i

Credit Risk Management and Profitability of Commercial Banks

in Zimbabwe (2009-2014)

May, 2015

Perry T Munzwembiri

Email: munzwembirip@gmail.com

Dissertation submitted in partial fulfilment of the requirements for a Bachelor of Commerce (Hons)

Degree in Finance at the National University of Science and Technology (NUST), Zimbabwe

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DEDICATIONS

Now unto the Lamb of God, who sits on the Throne, be glory and honour and praise forever,

and to my mother Irene, for whom my heart beats.

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ACKNOWLEDGEMENTS

I am heavily indebted to my supervisor Mr D Muyeche for his guidance and patience in

ensuring the success of this research. Further mention also goes to the whole Finance

Department for the knowledge imparted. I would also like to acknowledge my family for

their financial and moral support throughout the duration of my studies at the National

University of Science and Technology. To all my friends and colleagues who contributed in

any part to the success of this research, I could never thank you all enough.

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Table of Contents

DEDICATIONS ..................................................................................................................................... i

ACKNOWLEDGEMENTS ................................................................................................................ iv

List of Abbreviations ........................................................................................................................... ix

Glossary of Terms ................................................................................................................................. x

Abstract ................................................................................................................................................. xi

CHAPTER 1 .......................................................................................................................................... 1

1.0 Introduction ............................................................................................................................... 1

1.1 Background of the study ................................................................................................................ 1

1.2 Statement of the Problem ............................................................................................................... 4

1.3 Objectives of the Study ................................................................................................................... 5

1.3.1 Primary Objective .................................................................................................................... 5

1.3.2 Secondary Objectives ............................................................................................................... 5

1.4Research Questions .......................................................................................................................... 5

1.5 Significance of the Study ................................................................................................................ 6

1.5.1 Banking Institutions ................................................................................................................. 6

1.5.2 Regulatory Institutions ............................................................................................................ 6

1.5.3 Government .............................................................................................................................. 7

1.5.4 Public ......................................................................................................................................... 7

1.6 Scope of the Study ........................................................................................................................... 8

1.7 Hypothesis ........................................................................................................................................ 8

1.7.1 Null hypothesis ......................................................................................................................... 8

1.7.2 Alternative hypothesis ............................................................................................................. 9

1.8 Delimitations .................................................................................................................................... 9

1.9 Conclusion and Overview of Ensuing Chapter ............................................................................ 9

CHAPTER 2: LITERATURE REVIEW .......................................................................................... 11

2.0 Introduction ............................................................................................................................. 11

2.1 Theoretical Literature Review ..................................................................................................... 11

2.1.1 Risks in Banking .................................................................................................................... 11

2.1.2 Credit Risk in the Banking System ...................................................................................... 12

2.1.3 Sources of Credit Risk ........................................................................................................... 14

2.1.4 Credit Risk Indicators ........................................................................................................... 15

2.1.5 Credit Risk Measurement ..................................................................................................... 16

2.1.6 Credit Risk Management ...................................................................................................... 17

2.1.7 Alternative Credit Risk Management Strategies ................................................................ 22

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2.2 Empirical Review of Literature ................................................................................................... 25

2.2.1 Non-Performing Loans in the Credit Risk Management Framework .............................. 25

2.2.2 Economic and Financial Implications of Non-Performing Loans ..................................... 28

2.2.3 Strategies of Dealing with Non-Performing Loans ............................................................. 30

2.2.4 Relationship between Credit Risk and Profitability of Commercial Banks ..................... 31

2.2.4.1 Europe .............................................................................................................................. 32

2.2.4.2 North and Latin America ............................................................................................... 34

2.2.4.3 Middle East and North Africa ....................................................................................... 35

2.4.4.4 Australasia and Southeast Asia ..................................................................................... 36

2.2.4.5 Sub-Saharan Africa ........................................................................................................ 38

2.5.7 Conclusion and Synopsis of Ensuing Chapter ......................................................................... 45

Chapter 3: Research Methodology .................................................................................................... 46

3.0 Introduction ............................................................................................................................. 46

3.1 Research Philosophy ..................................................................................................................... 46

3.1.1 Epistemology .......................................................................................................................... 46

3.1.2 Ontology .................................................................................................................................. 47

3.2 Research Design ........................................................................................................................ 47

3.2.1 Research Strategy .................................................................................................................. 48

3.2.2 Research Method ................................................................................................................... 48

3.3 Population and Sample ................................................................................................................. 49

3.3.1 Study Population .................................................................................................................... 49

3.3.2 Sample Design ........................................................................................................................ 50

3.3.3 Sample Population ................................................................................................................. 51

3.4 Data Collection .............................................................................................................................. 51

3.4.1 Sources of Primary Data ....................................................................................................... 51

3.4.2 Sources of Secondary Data .................................................................................................... 52

3.5 Data Collection Instruments ........................................................................................................ 52

3.5.1 Questionnaire ......................................................................................................................... 53

3.5.1.2 Pilot Studies ..................................................................................................................... 53

3.6 Model Specification ....................................................................................................................... 54

3.6.2 Multivariate Regression ............................................................................................................ 55

3.6.3 R2 Test ..................................................................................................................................... 57

3.6.4 Multicollinearity ..................................................................................................................... 58

3.6.5 Heteroskedasticity .................................................................................................................. 58

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3.7.0 Justification of Variables ........................................................................................................... 58

3.7.1 Proxies for Credit Risk Management ................................................................................... 58

3.7.1.2 Non-performing loans ..................................................................................................... 58

3.7.1.3 Capital Adequacy Ratio .................................................................................................. 59

3.7.1.4 Cost per loan Asset Ratio ............................................................................................... 59

3.7.2 Proxies for Commercial Bank Profitability ......................................................................... 60

3.7.2.1 Return on Equity ............................................................................................................. 60

3.8 Control Variable ........................................................................................................................... 60

3.9 Hypotheses ..................................................................................................................................... 60

3.9.1 Null hypothesis ....................................................................................................................... 60

3.9.2 Alternative hypothesis ........................................................................................................... 60

3.10 Data Analysis Procedure ............................................................................................................ 61

3.11 Time Horizon ............................................................................................................................... 61

3.12 Chapter Summary ...................................................................................................................... 62

CHAPTER 4: DATA PRESENTATION AND ANALYSIS ........................................................... 63

4.0 Introduction ................................................................................................................................... 63

4.1.0 Questionnaire Response Rate ................................................................................................... 63

4.1.1 Work Experience ....................................................................................................................... 64

4.1.2 Causes of high non-performing loans in Zimbabwe ............................................................... 65

4.1.3 Impact of non-performing loans ............................................................................................... 66

4.1.4 The relationship between credit risk management and commercial bank profitability . 68

4.1.5 Credit Risk Evaluation and Assessment Tools .................................................................... 69

4.1.6 Effectiveness of Credit Risk Management Policies in Zimbabwe ...................................... 70

4.1.7 The most Effective Policy of reducing credit risk in Zimbabwe ........................................ 71

4.1.8 Effectiveness of a national credit reference bureau in reducing credit risk ..................... 72

4.1.9 The most effective strategy for recovering non-performing loans in Zimbabwe ................. 73

4.1.10 Effects for commercial banks complying with Basel III Committee guidelines on bank

supervision ....................................................................................................................................... 74

4.1.11 Summary of Questionnaire Findings ................................................................................. 75

4.2.0 Regression Analysis Results ...................................................................................................... 75

4.2.1 Analysis of Variance .............................................................................................................. 76

4.2.2 Descriptive Statistics .............................................................................................................. 77

4.2.3. Tests for Multicollinearity .................................................................................................... 78

4.2.3.1 Correlation Matrix of Variables ........................................................................................ 79

4.2.3.2 Collinearity Diagnostics .................................................................................................. 80

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4.2.4 Testing for (Autocorrelation Durbin-Watson Test) ............................................................ 81

4.2.5 Test for Normality of Residuals ............................................................................................ 81

4.2.6 Normal P-P plot of Regression Standardised Residual ...................................................... 82

4.2.7 Test for heteroskedasticity .................................................................................................... 83

4.2.8 Breuch-Pagan and Koenker Test for Heteroskedasticity ................................................... 83

4.2.9 Model Summary ......................................................................................................................... 85

4.2.10 Results and Discussion ............................................................................................................. 86

4.3 Reliability of the Results ............................................................................................................... 88

4.4 Conclusion ..................................................................................................................................... 88

5.0 Introduction ................................................................................................................................... 89

5.1 Model Results Revisited ............................................................................................................... 89

5.3 Conclusions .................................................................................................................................... 90

5.4 Implications and Recommendations of the Study ...................................................................... 92

5.5 Theoretical and Practical Contribution to Knowledge .............................................................. 95

5.6 Suggestions for Further Research ............................................................................................... 96

5.7 Chapter Summary ........................................................................................................................ 97

APPENDIX 1: LIST OF REFERENCES ......................................................................................... 98

APPENDIX 2: REGRESSION OUTPUT ....................................................................................... 103

APPENDIX 3:BREUSCH-PAGAN-KOENKER TEST FOR HETEROSKEDASTICITY ...... 109

APPENDIX 4: LIST OF FIGURES AND TABLES ...................................................................... 110

APPENDIX 5: QUESTIONNAIRE ................................................................................................. 112

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List of Abbreviations

BIS Bank for International Settlements

CAR Capital Adequacy Ratio

CLA Cost per Loan Asset

CRM Credit Risk Management

FDI Foreign Direct Investment

GDP Gross Domestic Product

NPL Non-performing Loan

NPLR Non-Performing Loan Ratio

LNTA Natural Logarithm of Assets

ROE Return on Equity

RBZ Reserve Bank of Zimbabwe

SPSS Statistical Package for Social Science

USD United States Dollar

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Glossary of Terms

Bank Profitability – The average returns on bank assets and equity, and net interest rate

margins.

Non-Performing Loans – Loans in which due interest and principal repayments are not

being made.

Capital Adequacy- The amount of capital banks have to hold in relation to the risk they face

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Abstract

The study sought to investigate the relationship between credit risk management and the

profitability of commercial banks in Zimbabwe during the period 2009 to 2014. Though the

majority of the literature examined found a positive relationship, most of these studies were

carried out in developed countries and the few researches on this subject carried out in sub-

Saharan Africa provided mixed results as to the existence of a relationship between the

variables. CAR, CLA and NPLs are defined as the proxies for CRM whilst ROE is taken to

be the proxy for commercial bank profitability. The research collects data from a sample of

four commercial banks out of a population of sixteen, representative of the whole banking

sector. Multivariate regression analysis is conducted to ascertain if there exists a relationship

between CRM and commercial bank profitability in Zimbabwe. Findings reveal that there

exists a positive relationship between CRM and commercial bank profitability. Between the

three proxies for CRM, NPLR was found to have the most significant effect on commercial

bank profitability. Consequently, the study recommended that, it is imperative for

commercial banks to leverage on relationship banking through building long term, mutually

beneficial relationships with their customers. This would help in the screening process of

borrowers as banks will have deeper insights into the attitudes, credit history and the capacity

and willingness of borrowers to repay loans. As Zimbabwean banks struggle to recover loans,

it is the recommendation of this study that repayment periods, interest rates and other related

fees be extended or lowered for struggling borrowers as continued hikes of rates will not

increase the chances of recovery of such loans.

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CHAPTER 1

1.0 Introduction

This chapter provides an introduction into the study`s focus areas. It starts with the

background of the study, and the statement of the problem highlighting why the research area

was chosen. The objectives of carrying out the study are also included together with the

research questions to be answered. The research`s significance to various stakeholders in the

economy is also detailed herein. Subsequently, the hypotheses of the study are stated as well

as a brief overview of the ensuing chapter.

1.1 Background of the study

The Zimbabwean banking sector has been through a tumultuous period over the last decade.

(Makoni, 2010) observes that in the late 1990s and at the turn of the 21th century, the

Zimbabwean economy was troubled by hyperinflation, resulting in declining savings from

depositors and forcing many banks to use other sources to fund their lending. With the

deepening of the crisis and imminence of the collapse of the banking sector, a temporary

withdrawal of its function as the lender of last resort was announced by the central bank in

December 2003. (Mambondiani, et al., 2013) opine that the departure of the central bank

from its previous approach of forbearance put a number of banks into liquidity crisis.

Subsequently, 13 banking institutions collapsed, all of which were indigenous, licensed after

the financial liberalisation of 1991. Since then there has been at least one banking crisis each

year up to 2009 when dollarization was introduced; caused by poor corporate governance

practices, inadequate supervision and monitoring by the central bank, liquidity constraints

and a generally tough operating environment (Mambondiani, 2012)

Post dollarization, though the economy has somewhat stabilised, the new dispensation has

brought with it a set of new challenges for the country`s fledgling banking system. Non-

Performing Loans have risen sharply since 2009 when the multi-currency regime was

adopted, to the stratospheric levels they are at today. This undoubtedly poses significant risks

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to the health and continued operations of banking institutions. Apart from being a source of

concern for financial stability, there is strong evidence that Non-Performing Loans have led

to a decrease in credit growth which is undermining current economic recovery efforts (RBZ,

2014) Banking sector profitability has been on the rise since dollarization, however,

continued growth could potentially be impeded by high incidence of NPLs. This study

therefore seeks to analytically look at the effect of credit risk management and the

profitability of selected banks in Zimbabwe, with the view of identifying sustainable

strategies to militate against the risks posed by these problems in banking institutions.

(Haneef, et al., 2012) note that banks are in the business of risk and that many of these risks

are of a traditional sort: credit risk, interest rate risk, liquidity risk. In the aftermath of the

Global Financial Crisis, it has been observed that some of these risks have caused significant

losses to the global banking system. In the Zimbabwean banking space however, credit risk

has become more prominent especially since the onset of the multi-currency era in 2009.

According to Reserve Bank of Zimbabwe (RBZ), in the face of challenging economic

conditions and the increasing cost of doing business, the debt repayment capacity of the

borrowers has remained under stress. Credit risk remains a key component in the risk profile

of the banking sector (RBZ, 2014).

Against an international average of 5%, Zimbabwe`s banking sector NPLs have risen sharply

from 1,62% in June 2009 to 18,5% as at 30 June 2014 (RBZ, 2014). After adoption of the

multiple currency system, the banking sector experienced marked improvements in the

intermediary role which resulted in improved financial support to the key productive sectors

of the economy (RBZ, 2010). With this, the profitability of banks improved significantly

coupled too with favourable economic conditions. (Rzehak, et al., 2011) postulate that bank

loans can become “non-performing” because of problems with the borrower’s financial

health, problems with the design or implementation of lender protection features, or both.

While officially reported, aggregate banking soundness indicators do not raise major flags,

they mask vulnerabilities specific to a fully dollarized banking system experiencing rapid

credit growth (Chikoko, et al., 2012). In Zimbabwe`s case, this rapid credit growth has led to

an increase in the number of loan delinquencies as borrowers failed to service the loans as

economic growth lost steam.

