio+CAR, 2009)
Pakistan regulation of banks
The banks in Pakistan works under the BANKING COMPANIES ORDINANCE, 1962 (LVII of 1962) AND THE BANKING COMPANIES RULES 1963 MADE UNDER THE ORDINANCE (As amended up to 30th June, 2007) (State Bank of Pakistan, 2007)
While doing the research, we are focusing on our research task and not to go beyond our specified boundary. Thus, we’re using deductive approach. We are also referring previous researches and theories related to our field of interest because we are studying a general phenomena i.e. relationship between profitability and credit risk management in conventional banking system of Pakistan.
We are using quantitative method of study. We analyze the data with the help of regression model and the annual reports of the selected banks. The regression output makes us answer our research question.
We are conducting the research based on two factors i.e. profitability of banks and credit risk management that’s why the design of research is co-relational. Our research will explain the relationship between the two and how credit risk management affects the profitability of banks in Pakistan.
We are identifying the impact of credit risk management on profitability and For it, we have adopted the strategy of taking help from the previous records, studies and researches in this field and the statistics and data required for performing the test is obtained from the annual reports of the respective banks available on their websites.
The population for the research consists of 20 private commercial banks out of the 54 banks operating in Pakistan. All the 20 chosen banks are working under conventional banking system as we are only focusing on conventional banks and all other banks such as Islamic banks, investment banks, micro-finance banks and public sector banks are not included in our research. The reason for this is to appropriately focus on one sector. On the basis of random sampling, 15 commercial banks are selected: Habib bank Ltd, MCB Bank ltd, Allied Bank Ltd, United Bank Ltd, Standard Chartered, Bank Alfalah, Faysal Bank Ltd, Bank Al-Habib, NIB Bank ltd, My Bank, RBS, Atlas bank, Arif habib Bank, Habib Metropoliton bank, JS Bank and Askari Bank ltd. In this research we are establishing the relation between profitability and credit risk management after implementation of BASEL II in Dec’2006, therefore data is obtained from annual reports of 2007 to 2009. There are total 30 observations for each of the variable used in this research.
Data and statistics for the tests are obtained from annual reports of 2007 to 2009. We’ll consider credit risk management disclosure, financial statements and notes to financial statements within the annual reports of the sample banks.
No research instrument is required in our research because the data used to conduct tests is secondary obtained from the annual reports of the banks from 2007 to 2009.
Multiple regression analysis is used in our research i.e. the relationship of one dependent variable to multiple independent variables. The regression outputs are obtained by using SPSS
Applied regression model
Dependent variable ROE and independent variables NPLR and CAR are considered in our study and all of them are numeric type. Therefore, multiple linear regression model is applied. Dependent variable
In many of the previous researches, ROE is used for the profitability of banks, Therefore, we have also used it as the indicator of profitability in the regression analysis.. According to Foong Kee K. (2008) indicated that the efficiency of banks can be measured by using the ROE which illustrates to what extent banks use reinvested income to generate future profits.
NPLR and CAR are the indicators of credit risk management and they chosen as the independent variables because credit risk management affects the profitability of banks.
NPLR, in particular, indicates how banks manage their credit risk because it defines the proportion of NPL amount in relation to TL amount. NPL amount is provided in the Notes to financial statements under Loans section. And the total loan amount is provided in the balance sheet of the banks in their annual reports. TL amount, the denominator of the ratio, has been gathered by adding two types of loans: loans to institutions and loans to the public. Thus, calculation of the NPLR has been accomplished in following way:
NPLR = (NPL amount) ÷ (TL amount)
CAR, CAR is regulatory capital requirement (Tier 1 + Tier 2) as the percentage of Risk weighted asset. The bank has to maintain a specific percentage of CAR to manage their Credit risk according to requirement of State bank of Pakistan. The required minimum CAR, on consolidated as well as on standalone basis has been increased for banks/DFIs to 10%.
