Impact of Credit Risk Management



Credit risk management has become essential in banking industry, the importance increased after the implementation of BASEL II, as the main source of income for banks is interest income, so how effectively banks control their loans they will be in better position to give return to their depositors, and to enhance their profitability. In this research impact of credit risk management on the profitability of commercial banks in Pakistan has been tested for this purpose private commercial banks working under conventional banking system has been taken into account. And also the contribution of BASEL II has been observed in CRM as the data is collected from 3 years after the implementation of BASEL II i.e. 2007 to 2009. For the purpose of measuring CRM in banks two variables has been taken i.e. CAR and NPLR, and to measure profitability ROE has been taken. To test this relationship regression has been applied, and result shows that NPLR has significant impact on ROE but CAR doesn't have. In the conclusion it is recommended that bank should focus on maintaining and controlling amount of non performing loans to ultimately get higher ROE, which results in better profitability.


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BCBS Basel Committee on Banking Supervision

CAR Capital Adequacy Ratio

NI Net Income

NPL Non-performing Loan

NPLR Non-performing Loan Ratio

ROE Return on Equity

RWA Risk Weighted Asset

TL Total Loan

TSE Total Shareholders' Equity

OECD Organization of Economic Cooperation and Development

SBP State Bank of Pakistan

DFI Development Financial Institutions

S. No

Table of Contents







Chapter 1………………………………………………………………………….



Statement of Problem

Research Question

Research Purpose




Chapter 2………………………………………………………………………….

2. Literature Review

Previous studies

ROE-Profitability indicator

Credit risk management indicators


Risk in Banks

Credit Risk Management in Banks


2.3.1 Pakistan regulations of Banks

Chapter 3…………...……………….….…………………………………………


Research Approach

Research Design

Research Strategy


Data Collection Technique

Research Instrument

Data Analysis

Applied Regression Model

Dependent Variables

Independent Variables

Reliability and Validity

Ethical Considerations

Chapter 4………………………………………………………………………….

Data Analysis

Co linearity


Scatter Plot


Chapter 5……………………………………………….........................................

Conclusion & Recommendations








Credit Risk arises because the possibility that promised cash flows on financial claims held y Financial Institutions, such as loans and Bonds will not be paid in full (Cornett)

The Ultimate advantages of Credit Risk Management are being accepted by Financial Institutions now and Risk Managers are focusing on different Risk Management Models in looking for different Business Opportunities (Heinemann).

However in general Financial Institutions that make Loans or buy bonds with long maturities are more exposed than Financial Institutions that make loans or buy bonds with short maturities. This means for example that banks, thrifts and life insurance companies are more exposed to Credit Risk than are money market mutual funds, since Banks and life insurance companies tend to hold longer maturity assets in their Portfolios than mutual funds.


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)

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The new Basel accord or Agreement (Called Basel-II) consists of three mutually reinforcing pillars which together contribute to the Safety and soundness of the financial System


CREDIT RISK: On Balance Sheet and Off Balance Sheet (Standardized vs. Internal Ratings Based approach)

MARKET RISK: Standardized vs. Internal Ratings Based approach

OPERATIONAL RISK: Basic Indicator vs. Standardized vs Advance measurement approach)


Regulatory supervisory review so as to complement and enforce minimum Capital Requirements calculated under Pillar - 1


Requirements on rules for disclosure of Capital Structure, risk exposures, and Capital Adequacy so as to increase Financial Institutions transparency and Enhance Market/Investor Discipline.

Like in every other Country in Pakistan also the State Bank of Pakistan issued a Road Map or Guidelines for Implementation of Basel-II in Pakistan and the deadline issued by State bank for the completion was December 2006.

Problem Statement

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 [1] , 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. [2] 

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.

Research question

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 amanagement 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

literature review

Previous Studies

ROE - profitability indicator

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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.



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.


It measures the Ratio of a Bank's Book value of Primary core Capital to the book value of its Assets. The Lower this Ratio, the more highly leveraged the bank is. Primary or core Capital Bank's common Equity(book value) plus quantifying cumulative perpetual preferred stock plus minority interests in equity accounts of consolidated subsidiaries (Cornett).


Risks in banks

As Banks perform different financial services to their Clients they face many types of risk. All Banks hold some assets that are potentially subject to default or Credit Risk. As Banks expand their services, they are exposed to foreign exchange risk. Further Financial institutions and Banks tend to mismatch the maturities of their balance sheet assets and liabilities to a greater or lesser extent and thus exposed to interest rate risk. If financial institutions actively trade these assets they are further exposed to Market Risk or asset price risk. Increasingly FI's hold contingent assets and liabilities off the balance sheet which represents off balance sheet risk, Moreover some all Financial Institution and Banks are exposed to some degree of Liability or withdrawal which exposes them to Liquidity risk. Finally the Risk that the Bank may not have enough Capital reserves to offset a sudden loss incurred as a result of one or more of the risks they face creates insolvency risk for the Banks.

Credit risk management

Capital Adequacy Ratio (CAR) is used by Regulators of Banking System to assess the Banks financial Position especially the Capital to Assets Ratio as it does not falls below the required level so the bank is stable enough against the losses.

State Bank of Pakistan the Regulator of Commercial Banks in Pakistan Monitor the Capital Adequacy Ratio of Commercial Banks to Provide Protection to the Depositors.

A minimum Capital Ratio effectively constrains the leverage of Commercial Bank since highly leverage commercial Banks may be more prone to Credit, interest rate and other shocks and thus to risk of failure.

Two types of capital are measured for this calculation. Tier-I Capital is closely linked to bank's book value of equity, reflecting the contribution of a bank's owners.

Tier two is a broad array of secondary capital resources. It includes bank's loan loss reserves up to a maximum of 1.25 percent of risk adjusted Assets plus various convertible and subordinated debt instruments with maximum Caps.

Advantages of using the Capital Adequacy Ratio CAR:

In the early phase, capital adequacy ratio does not take account different risk Profiles of different class of Money market instruments, since some assets are highly risky and some debt instruments are almost risk free, such as Government bonds, where as the some instruments such as loans granted to Individual by a commercial bank can result in a default which accounts for Risk. So the advantage of Capital adequacy is as it takes into account risk profiles of all investment.


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) [3] (State Bank of Pakistan, 2007)


Research approach

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.

Research Design

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.

Research Strategy

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 Collection

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.

Research Instrument

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.

Data Analysis

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.

Independent variables

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%. [4] 

Reliability and validity


4.1 Collinearity

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.

4.4 Regression

These results are on the average basis of number of years taken. The regression is applied…

Table shows that NPLR affects ROE negatively. NPLR β 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 β 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.