Resultantly, banks in Zimbabwe now sit on a high number of non-performing loans leading

to a deterioration of asset quality on the banks` balance sheets. (Chikoko, et al., 2012) further

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opine that each non-performing loan in the financial sector is viewed as an obverse mirror

image of an ailing unprofitable enterprise. As adverse economic operating conditions

continue to exist, it follows that consumers have less disposable incomes to spend on various

goods and services. In turn, companies then fail to generate sufficient demand for the

products they offer, thereby leading to suppressed performance. All these factors contribute

to the reduced capacity of companies that took out loans at the start of the multi-currency

system to repay them back, hence leading to an incidence of high non-performing loans.

Due to the increasing spate of non-performing loans, the Basel II Accord emphasized on

credit risk management practices. Compliance with the Accord means a sound approach to

tackling credit risk has been taken and this ultimately improves bank performance. Through

the effective management of credit risk exposure, banks not only support the viability and

profitability of their own business, they also contribute to systemic stability and to an

efficient allocation of capital in the economy (Psillaka & Tsolas, 2010).

To minimize these risks, it is necessary for the financial system to have; well-capitalized

banks, service to a wide range of customers, sharing of information about borrowers,

stabilization of interest rates, reduction in non-performing loans, increased bank deposits and

increased credit extended to borrowers. Loan defaults and nonperforming loans need to be

reduced (Basel, 2001). The key principles in credit risk management are; firstly,

establishment of a clear structure, allocation of responsibility and accountability, processes

have to be prioritized and disciplined, responsibilities should be clearly communicated and

accountability assigned thereto (Lindgren, 1987)

The main sources of credit risk include, limited institutional capacity, inappropriate credit

policies, volatile interest rates, poor management, inappropriate laws, low capital and

liquidity levels, directed lending, massive licensing of banks, poor loan underwriting,

reckless lending, poor credit assessment, no non-executive directors, poor loan underwriting,

laxity in credit assessment, poor lending practices, government interference and inadequate

supervision by the central bank. To minimize these risks, it is necessary for the financial

system to have; well-capitalized banks, service to a wide range of customers, sharing of

information about borrowers, stabilization of interest rates, reduction in non-performing

loans, increased bank deposits and increased credit extended to borrowers. Loan defaults and

non-performing loans need to be reduced (Basel, 2001).

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Credit risk management covers the decision-making process, before the credit decision is

made, and the follow-up of credit commitments, plus all monitoring and reporting processes

(Miller G, 1996)Modern credit risk management entails the management procedure devised

to eliminate or minimize the adverse effects of possible financial loss by identifying all the

potential sources of loss, measuring the financial consequences of a loss occurring, and using

controls to minimize actual losses or their financial consequences (Irukwu, 1998)

The effective management of credit risk is a critical component of a comprehensive approach

to risk management and essential to the long-term success of any banking organization. A

major function of commercial banks is to deal in the credit market; they perform this function

by mobilizing funds from surplus economic units and channelling the same to deficit units for

productive activities. This implies that, commercial banks grant loans to customers from the

public’s funds with the overall object of increasing profitability resulting from earnings.

Now, because profitability is a function of earnings resulting from viable loans and advances,

it follows that banks ought to effectively manage its credit risks in order to protect and

enhance profitability.

1.2 Statement of the Problem

There has been observed cases of banks that have recorded losses attributable to credit

impairments and high loan-loss provisioning. For instance, (RBZ, 2014) observes that a total

of 12 banks recorded profits for the period ended 30 June 2014 and that the losses suffered by

the other banks were among other factors, attributable to high levels of NPLs. As more and

more companies default on their loan obligations, banks are prejudiced as they are deprived

of financial resources that could otherwise have been profitably invested in other key sectors

of the economy. No senior management of today's financial institutions can perform their

functions without a vastly expanded understanding of the dimensions of risk and the various

tools to manage it (Haneef, et al., 2012). Consequently this study seeks to establish the link

between effective credit risk management, and the profitability of banks using a sample of

selected banks in Zimbabwe and assess how active credit risk management can be used as a

tool for enhancing efficient banking operations.

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1.3 Objectives of the Study

The study focused on the following primary and secondary objectives:

1.3.1 Primary Objective

The primary objective of this study is to establish if there is a relationship between effective

credit risk management practices and the improved profitability of banks.

1.3.2 Secondary Objectives

1. To determine if imprudent lending practices by banks sparked the high incidence of

Non-performing loans in Zimbabwe.

2. To analyse if rising Non-performing loans are always an indicator of weak credit risk

management practices by banks.

3. To recommend debt recovery strategies that Zimbabwean banks affected by high

Non-Performing loans can employ.

4. To identify strategies that can be used to curb the problem of rising NPLs in the

Zimbabwean context.

1.4Research Questions

In order to address the research problem, this study seeks to answer the following questions:

1. Do Zimbabwean banks have effective credit risk management policies?

2. To what extent did imprudent lending practices by Zimbabwean banks contribute to

high Non-performing loans?

3. How have macroeconomic variables contributed to the high incidence of Non-

Performing Loans in Zimbabwe?

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4. What are the effects of high Non-performing loans on the profitability of Zimbabwean

Banks?

5. What are the strategies that Zimbabwean banks can use to recover delinquent loans?

1.5 Significance of the Study

The credit risk departments of the bank try as much as possible to offer calculated risks and

come up with a proper analysis of anyone who wants to borrow. However, at the end of the

day, banks still struggle with bad debt which leads to growth of NPLs of the bank. If the risks

posed by NPLs are not tamed, a bank can go under (Rzehak, et al., 2011). This research will

be of interest to several stakeholders within and outside the banking sector.

1.5.1 Banking Institutions

Like most financial institutions, banks are involved in the business of risk. They face many

risks that if not adequately analysed and mitigated against, these risks can affect the financial

performance of the banking institutions. In Zimbabwe, as the trend of rising Non-Performing

Loans has been on the rise, banks have seen their profitability adversely affected as they have

had to see potential earnings being written down as bad debts. This study will therefore help

banking institutions manage credit risk effectively in their operations such that they meet

their set business objectives.

1.5.2 Regulatory Institutions

The research will be of particular importance to Zimbabwean regulatory authorities such as

the central bank as it will help them come up with more effective credit risk management

frameworks that can be employed by market participants. Furthermore, their supervisory

function will be enhanced as an increased understanding of the risks that banks are exposed

to in light of the dynamic financial services sector will be analysed in the study. Potential

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areas of strengthening the banking sector credit risk management regulatory framework will

also be identified by the information in this research.

1.5.3 Government

A healthy financial services sector underpinned by sound banking institutions is a vital

component for sustained economic growth and development. (Haneef, et al., 2012) posits

that it is a fact that NPLs are steadily causing lesser profitability of banking sector, as the

spread of banks is shrinking due to the lower recovery of loans and decreasing yield on

lending. It holds that as banks cut back on lending, economic growth will be dampened as the

key engines of growth will not be funded. Government will thus benefit from this by gaining

a better appreciation of the credit risks banks face, so as to be guided when making certain

market interventions like directing banks to lend to a particular industry as it has done in the

past. Hence government will gain from more knowledge of the full merits of directing bank

lending to specific economic sectors as well as the risks thereof through, a holistic

understanding of credit risk management and how it affects bank profitability. The net result

is government will have a solid platform on which to pursue its economic growth and

recovery policies.

1.5.4 Public

Over the years, Zimbabwe`s banking sector has been beset by a massive confidence deficit

the public has on the banking sector as a whole. As rising Non-Performing Loans threaten the

stability of banks by affecting profitability and growth, any bank failures as a result of this

will further erode public confidence. Hence the public will benefit from this study through

acquiring an enhanced understanding of the stability, credit standing, riskiness and overall

health of banking institutions which they are clients of. This knowledge will equip the public

to make informed decisions on which banks to use and which ones to avoid basing on their

levels of NPLs and profitability.

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1.6 Scope of the Study

Conceptual Scope

This study focused particularly on the conceptual aspects of credit risk management

framework of banking institutions in Zimbabwe, the incidence of rising NPLs, loan

impairments and write-offs as well as profitability of banking institutions affected by high

NPLs.

Geographical Scope

The research will focus on banking institutions domiciled in Zimbabwe and that are

servicing the Zimbabwean market.

Temporal Scope

The period February 1 2009 to 31 December 2014 is used to analyse the effect of credit risk

management on the profitability of selected banks in Zimbabwe. This post dollarization

period witnessed stabilisation of economic fundamentals in the country and provides a

suitable platform to analyse data.

Parametric Scope

Key variables of loan impairments, banking sector profitability, loan loss provisioning will be

focused on by this study.

1.7 Hypothesis

In carrying out the research, the following hypotheses were assumed:

1.7.1 Null hypothesis: There is no correlation between Capital Adequacy Ratio (CAR), Cost

per Loan Asset (CLA), Non-Performing Loans Ratio (NPLR) and (Return on Equity) ROE of

commercial banks in Zimbabwe.

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1.7.2 Alternative hypothesis: There is a correlation between CAR, CLA, NPLR and ROE of

commercial banks in Zimbabwe.

1.8 Delimitations

As previously discussed, Zimbabwe`s banking sector has faced turbulent headwinds since the

turn of the new millennium. There are therefore key legacy issues that stem from the banking

crises of the pre-dollarisation era. This research does not take into account any of the

potential impacts this period that may have spilled over into the multi-currency dispensation.

As a result, the study focuses on the period 2009-2014.

There also exist time constraints as well as financial considerations that inhibit the

exploration of some of the critical issues at hand. Consequently, this research relied on the

available literature and sources and was thus designed with these considerations in mind.

1.9 Conclusion and Overview of Ensuing Chapter

This chapter provided a brief background into the study of credit risk management and the

profitability of banking institutions. To put the research into context, a historical discussion

of the critical issues that have characterised Zimbabwe`s banking landscape since the turn of

the new millennium have been discussed. This paved the way for an appreciation of the

present conditions of the banking sector in the country. Through the statement of the

problem, the narrative focused on the key issue of rising Non-Performing loans in the

banking system. Objectives of the study were set thereafter and brief sections on the

Methodology, Significance of the study and Delimitations were subsequently provided.

Chapter 2, presented hereafter, will provide a theoretical premise for the study. This chapter

will interrogate the issues of credit risk management and the profitability of banks and

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explore the theoretical issues that underpin the two concepts. Particular attention will focus

on how these two interact and influence one another.

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CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

In this chapter, the researcher presents the theoretical foundation for the study by providing

relevant literature pertaining to credit risk management and profitability of commercial

banks. Theories that led to the development of the hypotheses will also be examined, with the

different concepts and discussion points arising then being used to frame the final research

model.

2.1 Theoretical Literature Review

2.1.1 Risks in Banking

(Bessis, 2002) posits that risk management in banking designates the entire set of risk

management processes and models allowing banks to implement risk-based policies and

practices. They cover all techniques and management tools required for measuring,

monitoring and controlling risks. The spectrum of models and processes extends to all risks:

credit risk, market risk, interest rate risk, liquidity risk and operational risk, to mention only

major areas.

Risk management is recognized in today’s business world as an integral part of good

management practice. In its broadest sense, it entails the systematic application of

management policies, procedures and practices to the tasks of identifying, analysing,

assessing, treating and monitoring risk (Haneef, et al., 2012).

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(Koch & Macdonald, 2014) posit that commercial banks’ risks can be identified as six types:

credit risk, liquidity risk, market risk, operational risk, reputation risk and legal risk. Each of

these risks might harmfully influence the financial institution’s profitability, market value,

liabilities and shareholder’s equity.

Given all these bank risks, (Boudriga, et al., 2009) identify credit risk as singularly the

greatest risk on bank’s performance. It is the risk that counterparties in loan transactions and

derivatives transactions might default, which means counterparties fail to repay the principal

and interest on a timely basis (Koch & Macdonald, 2014). This argument has merit in

Zimbabwe`s case as a build-up of toxic loans since dollarisation in 2009 have necessitated the

formation of Zimbabwe Asset Management Company (ZAMCO) to clean up commercial

bank balance sheets by buying the NPLs of banks. This signifies that the regulatory

authorities in Zimbabwe also view credit risk as a potential hindrance to the effective

operations of banks in Zimbabwe.

This view is further supported by (Agarwal, 2015) who asserts that although credit risk has

always been of primary concern to these institutions, its importance became paramount

during the recent financial crisis. The crisis has exposed the shortcomings of existing risk

management systems, and several firms saw significant losses resulting from failure of their

counterparties to deliver on contracts. (Ernst & Young, 2010) indicate that senior executives

in financial firms are paying more attention to their credit risk than any other type of risk.

While 67% of the respondents in Ernst & Young`s survey said credit risk was their top risk

priority, some of the other risk types they are likely to pay a lot of attention to are:

operational risk (44%), liquidity risk (38%), and market risk (33%). This shows that though

commercial banking is laden with numerous other risks, over the years, with the rapid and

dynamic development of the financial services sector, as well as the occurrence of the Global

Financial Crisis, credit risk has emerged as the crucial and foremost risk that financial

managers have had to contend with.

2.1.2 Credit Risk in the Banking System

Credit risk is the potential loss in the event of default of a borrower, or in the event of

deterioration in credit standing (Bessis, 2002). He further asserts that credit risk is generally

underpinned by three major risk components: ‘Exposure at Default’ (EAD), default

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probabilities (DP) and loss given default (LGD). Exposure characterizes the amount at risk,

default and migration probabilities characterize the chances of defaulting and migrating

across risk classes, recoveries reduce the loss under default. The ultimate issue in credit risk

management thus pertains to the following: what are the chances of losses and the

magnitudes of losses?

(Nawaz, et al., 2012) define credit risk as the current and prospective risk to earnings or

capital arising from an obligor’s failure to meet the terms of any contract with the bank or

otherwise to perform as agreed. Credit risk is found in all activities in which success depends

on the counterparty, issuers, or borrower performance. It arises any time bank funds are

extended, committed, invested, or otherwise exposed through actual or implied contractual

agreements, whether reflected on or off the balance sheet. Thus risk is determined by factors

extraneous to the bank such as general unemployment levels, changing socio-economic

conditions, debtors‟ attitudes and political issues.

Furthermore, the Basel Committee on Banking Supervision- (Basel, 1999) define credit risk

as the potential that a bank borrower or counterparty will fail to meet its obligations in

accordance with agreed terms. (Gastineau, 1992) concurs with this definition and views credit

risk as the possibility of losing the outstanding loan partially or totally, due to credit events

(default risk). Credit events usually include events such as bankruptcy, failure to pay a due

obligation, repudiation/moratorium or credit rating change and restructure.