Reliability and validity
In statistics, reliability is the consistency of a set of measurements or of a measuring instrument, often used to describe a test. (http://en.wikipedia.org/wiki/Reliability_(statistics))
In science and statistics, validity has no single agreed definition but generally refers to the extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world. (http://en.wikipedia.org/wiki/Validity_(statistics))
Whether you are planning a research project or interpreting the findings of someone else’s work,
determining the impact of the results is dependent upon two concepts: validity and reliability. Essentially,
validity entails the question, “does your measurement process, assessment, or project actually measure
what you intend it to measure?”. The related topic of reliability addresses whether repeated
measurements or assessments provide a consistent result given the same initial circumstances. (http://www.natco1.org/research/files/Validity-ReliabilityResearchArticle_000.pdf)
The expression of this problem is often that you have a low overall p-value but high individual values, the effect is an over fitting of the regression. The desirable regression is the one where the explanatory variables have low correlation with each other but each high correlated to the dependant variable, this is called “low noise”. To detect this multicollinearity you are forced to study the variables correlation with each other. If there is a correlation between to variables higher than 0.8 then there is reason to believe that multicollinearity exists.
Klein (1962) test has been applied also for analyzing multicollinearity in the panel data. If
Variance inflation factor: VIF > 1/ (1-R2)
Tolerance TOL< (1-R2) Then multicollinearity is significant.
On the other hand if tolerance (TOL) is less than 0.20 or Variance inflation factor(VIF) is equal or greater than 5 then there is multicollinearity exist and two or more explanatory variables are closely correlated.
As the factor (1-R2) is 0.582 which is less than tolerance level i.e., 0.976, it means that there is no multicollinearity exists between the independent variables. And the factor 1/(1-R2) is 1.718 which is greater than the VIF provided in the table, it also represents that there is no multicollinearity exists between the independent variables
The value of mean is -2.84E-16 which is approx. equal to zero, and the value of SD is 0.926 which is approx equal to 1, which means that the population is normal. Also the histogram follows the shape of the normal curve (bell shape curve)
4.3 Scatter Plot
The above scatter plot also shows the adequacy of the fitted model as it shows that the data is scattered and it does not follow any particular pattern, so it is to be said that the fitted model has minimum chances of error.
These results are on the average basis of number of years taken. The regression is appliedâ€¦
Table shows that NPLR affects ROE negatively. NPLR Î² coefficient is -1.160 which means that one unit increase in NPLR decreases ROE by 1.160 units while CAR is held constant. The statistical significance of NPLR on ROE is 0.041 which is less than 0.05. This means that NPLR predicts effect on ROE is 99.96%. CAR also has a negative Î² coefficient -0.909. This indicates that one unit increases in CAR will decrease ROE by 0.909 units, holding NPLR constant. The statistical significance of CAR is 0.171 which is a sign of relatively low significance. It implies that CAR predicts ROE with 82.9% probability. Thus, the results of the analysis states that NPLR has negative affect on ROE; meanwhile CAR also has negative affect.
The regression model will be
ROE = 0.295 – 0.909X1-1.160X2
R2 represents the prediction level of variance in ROE by NPLR and CAR, which is 0.418. This means that 41.8% of ROE can be predicted from both NPLR and CAR. Furthermore, adjusted R2 is 32.1% and is considered as more reliable value for the model analysis.
According to the table of F-distribution, the critical value of F distribution at the 5% significant level is 3.89. In Table, the statistic value of F is 4.304 which exceed the critical value of F (3.89), which means that the value of R2 i.e. 41.8% even it is not very high, is reliable enough. Hence, the regression as whole is significant; this mean that NPLR and CAR reliably predict ROE.
The aim of the study is to determine the impact of credit risk management on profitability. It is important to note that sample size represents 75% of the total population i.e. private commercial banks. That covers the major portion of the population, giving more accurate results.
The results obtained from the regression model show that there is an affect of credit risk management on profitability on reasonable level with 41.8% possibility of NPLR and CAR in predicting the variance in ROE. So, the credit risk management strategy defines profitability level to an important extent. Especially, NPL amount appears to be adding the most weight to that than CAR.
CAR is having negative impact on ROE, but on the other hand the significance value of CAR is 0.171which is greater than the p-value i.e. 0.05, which means that the value of coefficient for CAR is zero, making the affect of CAR on ROE nil. Only NPLR is significantly affecting the value of ROE.
In the end it is to be recommended that bank should focus on maintaining and controlling amount of non performing loans to ultimately getting higher ROE, which ensures the better profitability.
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