(Choudhry, 2011) contends that credit risk can also be a risk of loss on credit derivative

market. This nature of credit risk is however not presently applicable to Zimbabwe as there is

no active derivative market where financial derivatives are traded. It can also be credit

migration such as a downgrade in credit rating. Or when the bank invests in debt to high-

quality borrower whose risk profile has deteriorated (Choudhry, 2011). According to

(Conford, 2000) credit riskis the possibility that the actual return on a loan portfolio will

deviate from the expected return. That is loan delinquency and default by borrowers. While

loan delinquencies indicate delay in payment, default denotes non-payment, and the former if

unchecked, leads to the latter (Brownbridge, 1998).

Among other risks faced by banks, credit risk plays an important role on banks’ profitability

since a large chunk of banks’ revenue accrues from loans from which interest is derived.

However, interest rate risk is directly linked to credit risk implying that high or increment in

interest rate increases the chances of loan default (Kolapo, et al., 2012). This view is

14

supported by (Drehman, et al., 2008) who postulate that credit risk and interest rate risk are

intrinsically related to each other and not separable. Increasing amount of non-performing

loans in the credit portfolio is thus inimical to banks in achieving their objectives. Prejudiced

of both the potential interest earnings as well as the principal loaned out, the income earning

ability is greatly impinged upon, thus implying a greater need to proactively manage credit

risk.

2.1.3 Sources of Credit Risk

For most banks, loans are the largest and most obvious source of credit risk; however, other

sources of credit risk exist throughout the activities of a bank, including in the banking book

and in the trading book, and both on and off the balance sheet. Banks are increasingly facing

credit risk (or counterparty risk) in various financial instruments other than loans, including

acceptances, inter-bank transactions, trade financing, foreign exchange transactions, financial

futures, swaps, bonds, equities, options, and in the extension of commitments and guarantees,

and the settlement of transactions, and credit risk transfer (Hui-Cui, 2008).

(Kitua, 1996) concurs with (Hui-Cui, 2008) in her observation that loans constitute a large

portion of credit risk as they normally account for 10-15 times the equity of a bank. (Mavhiki,

et al., 2012) further add that the banking business is likely to face difficulties when there is a

slight deterioration in the quality of loans. (Brownbridge, 1998) opines that problems such as

these are more acute in developing countries where the problem often begins right at the loan

application stage and increases further at the loan approval, monitoring and controlling

stages, especially when credit risk management guidelines in terms of policy and procedures

for credit processing do not exist or are weak and incomplete.

(BIS, 2015) suggests that credit risk results from lending across all sectors through lending

products such as traditional bank lending and direct lending by non-banks. Banking

supervisors noted that, as the most traditional product in the banking sector, loans to the

corporate sector are one of the largest sources of credit risk in particular. The implication

thereof is that loans to medium sector companies are vital in the management of credit risk.

15

(Kithinji, 2010) on the other hand, identifies the major sources of credit risk as being limited

institutional capacity, inappropriate credit policies, volatile interest rates, poor management,

inappropriate laws, low capital and liquidity levels, direct lending, massive licensing of

banks, poor loan underwriting, laxity in credit assessment, poor lending practices,

government interference as well as inadequate supervision and monitoring by a central banks.

(Kolapo, et al., 2012) further suggest that an increase in bank credit risk gradually leads to

liquidity and solvency problems. Credit risk may occur when banks lend to borrowers it does

not have adequate knowledge of.

(Sandstorm, 2009) on the other hand argues that, credit risk in the banking industry is mostly

caused by adverse selection and moral hazards due to information asymmetry. The credit risk

situation of a bank can be exacerbated by inadequate institutional capacity, inefficient credit

guidelines, and inefficient board of directors, low capital adequacy ratios and liquidity,

compulsory quota-lending as a result of government interference and lack of proper

supervision by the central bank.

2.1.4 Credit Risk Indicators

(Basel, 1999) links the minimum regulatory capital to the underlying risk exposure of banks,

which refers to the greater risk bank exposed relates to the higher amount of capital bank

needs. This regulation indicates the importance of capital management in risk management

and the compliance with the regulatory requirement can be expressed as risk management

indicators. On the other hand; (Brewer III & Jackson III, 2006) regard non-performing loan

ratio (NPLR) as a significant economic indicator. It implies that lower NPLR is related with

the lower risk and deposit rate. There might be a positive relationship between deposit rate

and NPLR based on the possibility that bank’s deposit base will be increased by the high

deposit rate for funding high risk loans. The increasing high-risk loans might enhance the

probability of higher NPLR. So that the allocation of banks risk management deeply relies on

the diversification of credit risk to decrease the NPL amount.

16

(Boudriga, et al., 2009) note that CAR seems to reduce the level of problem loans which

means higher CAR leads to less credit exposures. Conversely, (Rime, 2001) suggests a

positive relationship between bank risks and capital ratio.

2.1.5 Credit Risk Measurement

The measurement of credit risk is of paramount importance in credit risk management.

(Davies & Kearns, 1992) emphasize that institutions should have procedures for measuring

their overall exposure to credit risk as well as exposure to connected parties, products,

customers and economic sectors. (KCB, 2005) also suggest that banks need to establish a

system that enables them to monitor quality of the credit portfolio on a day to day basis and

take corrective steps as and when deterioration occurs.

(Nawaz, et al., 2012) observe that to measure credit risk, there are a number of ratios

employed by researchers. The ratio of Loan Loss Reserves to Gross Loans (LOSRES) is a

measure of bank’s asset quality that indicates how much of the total portfolio has been

provided for but not charged off. Indicator shows that the higher the ratio the poorer the

quality and therefore the higher the risk of the loan portfolio will be. In addition, Loan loss

provisioning as a share of net interest income (LOSRENI) is another measure of credit

quality, which indicates high credit quality by showing low figures. In the studies of cross

countries analysis, it also could reflect the difference in provisioning regulations (Demirgüç-

Kunt, 2012)

(Coyle, 2000)notes that the measurement of credit riskincludes estimating the credit scoring

and the use of Altman Model for estimating credit default risks as follows. Credit scoring is a

system of categorizing creditworthiness by awarding points according to certain key features

of business to produce a total credit score called Z-Score, which is derived from a corporate

future prediction model using key financial ratios of a bank financial statement

(Hosna & Bakaeva, 2009) postulate that usually, banks can project the average level of losses

they can reasonably expect to experience. These losses are referred to as:

i. Expected losses: Perceived as cost of business undertaking by financial institutions;

17

ii. Unexpected losses: Losses above expected level when banks anticipate their

occurrence though the timing and severity cannot be known beforehand. A few

portions of unexpected losses might be absorbed by the interest rate charged on credit

exposure although markets will not support adequate prices to cover all unexpected

losses.

iii. Loss Given Default: The amount of funds that the bank can lose when the borrower

defaults on a loan.

(Hosna & Bakaeva, 2009) further argue that, capital is therefore needed to cover the risks of

such losses. Banks have an incentive to minimize capital they hold since reducing capital

frees up economic resources that can be directed to profitable investment. In contrast, the less

capital a bank holds, the greater is the likelihood that it will not be able meets its own debt

obligations, i.e. that losses in a given year will not be covered by profit plus available capital,

and that the bank will become insolvent. As such, it is imperative that banks strike a balance

between the risks and rewards of holding capital.

2.1.6 Credit Risk Management

Due to the increasing spate of non-performing loans, the Basel II Accord emphasized on

credit risk management practices. Compliance with the accord means a sound approach to

tackling credit risk has been taken and this ultimately improves bank performance. Through

the effective management of credit risk exposure, banks not only support the viability and

profitability of their own business, they also contribute to systemic stability and to an

efficient allocation of capital in the economy (Psillaki, et al., 2010)

The credit risk management strategies are measures employed by banks to avoid or minimize

the adverse effect of credit risk (Kolapo, et al., 2012) . A sound credit risk management

framework is crucial for banks so as to enhance profitability and guarantee survival.

According to (Lindgren, 1987) the key principles in credit risk management process are

sequenced as follows; establishment of a clear structure, allocation of responsibility,

processes have to be prioritized and disciplined, responsibilities should be clearly

communicated and accountability assigned.

18

(Rani, 2009) contends that credit risk management is fraught with rewards and risks that need

to be balanced through judicious and prudent risk management, failure of which may lead to

litigation, financial loss or damage of the bank`s reputation. (Marsh, 2008) observes that in

most developing and emerging countries, lending activities by banks have been controversial

and a difficult matter. More often than not this is the case in these countries since

businessperson the one hand demand more credit from banks while on the other hand,

commercial banks have suffered large losses on bad loans. This then makes the case for

active management of the lending activities of banks, since this is the major source of credit

risks in banks according as (Hui-Cui, 2008) suggests.

(Hui-Cui, 2008) sees the goal of credit risk management as maximizing 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. Furthermore she asserts that, 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 of any banking organization.

Establishing a clear process for approving new credit and extending existing credit as well as

monitoring credits granted to borrowers are considered important when managing credit risk

(Hefferman, 1996); (Mwisho A, 2001). Instruments such as covenants, collateral, credit

rationing, loan securitization and syndication have been used by banks in developing

countries in controlling credit losses (Boudriga, et al., 2009). It has also been identified that

high-quality credit risk management staff are critical to ensuring that the depth of knowledge

and judgment needed is always available, thus ensuring the successful management of credit

risk in banks (Koford & Tschoegl, 1997); (Wyman, 1999).

Credit risk management is very vital to measuring and optimizing the profitability of banks.

The long term success of any banking institution depends on an effective system that ensures

repayments of loans by borrowers which is critical in dealing with asymmetric information

problems, thus, reducing the level of loan losses, (Basel, 1999). Effective credit risk

management system involves establishing a suitable credit risk environment; operating under

a sound credit granting process, maintaining an appropriate credit administration that

involves monitoring, processing as well as enough controls over credit risk (Greuning &

Bratanovic, 2003).

19

(Joetta, 2007) contends that because of the dire consequences of credit risk, it is important

that credit managers perform a comprehensive evaluation of credit risk covering the credit

portfolio management, lending function and operations, credit risk management policies,

non-performing loans portfolio, asset classification and loan provisioning policy. This review

must be done at least annually. Further, when the risk has been identified, investment

decisions can be made and the risk vis a vis return balance considered from a better position.

Credit risk can be reduced by monitoring the behaviour of clients who intend to apply for

credit in the business. An important aspect in credit risk management is credit assessment.

Due to the dire effects of credit risk, whereby if not well managed can lead to bank failure, it

is important for a bank to have capacity to assess, administer, supervise, control, enforce and

recover loans, advances, guarantees and other credit instruments.

(Rani, 2009) identifies the following determinants of efficiency of credit risk management:

enhancing capital adequacy; strengthening assets quality; improving management soundness;

increasing earnings, having adequate liquidity and reducing sensitivity to market risk.

(Gestel & Baesens, 2009) argue that the most important method in managing credit risk starts

with appropriate selection of the counterparts and products. Furthermore, their theory holds

that a good risk assessment model and qualified credit officers are key requirements for

selection strategy. For counterparties with higher default risk, banks may need more collateral

to reduce risk. The pricing of product should be in line with the estimated risk. Secondly,

limitation rule of credit risk management restricts the exposure of bank to a given counterpart

in that it avoids the situation that one loss or limited number of losses endangers the bank’s

solvency. Bank’s determinants on how much credit a counterpart with a given risk profile can

take need to be limited.

Thirdly, the allocation process of banks provides a good diversification of the risks across

different borrowers of different types, industry, and geographies As a result, diversification

strategy spreads the credit risk thus avoids a concentration on credit risk problems. (Bank of

Malawi, 2007)identifies credit concentration as an aspect that increases the risk exposure of

the banks. The Bank refers to the Asian financial crisis as a typical case of how close linkages

among emerging markets under stress situations and correlation between market and credit

risks as well as between those risks and liquidity risk can lead to widespread losses affecting

profitability. (Mavhiki, et al., 2012) proffer that in Zimbabwe, some banks have taken an

20

aggressive approach with the bulk of the loans being salary based, with other banks having

concentrated their loan portfolios in other sectors such as agriculture.

(Ho , Yousoff, 2009)further contend that spreading investments into a broader range of

financial services or loans, business persons, mortgages among others reduce both the upside

and downside potential and allows for more consistent performance under a wide range of

economic conditions, and this can be performed across products, industries and countries.

That diversification reduces credit risks rapidly as the total risk of loan provisions fall as a

variety of loan products and borrowings from different industries increases, assuming the

correlation between markets is not perfect.

(Agarwal, 2015) identifies four strategies that are vital in the efficient management of credit

risk.

i. Credit and Portfolio Models

Most financial institutions have their own internal credit models that they use for risk

management. Credit portfolio models differentiate credit risk based on different parameters

such as industry, geography, credit grade, etc. A numerical simulation is run to generate a

large number of scenarios, simulating various states of the economy and the resulting impact

of each on the credit portfolio value. With this analysis, portfolio managers can make

decisions on what should be the ideal composition of the portfolio, based on their risk

appetite and performance targets.

ii. Internal Credit Ratings

Credit ratings provide an estimate of the creditworthiness of an entity, and are generally a

reflection on an entity’s ability to repay debt. In addition to the standard ratings provided by

credit-rating agencies, firms often also make use of internal ratings that they calculate

themselves. Each firm might have its own unique methodology for calculating internal

ratings. A firm could have internal ratings for various entities and complex products which

may not have an external rating.

iii. Exposure Limit

In order to keep their exposure to any single body in check, firms monitor their exposure to a

number of entities and categories such as counterparties, bond issuers, issuer type, product

type, etc. Firms create certain predefined limits for each entity, and once these limits are

21

reached, any further trades with the entity are blocked until the exposure comes down. This is

done to ensure risk diversification so that the firm is not overexposed to any one entity, and in

the case of a negative market event, has only limited losses. In some markets these limits are

regulatory requirements for certain types of financial firms and their exposures must be

reported.

iv. Stress testing

Stress testing is done to overcome some of the drawbacks of risk models that are overly

dependent on historical data, and to test the specific risk parameters which define the model.

Based on the limited inputs, these models can sometimes cause an underestimation of risk.

Stress testing typically allows testing based on a combination of different scenarios including

shocks and conceived scenarios, and is often applied to firm-wide portfolios to capture the

complete risk along different lines of business.

Banks can also buy credit protection in forms of guarantees through credit derivative

products and credit securitisation (Gestel & Baesens, 2009). By the protection, the credit

quality of guaranteed assets has been enhanced. These techniques are translated in the daily

organization by written procedures and policies which determine how counterparts are

selected, risk profile loans are granted and above which level an expert evaluation is required

(Basel, 1999) recommends the following principles for banks to effectively manage credit

risks:

i. Board of directors should have responsibility for approving and reviewing the credit

risk strategy.

ii. Senior management should have responsibility for implementing the credit risk

strategy, policies and procedures for identifying, measuring, monitoring, and

controlling credit risk.

iii. Banks should identify and manage credit risk inherent in all products and activities.

iv. Banks must operate under sound, well-defined credit granting criteria.

22

v. Banks should establish overall credit limits at the level of individual borrowers and

should have a clearly-established process in place for approving new credits as well as

the extension of existing credit.

vi. Banks should have in place a system for the on-going administration of various credit

risk-bearing portfolios and must have in place a system for monitoring the condition

of individual credits including determining the adequacy of provisions and reserves.

vii. Banks must have information systems and analytical techniques that enable

management to measure credit risk and banks should develop internal risk rating

systems in managing credit risk.

viii. Banks should consider potential future changes in economic conditions when

assessing individual and portfolio credits.

2.1.7 Alternative Credit Risk Management Strategies

The strategies for hedging against credit risk features in various literatures on credit risk

management include, but not limited to these:

i. Credit Derivatives: This provides banks with an approach which does not require

them to adjust their loan portfolio. Credit derivatives provide banks with a new source

of fee income and offer banks the opportunity to reduce their regulatory capital (Shao

& Yeager, 2007). The most common type of credit derivative is credit default swap

whereby a seller agrees to shift the credit risk of a loan to the protection buyer.

(Partnoy & Skeel, 2006) in the Financial Times of 17 July, 2006 said that “credit

derivatives encourage banks to lend more than they would, at lower rates, to riskier

borrowers”. Recent innovations in credit derivatives markets have improved lenders’

abilities to transfer credit risk to other institutions while maintaining relationship with

borrowers (Marsh, 2008).

ii. Credit Securitization: It is the transfer of credit risk to a factor or insurance firm and

this relieves the bank from monitoring the borrower and fear of the hazardous effect

of classified assets (Kolapo, et al., 2012). This approach insures the lending activity of

23

banks. The growing popularity of credit risk securitization can be put down to the fact

that banks typically use the instrument of securitization to diversify concentrated

credit risk exposures and to explore an alternative source of funding by realizing

regulatory arbitrage and liquidity improvements when selling securitization

transactions (Michalak & Uhde, 2009). A cash collateralized loan obligation is a form

of securitization in which assets (bank loans) are removed from a bank’s balance

sheet and packaged (tranched) into marketable securities that are sold on to investors

via a special purpose vehicle (SPV) (Marsh, 2008)

iii. Compliance to Basel Accord: The Basel Accord is a set of international principles

and regulations guiding the operations of banks to ensure soundness and stability

initially introduced in 1988. Compliance with the Accord means being able to

identify, generate, track and report on risk-related data in an integrated manner, with

full auditability and transparency and creates the opportunity to improve the risk

management processes of banks. The New Basel Capital Accord places explicitly the

onus on banks to adopt sound internal credit risk management practices to assess their

capital adequacy requirements (Chen & Pan, 2012).

iv. Adoption of a sound internal lending policy: The lending policy guides banks in

disbursing loans to customers. Strict adherence to the lending policy is by far the

cheapest and easiest method of credit risk management. The lending policy should be

in line with the overall bank strategy and the factors considered in designing a lending

policy should include; the existing credit policy, industry norms, general economic

conditions of the country and the prevailing economic climate (Kithinji, 2010).

v. Credit Reference Bureau: This is an institution which compiles information and

sells this information to banks as regards the lending profile of a borrower. The

bureau awards credit score called statistical odd to the borrower which makes it easy

for banks to make instantaneous lending decision. An Example of a credit bureau is

the Credit Risk Management System (CRMS) of the Central Bank of Nigeria (CBN)

(Kolapo, et al., 2012).

The growing body of literatures in finance and economics seem to highlight that failure in

credit risk management is the main source of banking sector crises which possibly leads to

economic failure experienced in the past, including the 2008 Global Financial Crisis (Fofack,

2005).

24

Though there has been a proliferation of academic studies on the relationship between credit

risk management and profitability of commercial banks, most of these studies have not

entirely been focused on developing nations, particularly those in sub-Saharan Africa, but

have tended to focus more on developed nations. Where research has been done in Africa, it

has tended to focus on Western African countries such as the studies by (Afriyie & Akotey,

2010); (Boahone, et al., 2012) in Ghana, (Kargi, 2011); (Kithinji, 2010) in Kenya and

(Kurawa & Sanusi, 2014); (Kolapo, et al., 2012) in Nigeria. (Mavhiki, et al., 2012) conducted

an analysis of the challenges faced by commercial banks in managing credit in Zimbabwe,

but their study is too broad and does not comprehensively look at the relationship of credit

risk management and profitability of commercial banks. Furthermore, there exist unique

market, regulatory as well as operational differences between Zimbabwe and other African

and European regions where such studies has been done, which make it inherently difficult to

infer results of those studies and apply them to Zimbabwe. This therefore leaves a gap to

adequately analyse the relationship between credit risk management and the profitability of

commercial banks in the country, which this study intends to fill.

In identifying the fit of credit risk management in the overall banking framework, (Marrison,

2002) postulates that the main activity of bank management is not deposit mobilisation and

giving out loans. Rather, he views effective credit management as crucial in reducing the risk

of customer default, adding that the competitive advantage of a bank is dependent on its

capability to handle credit valuably. Bad loans cause bank failures as the failure of a bank is

seen mainly as the result of mismanagement because of bad lending practices made with

wrong appraisals of credit status or the repayment of non-performing loans and excessive

focus on giving loans to certain customers.

(Spollen, 1997) assents with (Marrison, 2002) on his view that bad loans are often a result of

poor credit risk management and are mostly due to poor lending practices by bank

management. (Spollen, 1997) cites irregular meetings of loans committees, false loans, and

high sums of unrecorded deposits as some of the factors that contribute to high Non

Performing Loans and subsequently, bank failure. According to (Spollen, 1997), some

lending decisions involving high amounts of money are made by an individual worker

because of the status of the recipients of the loan. This is something that has been observed in

some Zimbabwean banking institutions in the cases of the now defunct Renaissance

Merchant Bank as well as Royal Bank where the same institutions struggled to recover some

of these loans (Gono, 2006).

25

Essentially, a strong credit risk management avoids significant drawbacks like credit

concentrations, lack of credit discipline, aggressive underwriting to high-risk counterparts

and products at inadequate prices (Gestel & Baesens, 2009). An effective credit risk

management is verified by internal risk control and audit which monitor credit discipline,

loan policies, approval policies, facility risk exposure and portfolio level risk (Gestel &

Baesens, 2009). (Basel, 1999) supports this view in their recommended principles of sound

credit risk management practices. These include:

i. Establishing an appropriate credit risk environment.

ii. Operating under a sound credit granting process;

iii. maintaining an appropriate credit administration, measurement and monitoring

process;

iv. ensuring adequate controls over credit risk

(BIS, 1999) 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. (Basel, 1999) assents to this view by

contending that 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.

2.2 Empirical Review of Literature

2.2.1 Non-Performing Loans in the Credit Risk Management Framework

According to the IMF, a loan is non-performing when payment of interest and principal are

past due by 90 days or more, or at least 90 days of interest payment have been capitalized,

refinanced or delayed by agreement, or payment are less than 90 days overdue, but are other

26

good reasons to doubt that payment will be made in full. Non-performing loans can lead to

efficiency problem for banking sector. The phenomena that banks are reluctant to take new

risks and commit new loans are described as the "credit crunch" problem.

(BIS, 1999) posit that credit crunch results in excess demand for credit and hence credit

rationing, where loans are allocated via non-price mechanism. Eventually, it imposes

additional pressure on the performance of the monetary policy. Furthermore, higher level of

NPLs reduces banks' aspiration to increase lending. However, countries with different

situations have shown different locations of the thresholds.

Bank loans can become non-performing because of problems with the borrower’s financial

health, problems with the design or implementation of lender protection features, or both. In

ascertaining how to deal with a problem loan, it is important to distinguish between a

borrower’s ability to pay and willingness to pay.

(EBCI, 2012) argues that the boom-bust cycle has left a legacy of high NPLs in various

countries in Central, Eastern and South-eastern Europe (CESEE). Very high credit growth

during 2003-08 gave rise to an unsustainable boom that ended abruptly with the global

financial crisis of 2008/09. The deep recession that followed brought many of the

accumulated underlying problems to the fore; including poor quality of some loans on banks’

books. NPLs now average some 11 percent in the region. The subdued economic outlook for

the region means that delinquent borrowers will continue to struggle and that collateral values

will remain at depressed levels for some time.

(Nawaz, et al., 2012) argue that the deregulation of the financial system in Nigeria embarked

upon from 1986 allowed the influx of banks into the banking industry. As a result of

alternative interest rate on deposits and loans, credits were given out indiscrimately without

proper credit appraisal (Philip, 1994). The resultant effects were that many of these loans turn

out to be bad. It is therefore not surprising to find banks to have non-performing loans that

exceed 50 per cent of the bank’s loan portfolio. The increased number of banks over-

stretched their existing human resources capacity which resulted into many problems such as

poor credit appraisal system, financial crimes, accumulation of poor asset quality among

others (Sanusi, 2002). The consequence was increased in the number of distressed banks.

(Chen & Pan, 2012) have identified Japan’s highlevel of NPLs as an outcome of prolonged

economic stagnation and deflation in theeconomy since the bursting of the “bubble” in the

27

early 1990s. In addition, highlight cross-shareholdings, stock market volatility, virtual blanket

guarantee of bank debts and the system of “relationship banking” as factors responsible for

the prolonged fragility of the Japanese banking sector as a result of NPLs.

(Brownbridge, 1998) observes that the single biggest contributor to the bad loans of many of

the failed banks has been insider lending. He further notes that the second major factor

contributing to bank failure is the high interest rates charged to borrowers operating in the

high-risk sectors of an economy. The most profound impact of these high non-performing

loans in banks` portfolios is the reduction in bank profitability especially when it comes to

disposals he further opines. (Philip, 1994) gives credence to this school of thought as he

proffers that the deregulation of the financial system in Nigeria embarked upon from 1986,

allowed an influx of banks into the banking industry. As a result of alternative interest rate on

deposits and loans, credits were given out indiscrimately without proper credit appraisal

(Adhikary, 2010) contends that, in the Indian subcontinent (India, Pakistan, Sri Lanka,

Bangladesh and Nepal), however, the causes of nonperforming loans are usually attributed to

the lack of effective monitoring and supervision on the part of banks (as required by the

BASEL principles of bank monitoring and supervisions), lack of effective lenders’ recourse,

weaknesses of legal infrastructure, and lack of effective debt recovery strategies. Among the

countries in the Indian sub-continent, the rate of NPLs as a percentage of total loans

disbursed in 2005 is seen to be minimal in India (5.2%), followed by Sri Lanka (9.6%).

Bangladesh, however, still records a staggering rate of 13.56%.

In Bangladesh, (Nawaz, et al., 2012) also further postulate that argue that the expansion of

credit policy during the early stage of liberation, which was directed to disbursement of credit

on relatively easier terms, did actually expand credit in the economy on nominal terms.

However, it also generated a large number of wilful defaulters in the background who, later

on, diminished the financial health of banks through the “sick industry syndrome.

The economic and financial implications of NPLs in a bank-centered financial economy can

be best explained by the following diagram overleaf:

28

2.2.2 Economic and Financial Implications of Non-Performing Loans

Figure 2:1

Source: (Adhikary, 2010) Bangladesh Institute Bank Management

This clearly shows the negative effects of economic shocks that NPLs can potentially have.

Commercial banks stand to lose out on revenues as they have to set out significantly high

provisions for loan losses, which in turn erode the bank`s capital position. Resultantly, this

curtails bank lending to productive sector of the economy and the whole economy then

suffers as a result of this lack of investment spending thereby triggering a financial crisis I the

country.

Zimbabwe has witnessed substantial increases in the levels of non-performing loans which

seems to suggest poor credit risk management policies over the years according to theoretical

literature. This bodes negatively for the local banking sector. Late last year, the NPLs in

Zimbabwe declined marginally and this can be attributed to the closure of both Interfin and

Allied Banks. This rise in non-performing loans in Zimbabwe can be illustrated graphically

as follows:

29

Figure 2:2 Trends of NPLs since dollarisation

Source: Reserve Bank of Zimbabwe Monetary Policy Statement, 2015

However, the findings obtained by (Adhikary, 2010) can be queried in the Zimbabwean

context. Though the levels of NPLs have actually increased since dollarisation in 2009,

prompting the creation of Zimbabwe Asset Management Company (ZAMCO) to buy out the

bad loans off the balance sheets of commercial banks, total advances have actually increased.

(RBZ, 2015) observes that total loans and advances increased from $3.7 billion as at 31

December 2013 to $4.0 billion as at 31 December 2014, translating to a loan to deposits ratio

of 78.9%. The total sectoral distribution of the loans can be depicted on Figure 2.3 overleaf as

follows:

30

Figure 2:3Sectoral Distribution of Commercial Bank Loans in Zimbabwe

Source: Reserve Bank of Zimbabwe Monetary Policy Statement, 2015

This actually leaves room to research whether NPLs, a proxy of credit risk management

actually affect the lending practices of commercial banks in Zimbabwe and so influence the

level of economic growth.

2.2.3 Strategies of Dealing with Non-Performing Loans

In India, Visaria (2009) confirms that judicial arbitration is supportive in debt recovery

strategy. He acknowledged that legal (judicial) enforcement is slow in assisting in debt

recovery. He presupposes that difference in judicial quality in India affects the debt recovery

through the legal means this is due to unobservable country specific factors that affect firm

performance, growth prospects and financial decisions. The introduction of judicial

arbitration assisted in debt recovery in Indian banks. The judicial framework in India varies

greatly with Zimbabwe`s judicial system, such that this strategy may have little or even

different effects in addressing the vices of NPLs in Zimbabwe.

31

(Ikpefan & Mukoro, 2011) posit that in Nigeria, Asset Management Corporation of Nigeria

(AMCON) was established for the purpose of acquiring the Non-Performing loans (NPLs) of

banks with a view to freeing such banks from the burden of provisioning requirements –

Substandard, doubtful and lost accounts. The process of acquiring the NPLs involves the

injection of fresh funds thereby alleviating the liquidity and insolvency problems of the

affected banks. AMCON is an interventionist initiative employed in the management of

delinquent assets in the banking system. It operates as a variant of loan acquisition

companies, but has been adjudged to be more effective in the managements of NPLs. This

strategy has also been adopted in Zimbabwe, with ZAMCO having acquired NPLs amounting

to $65 million to date using other financing mechanisms provided for in its funding strategy

(RBZ, 2015). Risks of moral hazard may arise however as banks may mistakenly get the idea

that previous bad lending decisions will always be pardoned; hence there is need for such

asset management companies to only acquire secured loans from commercial banks.

The following are some of the recommended bank debt recovery tips, which are likely to help

increase their debt collection success; flexible repayment plans for customers experiencing

financial difficulty, well formulated hardship programs for borrowers that are late on their

repayment, extend or lower payments, interest rates, or lower fees when you anticipate

customer payment problems, create communication channels where customers can openly

discuss their issues. By proactively reaching customers early, you can prevent larger

problems later. This can be done by banks organizing regular pipelines of customer with

issues and working towards assisting them make repayments through discussions, and

outsourcing bank debt recovery to collection agencies in extreme circumstances when the

debt is not likely to be recoverable by the bank staff.

2.2.4 Relationship between Credit Risk and Profitability of Commercial Banks

(Basel, 1999) observes that banks are increasingly facing credit risk (or counterparty risk) in

various financial instruments other than loans, including acceptances, interbank transactions,

trade financing foreign exchange transactions, financial futures, swaps, bonds, equities,

options, and in the extension of commitments and guarantees, and the settlement of

transaction. The credit risk management strategies are measures employed by banks to avoid

32

or minimize the adverse effect of credit risk. A sound credit risk management framework is

crucial for banks so as to enhance profitability guarantee survival (Kolapo, et al., 2012)

2.2.4.1 Europe

(Fan & Yijun, 2014) examined the relationship between credit risk management and

profitability of selected banking institutions in Europe for the period 2007-2012; by

employing a series of regression analysis models. Their empirical findings showed that the

relationship between CAR and ROE and CAR and ROE is not significant. They attribute this

to the impact of systemic risks during the Global Financial Crisis. Having come into a period

of relative economic stability with the introduction of the multi-currency system in

Zimbabwe, this study would seek to test if indeed CAR and ROE have a significant

relationship given the reduction of systemic shocks of the hyperinflationary era, which can be

likened to the shocks of the Global Financial Crisis. Secondly, (Fan & Yijun, 2014) found

that there is a negative relationship between NPLR and ROE, implying that the higher the

NPL ratio, the less capital there is to invest. Therefore, combined with the findings from the

two proxies (CAR and NPLR) for credit risk management, conclude that there is a positive

relationship between credit risk management and profitability of commercial banks. Thus the

better the credit risk management is, the higher the profitability of commercial bank is.

(Hosna & Bakaeva, 2009) conducted a study to determine the impact of credit risk

management on profitability of four banks in Sweden using multiple regressions with two

independent variables. The results obtained from their regression model show that there is an

effect of credit risk management on profitability on reasonable level with 25,1% possibility of

NPLR and CAR in predicting the variance in ROE. So, the credit risk management strategy

defines profitability level to an important extent. However, their results leave a gap in that

ROE of commercial banks could in turn be influenced by other factors other than NPLR and

CAR, implying therefore that commercial bank profitability may not be wholly related to

credit risk management. This may mean that this bank’s profitability has other predictors or

variables that affect ROE more reliably than NPLR and CAR Furthermore, in light of the

increase in regulatory capital requirements as well as the incidence of high non-performing

loans in Zimbabwe, it would be prudent to carry out a study to ascertain if indeed NPLR

33

rather than CAR influences commercial bank profitability, especially at a time when

commercial banks are trying to raise capital to comply with the regulatory minimum capital

requirements.

On the contrary, (Athanasoglou, et al., 2005) examine the profitability behaviour of bank-

specific, industry related and macro-economic determinants, using an unbiased panel dataset

of South Eastern European credit institutions over the period 1998-2002. Their estimation

results indicate that, with the exception of liquidity, all bank-specific determinants

significantly affect bank profitability in the anticipated way. The macro-economic

environment has a direct impact on the aggregate performance of the industry. Further,

concentration is positively correlated with bank profitability. With respect to the

macroeconomic variables, inflation has a strong effect on profitability, while bank profits are

not significantly, affected by real GDP per capita fluctuations.

These results in South Eastern European countries suggest that commercial bank profitability

is not at all related to credit risk management but that the macro-economic environment

particularly inflation has a more pronounced effect on the bank profitability. This however

seems contrary to evidence in the Zimbabwean market as the banking sector remained

profitable, with an aggregate net profit of $52.8 million for the year ended 31 December

2014, which is well above the $3.4 million reported for the same period in 2013. A total of 14

banks out of the 19 operating banking institutions recorded profits for the year ended 31

December 2014 (RBZ, 2015). These results were achieved at a time when Zimbabwe is

undergoing a period of disinflation as well as generally poor macro-economic conditions.

This may mean that, credit risk management may influence bank profitability.

(Salas & Saurina, 2002)examined credit risk in two institutional regimes of Spanish

Commercial and Saving Banks" and used panel data to compare the determinants of loan

problem in Spanish banks in the period 1985-1997, using macroeconomic and individual

banks variables to explain credit risk. These variables are: GDP growth rate, family and firm

indebtedness, rapid credit expansion, portfolio composition, bank size, net interest margin,

capital ratio and market power. The study concludes the significance of early warning

indicators, the advantage of merger of banks, the role of banking competition and the type of

ownership in determining credit risk.

34

2.2.4.2 North and Latin America

(Duca & McLaughlin, 1999) conclude that high ups and downs in the bank profitability and

performance are highly correlated with the dissimilarity in credit risk, thereby implying that

raised exposure to credit risk has a direct link to a decline in the bank profitability in the

United States of America. This seems to agree with (Abreu & Mendes, 2001) who find a

significant association between loan ratio and profitability. (Gieseche, 2004) then lends

weight to these findings by submitting that credit risk is the most important risk for banks and

their business models such that business achievement totally depends on the correct

measurement of this risk.

(Epure & Lafuente, 2012) examined bank performance in the presence of risk for Costa-

Rican banking industry during 1998-2007. The results showed that performance

improvements follow regulatory changes and that risk explains differences in banks and non-

performing loans negatively affect efficiency and return on assets while the capital adequacy

ratio has a positive impact on the net interest margin. This suggests that in Latin America, the

results of the relationship are somewhat mixed and there exists a gap for the Zimbabwean

market that this research can fill by properly ascertaining whether regulatory changes, such as

the new minimum capital regulations indeed affect bank performance as indicated by bank

profitability in the study undertaken by Epure & Lafuente, (2012).

(De Young & Roland, 1994) observed that the United States Office of the Comptroller of the

Currency found the difference between the failed banks and those that remained healthy or

recovered from problems was the calibre of management. Superior managers not only run

their banks in a cost efficient fashion, and this generates large profits relative to their peers,

but also impose better loan underwriting and monitoring standards than their peers which

result to better credit quality. This assertion could be tested in Zimbabwe were locally owned

banks that have collapsed under the clasp of high NPLs and liquidity challenges since the

banking crisis of 2003-2004 which has been attributed to poor quality management in some

instances.

(Boahone, et al., 2012)stress that banks that have higher loan portfolio with lower credit risk;

improve on their profitability. (Angbanzo, 1997) concur in their supposition that banks with

35

larger loan portfolio appear to require higher net interest margin to compensate for higher risk

of default. (Cooper, et al., 2003) add that variations in credit risks would lead to variations in

the health of banks’ loan portfolio which in turn affect bank performance. Meanwhile, (Ducas

& Mclaughlin, 1990) had earlier argued that volatility of bank profitability is largely due to

credit risk. Specifically, they claim that the changes in bank performance or profitability are

mainly due to changes in credit risk because increased exposure to credit risk leads to fall in

bank performance and profitability

2.2.4.3 Middle East and North Africa

(Al-Khouri, 2011) further evaluated the effect of bank’s specific risk characteristics and the

overall banking environment on the performance of 43 commercial banks operating in six of

the Gulf Cooperation Council (GCC) countries over the period of 10 years. The study

employed the use of regression as an analysis tool, and its findings prove that credit risk,

liquidity risk, and capital risk are the key aspects that influence bank profitability in the GCC

countries. However this methodology is fraught with irregularities in that macro-economic as

well as market factors differ greatly from one country to another and as such the results

obtained from 43 different banking institutions in 6 different jurisdictions cannot be wholly

applied to each region. As such, it leaves a gap in trying to standardise the results and

establish the exact nature of the relationship of credit risk management and profitability

especially in countries like Zimbabwe which do not conduct Islamic banking.

(Almumani, 2013) attempted to assess the managerial factors that affect commercial banks`

profitability in Jordan. This study included all local Jordanian banks listed on its stock

exchange since 2000, with the notable exclusion of Islamic Banks which were excluded due

to their special operational characteristics. The major outcome of this study was that the

profitability of Jordanian Commercial Banks is influenced by operational efficiency, and that

other variables like liquidity, credit composition, credit risk did not show any statistical effect

on bank profitability. It was also concluded that higher credit risk may lead to higher

profitability, but with less significant impact on overall profitability. However the major

drawback of their methodology was the limited number of observations both time series and

36

cross-sectional for a market the size of Jordan, such that their results would not be an

adequate basis to discount the effects of credit risk management on profitability.

Conversely, in an effort to study the impact of bank regulations, concentration, financial and

institutional development on commercial banks’ margin, and profitability in the Middle East

and North Africa (MENA) nations from 1989 to 2005, using the unbalanced panel data

regression, (Ben-Naceur & Omran, 2008) found that bank capitalization and credit risk have

considerable and positive influence on net interest margin, cost efficiency, and profitability of

bank. Nonetheless, the time period within which this study was conducted is relatively a long

time ago, and the results of such a study may not be applicable in today`s rapidly changing

financial and economic markets, thus necessitating the need for more recent studies to

analyse the relationship between credit risk management and commercial bank profitability.

(Hakim & Neaime, 2002) investigate the impact of liquidity, credit and capital on bank

profitability in order to shed light on strength of risk management practices by using the a

regression model of time series and cross section data of banking institutions in Lebanon and

Egypt during the period (1993-1999).The study expressed the likelihood of borrowers not

repaying their loans as promised. Applying right loan policies with good assessment of credit

risk and determining appropriate amount of collateral with little concentration of loan and

good training of credit officers are part of good risk management.

2.4.4.4 Australasia and Southeast Asia

(Gizycki, 2001) examined the overall variability of Australian banks’ credit risk taking in the

1990s and found out that the impaired asset ratios of smaller banks tend to be more variable

than for the larger banks. Foreign banks with small assets bases within Australia experienced

particularly high levels of impaired assets and low but variable profits between 1990 and

1992. The variance of the full panel data was decomposed to distinguish variation across

banks and variation through time. Though the relationship between credit risk and variability

of bank profits was established, the time frame within which the study was conducted is long

and the banking sector has remarkably changed since the 1990s when the study was

conducted.

37

(Podel, 2012) assessed the effect of Credit Risk Management on the financial performance of

Nepalese banks using regression analysis. The study establishes that all credit risk factors

have an inverse influence on the financial performance of banks; conversely, the default rate

exerts a major impact on bank performance. The study encourages banks to create and

develop policies with the aim of not only reducing the exposure of the banks to credit risk but

also improving profitability.

(Zaid & Rosly, 2009) argued that profitability position of Islamic banks depends mainly upon

the level of risk in their investment projects. Thus, the inferred implication from their

assertion is that a higher degree of non-performing loans (risk) is expected to generate high

level of profitability. This is premised on the understanding that credit risks are deemed to be

the most important type of risks faced by a bank in its relationship with owner’s wealth

(Elgari, 2003). A positive relationship thus emerges from this model, being the higher level

of non-performing financing, the higher level of profitability. A drawback of this model is

that it ignores the effect of risk degree in explaining future earnings. On the whole, it does not

primarily conceptualize the relationship between credit risks, and profitability together.

(Aburbasheh, 2014) contends that non-performing financing has no significant influence on

profitability (ROE) of Islamic banks in Indonesia. It uses a sample of eleven Islamic

commercial banks that fully disclosed their financial data in bank of Indonesia from 2008

until 2013. Furthermore, the study employs partial least square model for path analysis to

analyse data variables and to test the research hypotheses. Non-performing financing were

used as an intervening variable in order to measure the ability of assets to generate profit in

long run, and to describe the capacity of bank in spreading risks and recovering default loans

(Sundarajan and Errico, 2002). However, in order to sufficiently analyse the relationship it

would be prudent to include another intervening variable standing for credit risk

management, something that is considered by this research through including CAR.

(Haneef, et al., 2012) risk management in Pakistani banks is improving but it adversely

effects the profitability behaviour, however, despite best efforts of respective banks and

regulatory bodies including State Bank of Pakistan, the amount of NPLs continues to grow.

NPL is also creating pressure on the banking sector for revenue, and inherently reducing

profitability. This suggests that there is a positive relationship between credit risk

management and commercial bank profits variability.

38

2.2.4.5 Sub-Saharan Africa

(Kurawa & Sanusi, 2014) studied a population sample of 16 banks quoted on the Nigerian

Stock Exchange as at 2011/2012 Data were analysed by calculating the profitability for each

year for the period of study. Random-effect generalized least squares (GLS) regression was

subsequently done by comparing the profitability ratio (proxy by ROA) to DR, cost per loan

assets (CLA) and CAR, as well as loan and age as control variables. The result confirms that

the independent variables (CRM indicators) have individual and uniting effect on the

profitability (ROA) of Nigerian banks. Consequently, this study provides additional evidence

that there is a significant positive relationship between CRM and profitability of Nigerian

banks.

(Felix & Claudine, 2008) investigated the relationship between bank performance

(profitability) and credit risk management. It could be inferred from their findings that return

on equity (ROE) and return on assets (ROA) both measuring profitability were inversely

related to the ratio of non-performing loan to total loan of financial institutions thereby

leading to a decline in profitability. The study also found out that an increase in loan loss

provision is also considered to be a significant determinant of potential credit risk and further

highlighted that credit risk in emerging economy banks is higher than that in developed

economies. (Ahmed, et al., 1998) in their study also found that loan loss provision has a

significant positive influence on non-performing loans. Therefore, an increase in loan loss

provision indicates an increase in credit risk and deterioration in the quality of loans

consequently affecting bank performance adversely, which supports (Felix & Claudine, 2008)

earlier supposition.

(Kargi, 2011) evaluated the impact of credit risk on the profitability of Nigerian banks.

Financial ratios as measures of bank performance and credit risk were collected from the

annual reports and accounts of sampled banks from 2004-2008 and analysed using

descriptive, correlation and regression techniques. The findings revealed that credit risk

management has a significant impact on the profitability of Nigerian banks. It concluded that

banks’ profitability is inversely influenced by the levels of loans and advances, non-

performing loans and deposits thereby exposing them to great risk of illiquidity and distress.

39

(Kithinji, 2010) assessed the effect of credit risk management on the profitability of

commercial banks in Kenya. Data on the amount of credit, level of non-performing loans and

profits were collected for the period 2004 to 2008. The findings revealed that the bulk of the

profits of commercial banks are not influenced by the amount of credit and non-performing

loans, therefore suggesting that other variables other than credit and non-performing

(Boahone, et al., 2012) utilized regression analysis in an attempt to reveal the connection

between credit risk and profitability of selected banks in Ghana and established that credit

risk components (non-performing loan rate, net charge-off rate, and the pre-provision profit

as a percentage of net total loans and advances) have a positive and significant relationship

with bank profitability. This shows that banks in Ghana enjoy high profitability regardless of

high credit risk, an opposing view to other views expressed in many studies that credit risk

indicators are negatively related to profitability. There is thus need to study the relationship

between in the Zimbabwean context to see if this relationship is applicable to sub-Saharan

countries as a whole.

(Muritala & Abayomi, 2013) analysed the relationship between credit risk management and

bank profitability of some selected commercial banks in Nigeria using econometric analysis

method for the period 2006- 2012. Their study shows that there is a significant relationship

between bank performance (in terms of profitability) and credit risk management (in terms of

loan performance). Hence if the risks presented by these variables are not managed, profits of

a banking institution will be unstable. This is underscored by their results which reveal that

the profit after tax was responsive to the credit policy of Nigerian banks.

(Kolapo, et al., 2012) employ panel data regression analysis of the effect of credit risk on

bank performance measured by return on equity The effect of credit risk on bank

performance measured by the Return on Assets of banks is cross-sectional invariant. That is,

nature and managerial pattern of individual firms do not determine the impact. Loan and

Advances ratio coefficient exerts most significant positive effect on the profitability across

the banking firms. According to their study, an increase in non-performing loan reduces

profitability; an increase in loan loss provision also reduces profitability and increase in total

loan and advances increase profitability. This should be expected as loans and advances

generate interest for banks. However, there is room for further study to ascertain if indeed

ROE is cross-sectional invariant and is unaffected by nature and managerial patterns of firms

in Zimbabwe.

40

(Tefera, 2011) uses regression model, to analyse the data which is collected from the National

Bank of Ethiopia (NBE) and from seven commercial banks of the country collected from

2001-2010. There was no correlation among independent variables (NPLR and CAR) which

means each of the independent variables explained the dependent variable separately. Both

nonperforming loan ratio and capital adequacy ratio has a negative impact on profitability‘s

of commercial banks in Ethiopia. The impact level of nonperforming loan ratio is negative

which means, a single unit increase in nonperforming loan ratio leads in decrease of

profitability of commercial banks of Ethiopia. Furthermore, he also finds that, NPLR is

inversely related with profitability whereas capital adequacy ratio has a direct relation with

profitability of banks.

(Flamini, et al., 2009) used an unbalanced panel of SSA commercial banks in their study.

Additionally, they employed annual bank and macroeconomic data for 41 SSA countries over

the period 1998–2006. They find that credit risk has a positive and significant effect on

profitability. This suggests that risk-averse shareholders target risk adjusted returns and seek

larger earnings to compensate higher credit risk. Macroeconomic variables significantly

affect bank profitability in Africa. In particular, inflation has a positive effect on bank profits,

which suggest that banks forecast future changes in inflation correctly and promptly enough

to adjust interest rates and margins. This shows mixed results from their study of the

relationship between credit risk management and profitability.

(Ikpefan & Mukoro, 2011) argue that, the effectiveness of the financial intermediation role of

banks in Nigeria is increasingly under threat as a result of soaring credit delinquency.

Experience over the years shows that many credit customers of banks default in their

repayment obligations. The consequence has been the deterioration in the quality of risk

assets and a rise in the level of non-performing loans and advances (NPLs) with the attendant

increase in the required loan loss provision. This is then seen to impact directly on the

profitability of banks. Non–performing loans are often used to measure the positive and

fitness of a bank’s credit risk management and theoretically NPLs are expected to have an

inverse relationship with a bank’s profitability (Afriyie & Akotey, 2010).

Problems in respect of credit especially, weakness in credit risk management have been

identified to be the main part of the major reasons behind banking difficulties. Loans forms

huge proportion of credit as they normally accounted for 10 – 15 times the equity of a bank

(Kitua, 1996). Poor loan quality starts from the information processing mechanism and then

41

increase further at the loan approval, monitoring and controlling stages (Liukisila, 1996).

This problem is magnified especially, when credit risk management guidelines in terms of

policy and strategies and procedure regarding credit processing do not exist or are weak or

incomplete.

Banks that have higher loan portfolio with lower credit risk improve on their profitability.

(Angbanzo, 1997) stressed that banks with larger loan portfolio appear to require higher net

interest margin to compensate for higher risk of default. (Cooper, et al., 2003) add that

variations in credit risks would lead to variations in the health of banks’ loan portfolio which

in turn affect bank performance. (Ducas & Mclaughlin, 1990) had earlier argued that

volatility of bank profitability is largely due to credit risk. Specifically, they claim that the

change in bank performance or profitability are mainly due to changes in credit risk because

increased exposure to credit risk leads to fall in bank performance and profitability.

(Mamman & Oluyemi, 1994) postulate that the profitability of a bank depends on its ability

to foresee, avoid and monitor risks, possible to cover losses brought about by risk arisen. This

has the net effect of increasing the ratio of substandard credits in the bank’s credit portfolio

and decreasing the bank’s profitability. The banks supervisors are well aware of this problem,

it is however very difficult to persuade bank mangers to follow more prudent credit policies

during an economic upturn, especially in a highly competitive environment. They claim that

even conservative mangers might find market pressure for higher profits very difficult to

overcome.

For Zimbabwe`s case, it is vital to carry out research into the relationship between credit risk

management and profitability of commercial banks. For instance, literature suggests that

when NPLs which are a commonly accepted measure of credit risk management rise,

commercial bank profitability tends to decline as a result of a heavy loan loss provisioning

burden. In Zimbabwe however, commercial bank profitability has actually improved

especially in the past year, in spite of rising NPLs. The banking sector remained profitable,

with an aggregate net profit of $52.8 million for the year ended 31 December 2014, which is

well above the $3.4 million reported for the same period in 2013, hence there is a knowledge

gap to carry out the research so as to properly ascertain the relationship of the two variables

in the Zimbabwean context.

The findings of the studies reviewed bring to the fore diverse outcomes. Based on the above

studies, the common determinants for credit risk management are the level of bad loans (non-

42

performing loans), problem loans, or provision for loan losses on the one hand, and on the

other hand, the frequently used proxy for profitability is return on assets (ROA )and ROE.

The regular approach used by the largest part of these studies was to analyse the effect of

credit risk management on any other variable.

In underscoring the importance of effective risk management in banking institutions, (Bessis,

2002) further asserts that, those banking institutions that actively manage their risks have a

competitive advantage. They take risks more consciously, they anticipate adverse changes,

they protect themselves from unexpected events and they gain the expertise to price risks.

The competitors who lack such abilities may gain business in the short-term. Nevertheless,

they will lose ground with time, when those risks materialize into losses.

Credit risk is by far the most significant risk faced by banks and the success of their business

depends on accurate measurement and efficient management of this risk to a greater extent

than any other risk (Gieseche, 2004)Increases in credit risk will raise the marginal cost of

debt and equity, which in turn increases the cost of funds for the bank and inherently affect

profitability (Basel, 1999).

The vast majority of the empirical literature studied utilised regression analysis in evaluating

the effect of credit risk management on several independent variables suited to the respective

regression models. Most of the reviewed literature used CAR, NPLR and Loan-Loss

Provision as the common proxies for measuring Credit Risk Management, whilst ROE and

ROA were the commonly used proxies for measuring Profitability.

Furthermore, the literature was also evaluated in terms of the geographical area covered by

the studies, analytical methods employed in the studies as well as the nature of relationship

found to exist between credit risk management and bank profitability. These classifications

are illustrated diagrammatically overleaf.

43

Figure 2:4: Evaluation of geographical area covered by literature

Source: Secondary Data

53%

47%

Classification of Empirical Studies

Geographically

African Countries

Other Countries

44

Figure 2:5 Evaluation of analytical methods used by scholars

Source: Secondary Data

Figure 2:6 Evaluation of results obtained by scholars

Source: Secondary Data

82%

18%

Analytical Tools Employed by Empirical

Studies

Regression Analysis

Other Methods

41%

35%

24%

Relationship Between Credit Risk

Management and Profitability

Positive Relationship

Negative Relationship

Mixed Relationship

45

2.5.7 Conclusion and Synopsis of Ensuing Chapter

In conclusion therefore, this review of both empirical and theoretical literature has

highlighted that there exists a relationship between the two variables of credit risk

management and profitability of commercial banks. This then provides a theoretical foothold

for the earlier hypotheses made in Chapter 1 of the two variables. The researcher would thus

like to proceed in statistically proving the relationship between the four variables of NPLs

and CAR which are proxies for credit risk management as well as ROA and ROE which are

proxies for commercial bank profitability. This would assist in ascertaining if this

relationship exists for commercial banks in Zimbabwe. The next chapter will provide an

analysis of the statistical testing of the variables which will provide an indication of whether

there exists a relationship between credit risk management and profitability of commercial

banks in Zimbabwe.

46

Chapter 3: Research Methodology

3.0 Introduction

The way in which research is conducted may be conceived of in terms of the research

philosophy subscribed to, the research strategy employed and so the research instruments

utilised (and perhaps developed) in the pursuit of a goal – the research objective(s) - and the

quest for the solution of a problem - the research question. This chapter therefore deals with

the research methodology of the study, including the research philosophy, research design,

setting, population, sample and data-collection instruments, the model specification and the

logic behind the systematic steps adopted in solving the research problem

3.1 Research Philosophy

Research philosophy relates to the development of knowledge and the nature of that

knowledge (Saunders, et al., 2009). There are two major ways of thinking about research

philosophy: epistemology and ontology.

3.1.1 Epistemology

The researcher`s epistemological position is positivistic. Positivism focuses on the

explanation of social reality whilst the other alternative, interpretivism concentrates on the

understanding of the subjective meaning of social actions. Taking cognisance of the primary

research objective, the main aim of this research is on finding the relationship between the

credit risk management and profitability of commercial banks rather than how the

relationship is. Hence, the process of generating hypotheses, testing through statistical

programs and generating the explanation of laws matches the concept of the positivism

47

position. Furthermore, this study is undertaken through objective financial ratio valuation and

statistical test and from this perspective, the positivistic epistemological position is more

appropriate than interpretivism.

3.1.2 Ontology

This research supposes that profitability is something that the bank “has” and it is something

that independently exists in the world, can be observed, categorized and measured. Therefore,

the researcher wants to measure how it could be determined by credit risk management but

not the point that how it is inherited from social factors’ interaction. Thus, the researcher

chooses objectivism as his ontological standing point.

The subjectivism approach is inappropriate for this research since it assumes that, social

phenomena and their meanings are continually being accomplished by social actors. It

implies that social phenomena and categories are not only produced through social interaction

but that they are in a constant state of revision. This thus, implies that the commercial bank

profitability and credit risk management of commercial banks are not independent of social

actors, and this renders it unsuitable.

3.2 Research Design

The research design used for this research is the explanatory study. This research design will

be used basing on the contention that the ultimate objective of this study is to test if there

exists a relationship between credit risk management and the profitability of commercial

banks and how the credit risk management could potentially affect the profitability of a bank.

The exploratory design is inappropriate for this study since it is used when the research

problem is badly understood by the researcher. Though this form of research often leads to

new theories and concepts, there is also the danger that the research will produce false leads

or useless theories.

48

3.2.1 Research Strategy

The importance of research strategy is whether it will enable researchers to answer their

research questions and meet their objectives. This means the choice of research strategy will

be guided by research questions and objectives (Saunders, et al., 2009).

This research should be archival strategy which involves the data from administrative

records. The data are collected from the annual reportsand financial statements of selected

banks, a type of documentary secondary data. So the researcher considers that archival

strategy is more appropriate for the study.Actually, archival research enables the social

scientist to both enhance and challenge the established methods of defining and collecting

data. Original source materials may be discussed and analysed to ask new questions of old

data (Lewsi-Beck, et al., 2004). It provides a comparison over time or among geographic

areas to verify or challenge existing findings (Lewsi-Beck, et al., 2004). Or the researchers

draw together evidence from different sources in order to provide a bigger picture.

Other research strategies are not suitable for the purposes of this research, for instance, action

strategy focuses management research such as resolution of organizational issues while our

research purpose is testing the existence of relationship between credit risk management and

profitability. It is thus too excessive and broad to be adopted for this research. Though the

researcher takes a localised focus on Zimbabwean banks, this research is however not a case

study research. Case study emphasizes on one unit or limited variables to gain rich

understanding of the context in the research (Morris & Wood, 1991). Nonetheless, this

research concerns itself with 4 Zimbabwean commercial banks out of a sample of 16

commercial banks using 4 financial ratios over a period of 5 years. This research is not

focused on deep understanding to one specific case thus case study is inappropriate.

Moreover, the data in our research will be only used once and without further test. To this

extent, our research strategy is not grounded theory.

3.2.2 Research Method

Research methods can be broadly classified into two distinct groups, qualitative and

quantitative research methods. According to (Bryman & Bell, 2011), quantitative research

emphasizes quantification of the data collection and analysis. Usually, quantitative research

49

conducts a deductive approach to the relationship between theory and research which focus

on testing of theory (Bryman & Bell, 2011).

This research is predominantly a quantitative research relying primarily on financial data and

ratios obtained from annual reports and audited financial statements of the sample banks

under study. Questionnaires are also used to compliment the secondary data obtained from

annual reports and financial statements. Findings from the interviews and questionnaires are

then used to compare and contrast the results of the statistical analysis.

Qualitative research is not appropriate for this study since it emphasizes the words rather than

quantification of data. It prefers conducting an inductive approach to the relationship between

theory and research which aims on the generation of theories.

3.3 Population and Sample

The study population for the purposes of this research consists of the 16 Commercial Banking

Institutions in Zimbabwe as at 31 December 2014.

3.3.1 Study Population

Table 3:1

Bank Year of Incorporation NPLR

AfrAsia Bank Zimbabwe Limited 1997 18.26%

Allied Bank Zimbabwe Limited 2005 7.68%

Agricultural Development Bank of Zimbabwe 1999 8.09%

BancABC Zimbabwe Limited 1997 12.4%

Barclays Bank Zimbabwe 1912 0.6%

Commercial Bank of Zimbabwe Limited (CBZ) 1980 4.57%

Ecobank Zimbabwe Limited 2002 5.79%

50

FBC Bank Zimbabwe 1997 5.06%

MBCA Bank Zimbabwe 1956 2.79%

Metbank Zimbabwe Limited 1999 12.23%

NMB Bank Limited 1992 17.7%

Stanbic Bank Zimbabwe Limited 1965 5.0%

Standard Chartered Bank Zimbabwe 1892 4.94%

Steward Bank Limited 2009 21.27%

ZB Bank Limited 1951 18.0%

Source: Developed by the researcher using the Central African Stock Exchanges Handbook

2014.

3.3.2 Sample Design

The researcher will choose a representative sample from the population to get information

from. This will deemed to be a true reflection of the whole population, so the results are to be

interpreted with this in mind. Questionnaires are to be sent to these selected sample

population as well as other banking institutions.

A purposive sampling approach was used in selecting sample banks with available data in

their published financial statements. The working sample population was subsequently drawn

from the total study population of Zimbabwe`s Commercial Banks. This helps in improving

the reliability of the research, data as well as the results thereof. Furthermore, the NPL to

Total Loan Advances metric was also used in coming up with the sample population from the

study population

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3.3.3 Sample Population

Table 3:2

Bank Year of Incorporation NPLR

AfrAsia Bank Zimbabwe Limited 1997 18.25%

Barclays Bank Zimbabwe Limited 1912 0.6%

NMB Bank Zimbabwe Limited 1999 17.7%

Standard Chartered Bank Zimbabwe 1892 4.94%

Source: Developed by researcher

3.4 Data Collection

The major source of data for the purposes of analysing the relationship between credit risk

management and profitability of commercial banks was secondary data obtained in the

audited financial statements, annual reports and financial results of the selected commercial

banks. Primary data collected through questionnaires will also be used to provide a basis for

comparison with the results obtained from secondary data collection.

3.4.1 Sources of Primary Data

Primary data for this research will be collected using questionnaires. The information

obtained from the primary data collection through the use of questionnaires will be used to

compliment empirical findings from regression analysis as well as providing a basis for

comparison. Questionnaires will be sent out to credit officers of commercial banks in

Zimbabwe.

Primary data is also considered to be more up to date and has close proximity to the truth.

Also the primary data gathered fits well and relevant in the achievement of objects for this

research. This is because data is gathered from the people who are aware of the area covered

52

under this study. However, it is time consuming to gather as it involves booking of

appointments in terms of interviews and the data is expensive to gather because of traveling

to and from the area of study. Furthermore, the cost element therefore means that the whole

population will not interviewed or questioned, however the sample population will be chosen

and is assumed to be a true reflection of the whole population.

3.4.2 Sources of Secondary Data

In order to effectively perform the regression analysis, collection of the data for the variables

that are going to used needs to be collected. The variables to be used are ROE, CAR, CLA

NPLR and Size of the bank as a control variable. These data are obtainable from the audited

financial statements as well as company annual reports of the selected banks. The data

gathered was sourced additionally from the internet, textbooks, publications and journals. The

data was found more quickly and cheaply because the researcher used the library resources

and the internet.

However, secondary data posed problems for the researcher in that, the secondary data

previously gathered is not always accurate in addressing the problem since in some cases, it

was collected for different purposes such as assessing debt recovery tools as an operational

strategy for banks in Nigeria. Again, some of the data were out-dated since some journal

articles have been written over the past decades.

3.5 Data Collection Instruments

Data collection instruments refer to devices used to collect data such as questionnaires, tests,

structured interview schedules and checklists (Seaman 1991:42). The research instruments

translate the research objectives into specific questions items the responses to which will

provide the data required to achieve the research objectives (Giddaiah, 2009).

53

3.5.1 Questionnaire

For the purposes of this research, a mixture of both open ended and closed ended

questionnaire was used in order to adequately obtain information necessary for establishing

the relationship between credit risk management and commercial bank profitability. Pursuant

to this, the researcher prepared very concise, pre-planned set of questions designed to yield

specific information to meet the particular needs for research information about the topic

under review. The questionnaires were sent out to a sample of practitioners and credit officers

of commercial banks in Zimbabwe.

The major benefits of such a questionnaire design are that it allowed for uniformity of

questions as each respondent received the same set of questions phrased in exactly the same

way. In turn, this enabled the standardisation of responses especially as the questionnaire was

highly structured and the conditions under which the questionnaire is conducted are

controlled. Furthermore, respondents feel that they remain anonymous and can express

themselves in their own words without fear of identification. Additionally, they are

economical since the expense and time involved is usually minimal as compared to other

methods such as interviews in which the interviewer has to be trained.

However, the two major drawbacks are that the respondents’ motivation is difficult to assess,

affecting the validity of response. Nonetheless, through judgemental sampling, this risk of

bias is dealt with as respondents who are adjudged to be as objective as possible are selected.

Again, unless a random sampling of returns is obtained, those returned completed may

represent biased samples. Furthermore, the questionnaires had to undergo an approval

process within various banks in line with their internal policies and some banks did not

approve the researcher`s questionnaires.

3.5.1.2 Pilot Studies

A pilot study on the questionnaire was carried so as detect and eliminate difficulties in the

wording and structure of the questionnaires. The pilot survey was carried out using a small

initial batch questionnaires sent out to a small sample of respondents. Thereafter the problems

with the wording of the questions were cleared and free from mistakes. In addition to this,

54

pilot survey also helps the researcher by finding out the average time to be allocated would

take to administer the questionnaire and whether all the instructions and questions were clear.

It is also useful in ensuring that there are no problems of respondents misinterpreting the

questions.

3.6 Model Specification

According to a study conducted by (Haslem, 1968), profitability of a bank depends on factors

including management, size, locations and time. (Guru, et al., 1999) also concluded that

bank’s profitability is influenced by internal determinants and external determinants. In this

study, the researcher aims to study only one of internal determinants of commercial banks’

profitability - credit risk management. In order to avoid the possibility that the relationship is

due to some other factors, the researcher introduced a control variable. Following the model

of Samy& Magda (2009), the researcher selected bank’s size as our control variable. In

previous researches, firm size has been measured on many different grounds and it is said that

it does not matter which measure of firm size is used. One common measure that is proven as

the most interchangeable to use as a measure for firm size; is the natural logarithm of total

assets (Shalit & Sankar, 1977).

55

3.6.1 Summary of Variables

Table 3:3

Variable Name Calculation Method

Dependent Variables ROE Net income/Total Equity

Capital

ROA Net Income/Total Assets

Independent

Variables

CAR Total Capital/ Risk Weighted

Assets

NPL NPL/Total Loans

Control Variable Bank Size (LNTA) Natural logarithm of total

assets of bank

Source: Developed by the Researcher

3.6.2 Multivariate Regression

(Parramore & Watsham, 1997) state that a regression analysis tests the statistical strength of

the model as hypothesized. Thus the researcher will conduct a regression analysis in order to

ascertain the relationship between the dependent and independent variables. The model is

based on the Ordinary Least Squares, which generally takes the form of:

Where:

56

The OLS is a regression estimation technique that "The least squares regression line of y on x

is the line that makes the sum of the squares of the vertical distances of the data points from

the line as small as possible" (Moore, 2009). For the purposes of this research, a model

similar to that used previously by other researchers will be employed, considering that there

is more than one independent variable. Therefore, the researcher needs to move from single-

independent-variable regressions to equations with more than one independent variable.

Implying that, multivariate regression model needs to be introduced.

Where:

A multivariate regression coefficient indicates change in dependent variable associated with

one unit increase in one independent variable, holding other independent variables constant

(Studenmund, 2011). Based on all the information above, the researcher will therefore

perform the following regressions:

Where:

57

According to (Studenmund, 2011), there are 7 assumptions to make for OLS estimators to be

best available:

The regression model is linear, is correctly specified, and has an additive error term.

The error term has a zero population mean.

All explanatory variables are uncorrelated with the error term.

Observations of the error term are uncorrelated with each other (No serial

correlation).

The error term has a constant variance (No heteroskedasticity).

No explanatory variable is perfect linear function of any other explanatory variables

(No perfect multicollinearity).

The error term is normally distributed. (Optional)

Hence there is need for the testing of linearity, multicollinearity and heteroskedasticity in the

later parts.

3.6.3 R2 Test

When evaluating the overall fit of a regression equation, we need to take a look at a measure

named R2 or the coefficient of determination. R

2 is the ratio of the explained sum of squares

to the total sum of squares:

The higher R2 is, the closer the estimated regression equation fits the data. R

2 measures the

percentage of the variation of Y around that is explained by the regression equation. R2 lies

between 0 and 1, the closer the value to 1, the better the overall fits (Studenmund, 2011).

58

3.6.4 Multicollinearity

Multicollinearity will cause the variances and standard errors of the estimates to increase and

the t-scores to decrease. However, it will not bias the estimate and the overall fit of the

equation (Studenmund, 2011). In our research, we therefore will test the multicollinearity of

CAR and NPLR. Since there are only two explanatory variables, the simplest way to detect

multicollinearity is to examine the simple correlation coefficients between CAR and NPLR.

If the r is high in absolute value, then the two variables are quite correlated and

multicollinearity is a potential problem. Some researchers pick an absolute value of 0.80, and

concern about multicollinearity when the correlation coefficient exceeds 0.80 (Studenmund,

2011).

3.6.5 Heteroskedasticity

Heteroskedasticity exists if the variance of the distribution of error terms changes for each

observation or range of observations (Studenmund, 2011). Heteroskedasticity although does

not bias the estimate of coefficient, it could cause the OLS estimates of SE ( )s to be biased,

leading to unreliable hypothesis testing. In order to test for heteroskedasticity, the researcher

will perform the Bresuch-Pagan-Koenker test for our data. It is an approach for detection of

heteroskedasticity by running a regression with the squared residuals as the dependent

variable (Studenmund, 2011). This test is deemed as being more rigorous for small sample

data sizes, and is thus appropriate for the purposes of this study.

3.7.0 Justification of Variables

3.7.1 Proxies for Credit Risk Management

3.7.1.2 Non-performing loans

(Hosna & Bakaeva, 2009)state that NPLs, in particular, indicate how banks manage their

credit risk because it defines the proportion of loan losses amount in relation to Total Loan

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amount. According to the (World Bank, 2013), NPLs are defined as the loans overdue by

more than 90 days and should be the gross value of the loan as recorded in the balance sheet

not just the amount that is overdue.

Default Ratio is the resultant ratio that measures the proportion of NPLs against the total

loans for a period. It thus gives an assessment of the total borrowers default on the conditions

of loans and advances for a given period. Essentially it simply measures the efficiency of the

loan portfolio management for a given bank within a given period of time.

3.7.1.3 Capital Adequacy Ratio

CAR is a ratio that measures the total capital of bank articulated as a percentage of its risk

weighted credit coverage (Kolapo, et al., 1996); (Appa, 1996). (Afriyie, 2010) postulates that

this variable is the core measure of a bank's financial strength from a regulator's point of

view. It consists of the types of financial capital considered the most reliable and liquid,

primarily shareholders' equity. Banks with good Capital Adequacy Ratio have good

profitability. The formula used to calculate CAR is the one previously used by researchers

(Ara, et al., 2009)This formula is:

3.7.1.4 Cost per loan Asset Ratio

CLA is the average cost per loan advanced to a bank customer in monetary terms. Its

primary purpose is to indicate the efficiency of commercial banks in distributing loans to

customers. (Appa, 1996). Since this ratio measures the efficiency with which loans are

advanced, the lower the cost per each loan advanced, the higher the probability of increased

profitability for a commercial bank. It can be calculated as follows:

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3.7.2 Proxies for Commercial Bank Profitability

3.7.2.1 Return on Equity

This is a dependent variable and it measures the return on shareholders’ investment in the

bank. ROE was used as the indicator of the profitability in the regression analysis because

ROE along with ROA has been widely used in earlier research (Li & Zou, 2014).It shows the

effectiveness of management in the utilization of the funds contributed by the shareholders of

the bank. It is given by the ratio of net income and total equity capital, where net income

means the net income after tax and total equity capital is contributed by the bank’s

shareholders.

3.8 Control Variable

Bank size as measured by the natural logarithm of total bank assets (LNTA) has been used as a

dummy variable to control for the effects of size on the credit risk management process of commercial

banks Laeven (2005)

3.9 Hypotheses

Basing on all the previously explained indicators for both profitability of commercial banks

and their credit risk management, the following hypotheses were thus assumed.

3.9.1 Null hypothesis: There is no correlation between Capital Adequacy Ratio (CAR),

Cost per Loan Asset (CLA) and Non-Performing Loans Ratio (NPLR) and (Return on

Equity) ROE of commercial banks in Zimbabwe.

3.9.2 Alternative hypothesis: There is a correlation between CAR, NPLR, CLA and ROE

commercial banks in Zimbabwe.

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3.10 Data Analysis Procedure

Initially, secondary data, being audited financial statements were collected from the websites

of the sample banking institutions as well as other publications and internet sources.

Secondly, relevant information to help with the research was gathered by means of

questionnaires and interviews. Thirdly, data obtained through the steps mentioned above

were aggregated, analysed and subsequently compiled in a logical and coherent manner.

Fourthly, the data collected were analysed through the use of regression analysis in order to

establish the relationship between credit risk management of commercial banks in Zimbabwe

and the profitability of the former, with a particular emphasis on CAR, NPL, CLA ROE, .

Finally, findings and recommendations as well as the conclusion will be presented. The

estimation of the model and other diagnostic tests were carried out using SPSS (Version

16.0).

3.11 Time Horizon

Data were collected from the audited financial statements of the sample banks from February

2009 to 31 December 2014. This period is particularly important in the Zimbabwean context

as it coincided with the inception of the multi-currency dispensation in the Zimbabwean

economy. This marked a significant change in the economic landscape of the country, and

several economic and financial variables. As a result, this research considers that period

independently due to the shift in the economic landscape to gain a better understanding of the

new challenges, variables as well as other factors that come into play in studying credit risk

management and profitability of commercial banks. Though the correlation coefficient

between credit risk management and profitability might be significantly influenced by the

shift in economic fundamentals as result of dollarisation, the relationship itself will not be

impacted. What we are concerned in our research is if the relationship between credit risk

management and profitability exists, not the exact correlation coefficient.

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3.12 Chapter Summary

This chapter discussed the research methodology of the study and described the research

design, population, sample, data-collection instrument, research philosophy and the

specification of the model. The research design employed is the explanatory design, and the

research is placed in the positivistic epistemological position. The researcher chooses the

objectivism as his ontological stand. To effectively carry out the study, the researcher

employs the archival research strategy. Further, multivariate regression analysis will be

undertaken in order to test the relationship between the subject variables. Questionnaires and

interviews will be utilised to provide information and data to contrast with the findings

obtained from statistical analysis. The ensuing chapter will cover extensive analysis of the

data obtained for the study.

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CHAPTER 4: DATA PRESENTATION AND ANALYSIS

4.0 Introduction

Chapter four presented herein looks at the analysis and presentation of the results obtained by

the researcher in studying the relationship between credit risk management and the

profitability of commercial banks in Zimbabwe. The findings and analysis were directed

towards addressing research objectives and answering research questions. Findings from

primary data gathered through the use of questionnaires are also presented. In this research

the researcher used multiple regression analysis which is used to test whether one or more

independent variables (predicates) influence a dependent variable (outcome variable) and if

this effect is positive or negative. Prior to the model estimation, diagnostic tests were

conducted to test for linearity of the data, heteroskedasticity, and normality of errors,

multicollinearity and serial autocorrelation of the data.

4.1.0 Questionnaire Response Rate

A total of 35 questionnaires were prepared and 30 questionnaires were returned. The

questionnaires were sent out to the credit officers as well bank practitioners of sample

commercial banks in Zimbabwe. The response rate was 85%, and the bulk of the

questionnaires that were not returned were because of the long approval process the

questionnaires had to go through at each of the respective commercial banks in line with each

respective commercial bank`s internal policy. The remainder was in cases where, some credit

officers had travelled out of the country, and one had gone on maternity leave.

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Figure 4:1 Questionnaire Response Rate

Source: Generated by researcher using SPSS Output

4.1.1 Work Experience

35 % of the respondents had work experience of between 1-3 years, and these were the

majority of the respondents. A further 28 % of the practitioners revealed that they had work

experience of more than 10 years in the banking industry. Workers with experience of

between 4-10 years constituted 25% of the questionnaire respondents. Only 12 % of the

respondents had work experience of less than 1 year. The varied work experience of the

respondents was desirable as this would allow for diverse views of the research objectives as

well as the research questions. Combined, workers with between 1-3 years and more than 10

years represented 63 % and this would ensure factual and insightful views on credit risk

management and commercial bank profitability. These findings are revealed in the figure

overleaf:

35

30

Questionnaire Response Rate

Questionnaires Prepared

Questionnaires Returned

65

Figure 4:2 Work Experience

Source: SPSS analysis output

4.1.2 Causes of high non-performing loans in Zimbabwe

A total of 45 % of the respondents agreed that the major cause of non-performing loans in

Zimbabwe was ineffective credit risk management techniques, and these represented the

majority of the respondents. This finding helped in answering one of the secondary objectives

of this study, which was to ascertain the major cause of non-performing loans in Zimbabwe’s

commercial banking system. 28 % of the respondents felt that the high non-performing loans

was as a result of a poorly performing economy such that borrower’s ability to repay loans

was constrained by low aggregate demand of products in the case of corporates or

retrenchments and low disposable salaries where individual and household borrowers were

concerned. 25 % of the respondents viewed imprudent lending practices by banks as the

major driver of toxic loans. 2 % viewed unwillingness by borrowers to repay loans as the

major cause of non-performing loans and another 2 % attributed non-performing loans to

other factors such as inadequate underwriting of risks as they come, poor risk evaluation of

borrowers and get rich quickly mentality of bank executives. These findings are shown by

figure 4:3 below:

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Figure 4:3 Causes of Non-performing loans in Zimbabwe

Source: SPSS analysis output

4.1.3 Impact of non-performing loans

An open ended question was used to objectively assess the impact of high non-performing

loans on Zimbabwe`s commercial banking system. The majority (45 %) of the respondents

broadly concurred that the resultant loan impairments and write-offs negatively affect the

profitability of commercial banks. 25 % of the responses stated that high non-performing

loans eroded the core capital of commercial banks, and 20 % saw non-performing loans as

the cause of bank closures.10 % of the respondents agreed that non-performing loans reduce

the liquidity of commercial banks such that they cannot lend to the productive sectors of the

economy. Only 5 % stated that the major impact of non-performing loans was reduced net

interest income.

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Figure 4:4 Impact of Non-performing loans

Source: generated by the researcher using SPSS output

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Impact of NPLs on Commercial Banks

Impact of NPLs on Commercial

Banks

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4.1.4 The relationship between credit risk management and commercial bank

profitability

Figure 4:5 Relationship between credit risk management and non-performing loans

Source: SPSS analysis output

80% of the respondents stated that there existed a relationship between the profitability of

commercial banks and only 20% opined that there exists no significant relationship between

credit risk management and commercial bank profitability. This answers the primary

objective of this research of establishing whether a relationship exists between credit risk

management and commercial bank profitability. As to how credit risk management affects

commercial bank profitability, the respondents’ agreed that there exists a negative

relationship such that poor credit risk management practices caused a decline in bank profit

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4.1.5 Credit Risk Evaluation and Assessment Tools

Figure 4: 6 Credit risk evaluation and assessment tools

Source: SPSS analysis output

35% of the respondents identified credit scoring models as the most commonly used credit

risk evaluation and assessment tool in commercial banks, closely followed by internal ratings,

as evidenced by 30% of the questionnaire responses. 15% stated that they use the 5Cs method

in assessing and monitoring credit risk, and 10% said they used CAMPARI method. Stress

testing and SWOT analysis accounted for 8% and 2% of the responses respectively. The use

of credit reference bureaus, financial analysis, Porter`s 5-forces model and rating agencies

were also identified as some of the techniques that can be used by commercial banks in

assessing and monitoring credit risk.

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4.1.6 Effectiveness of Credit Risk Management Policies in Zimbabwe

Figure 4:7 Effectiveness of Credit risk management policies

Source: SPSS analysis output

52% of the respondents view the current credit risk management policies of commercial

banks in Zimbabwe as weak, whilst 40% rated the credit risk management policies in

Zimbabwe as average. The remaining 8% are of the view that Zimbabwe has strong credit

risk management policies. This is consistent with the findings obtained through the

questionnaire which stated that the major cause of non-performing loans in Zimbabwe was

ineffective credit risk management techniques adopted by commercial banks.

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4.1.7 The most Effective Policy of reducing credit risk in Zimbabwe

Figure 4: 8 Effective policy of reducing credit risk in Zimbabwe

Source: SPSS analysis output

For a Zimbabwean commercial bank trying to reduce credit risk, 35% of the respondents

identified the building of long term relationships with customers as the most effective method

of reducing credit risk in the market. Another 33% were of the view that screening and

monitoring of prospective borrowers was a very effective means of reducing credit risk.

Collateral requirements and credit rationing each accounted for 18% and 14% of the

responses respectively, as the most effective policy of reducing credit risk management.

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4.1.8 Effectiveness of a national credit reference bureau in reducing credit risk

Figure 4:9 Effectiveness of a national credit reference bureau

Source: SPSS analysis output

The majority of the respondents, 64% agreed that the creation of a national credit reference

bureau would help reduce credit risk management in Zimbabwe to a greater extent, whilst

22% were of the view that its effects would only be moderate. The remaining 14% concurred

that the creation of a national credit reference bureau would be less effective, arguing that its

strength would come from how the market players themselves choose to utilise it and follow

its credit references.

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4.1.9 The most effective strategy for recovering non-performing loans in

Zimbabwe

Figure 4:10 Loan recovery strategies

Source: SPSS analysis output

The majority of the respondents, constituting 55%of the respondents recommended the

extension or lowering of interest fees and payments to struggling borrowers as the strategy

most likely to yield positive benefits in attempting to recover non-performing loans. 25%

recommended legal strategies such as obtaining repossession orders or demand notices as the

most effective method. Having a centralised debt collection function garnered the support of

178% of the respondents whilst only 2% recommended debt scoring as the most effective

strategy.

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4.1.10 Effects for commercial banks complying with Basel III Committee

guidelines on bank supervision

Figure 4:11 Effects of compliance with Basel III guidelines on bank supervision

Source: SPSS Analysis Output

64% of the respondents were of the view that there are positive effects to be realised by

Zimbabwean commercial banks in complying with the Basel III committee guidelines on

bank supervision. 18% indicated that there would not be any effect on commercial banks

through complying with the Basel III requirements. The other 18% was not sure whether

there would be any positive effects or not.

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4.1.11 Summary of Questionnaire Findings

From the responses obtained through the questionnaire, it was established that there exists a

relationship between credit risk management and the profitability of commercial banks in

Zimbabwe. High non-performing loans in Zimbabwe were largely attributed to ineffective

credit risk management systems by local banks. The major impacts of poor credit risk

management as evidenced by high non-performing loans were identified as erosion of banks`

core capital, reduced profitability through lower net interest income, bank closures as well as

higher loan impairments and write-offs which negatively affect the economy. The extension

or lowering of interest rates, fees and payments was identified as the most effective method

of recovering non-performing loans. The building and fostering of long term relationships

with borrowers was identified as the most effective method of lowering credit risk in the

market and the creation of a credit reference bureau was also viewed to have a significant

impact in lowering credit risk. Compliance to Basel III committee guidelines on bank

supervision and the implementation of international credit risk standards were viewed to have

positive impacts on commercial banks and improve profitability.

The adoption of more sound credit risk management strategies and tools, training and hiring

of competent and skilled credit officers, enacting of regulations to punish risky lending by

banks as well as a shift in the mind set of bank executives such that they have a long term

view of profitability were also suggested as some of the ways in which credit risk

management frameworks of commercial banks in Zimbabwe could be improved.

4.2.0 Regression Analysis Results

Below the researcher uses regression command for administration of regression. This is

followed by the output of these SPSS commands.

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Table 4:1

Variables Entered/Removedb

Model

Variables

Entered

Variables

Removed Method

1 LNTA, CAR,

NPL, CLAa

. Enter

a. All requested variables entered.

Source: SPSS Regression output

Table 4:1 above displays the variables entered or removed from the study at any point in time

from the inception of the study. It also shows the four independent variables in the study,

which are CAR, NPL, CLA and the control variable LNTA. Since there was no variable

removed from the study, the third column remains empty. Lastly, the fourth column shows

the method adopted by the researcher to enter data into the model, and in this case, the

researcher utilised the “enter method” to either enter or remove model variables.

4.2.1 Analysis of Variance

Table 4:2

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 12607.604 4 3151.901 8.647 .001a

Residual 5467.497 15 364.500

Total 18075.101 19

a. Predictors: (Constant), LNTA, CAR, NPL, CLA

b. Dependent Variable: ROE

Table 4:2 above provides a summary of the analysis of variance in the model. The regression

row displays information about the variation that is not accounted for by the model, whilst the

77

mean square is the sum of the squares divided by the degrees of freedom. If the significance

value of the F statistics is small, then the independent variables perform an adequate role in

explaining the variations in the dependent variable. Since the P-value is 0.001(less than 0.05),

it shows that, that the model of ROE is significant at the 5% level of significance

4.2.2 Descriptive Statistics

Table 4:3

Descriptive Statistics

Mean Std. Deviation N

ROE 5.2625 30.84349 20

CAR 18.3990 9.20260 20

NPL 3.7168 6.48745 20

CLA 38.7702 21.79782 20

LNTA 19.1265 .54145 20

Source: SPSS regression output

Table 4:3 above details the descriptive statistic of the regression model undertaken. The mean

of ROE, which is the dependent variable, is 5.26 and its standard deviation is 30.84, which

indicates some variation in the levels of profits of the sampled banks albeit to a lesser degree.

Essentially, the sample banks in this study have greater diversification on their ROE ratio. It

can also be observed that the remaining independent variables also exhibit some level of

variation, with CLA displaying the highest level at 21.79. The lower the standarddeviation of

the independent variables (CAR, NPL, and CLA) in relation to the standard deviation of the

dependent variable (ROE), the lower the risk exposure of the profitability of the banks.

Furthermore, the statistics also revealed that, the biggest value of NPLS is 19.88% and the

smallest value is 0 % which means the spread among value ofNPLs is equal to their biggest

value.

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The natural logarithm of total bank assets (LNTA) was chosen to be the control variable in

this study. Total size of total bank assets in the study ranges from $150,000,000 being the

lowest to $450,000,000 being the highest. The banks in this study represent a 25 % of the

total commercial banks in Zimbabwe as at 31 December 2014. This then gives a fair

representation of Zimbabwe`s commercial banking system.

4.2.3. Tests for Multicollinearity

In an effort to establish the nature of the correlation between the dependent and the

independent variables and also to ascertain whether or not multicollinearity exists as a result

of the correlation among variables, the table below is drawn up for such purposes.

Multicollinearity is a situation where the explanatory variables are nearly linear dependent

(Jurczyk, P(2011). In the table below we can observe that the highest correlation among all

the variables is 0.467 which is the correlation between CAR and CLA. However, researchers

always prefer an absolute value larger than 0.8 to be enough to cause multicollinearity

(Studenmund, A (2011). Considering that 0.467 is quite far from 0.8, it can be concluded that

there is no problem of multicollinearity among the variables chosen for the study.

Table 4:4

Correlations Matrix of Variables

ROE CAR NPL CLA LNTA

Pearson Correlation ROE 1.000

CAR .325 1.000

NPL -.443 -.445 1.000

CLA -.412 .467 -.286 1.000

LNTA .097 .028 -.060 -.274 1.000

Source: SPSS regression analysis

Table 4 above also reveals that, the values on the diagonal are all 1.000, indicating that each

variable is perfectly correlated with itself. The highest correlations with the dependent

variable (ROE) are for NPL (-0.443) and CLA (-0.412). Both these correlations are negative,

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implying that, as the value of NPLs and CLA increases respectively, concomitantly, the level

of profitability for commercial banks decreases. Conversely, CAR (0.325) displays a positive

correlation with ROE, which means that as the value of CAR increases, commercial bank

profitability rises with it. Though having a value of (0.325), it can be argued that this

relationship is not very strong. For LNTA which is the control variable in the study, though it

shows a positive relationship with ROE, having a value of (0.097), the relationship is not

strong either.

To further test for multicollinearity, the table below which also shows the correlations of the

model variables is used. The table uses the Tolerance Value (TV) and the Variance Inflation

Factor (VIF).

4.2.3.1 Correlation Matrix of Variables

Table 4:5

Variable 1/VIF (Tolerance Value) VIF

CAR 0.664 1.507

NPL 0.778 1.269

CLA 0.689 1.452

LNTA 0.886 1.128

Sources: SPSS regression output

Kurawa, Gabba, (2010) highlight that; multicollinearity feature exists when the value of TV

is less than 0.2. From the table above, it can be witnessed that the TV ranges from 0.664 to

0.886. Therefore on the basis of the TV, it can be inferred that the problem of

multicollinearity does not exist. Additionally, the VIF which is simply the reciprocal of the

TV shows that multicollinearity exists whenever the VIF exceeds 10 Sabari,J(2010). From

the above table, VIF shows values which range from 1.128 to 1.507, thereby indicating that

the problem of multicollinearity does not exist in this study.

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4.2.3.2 Collinearity Diagnostics

Table 4:6

Collinearity Diagnosticsa

Model Dimension Eigenvalue

Condition

Index

Variance Proportions

(Constant) CAR NPL CLA LNTA

1 1 3.945 1.000 .00 .01 .01 .01 .00

2 .824 2.188 .00 .01 .61 .01 .00

3 .140 5.312 .00 .01 .12 .77 .00

4 .091 6.572 .00 .95 .24 .09 .00

5 .000 108.866 1.00 .01 .01 .12 1.00

Source: SPSS Regression Output

Eigenvalues provide an indication of how many districts dimensions are there among the

independent variables. When several eigenvalues are close to zero, the variables are highly

inter-correlated and small changes in the data values may lead to large changes in the

estimates of the coefficients. Condition index are the square roots of the ratios of the largest

eigenvalue to each successive eigenvalue. A condition index greater than 15 indicates a

possible problem and an index greater than 30 suggests a serious problem with collinearity.

Even if eigenvalues are used for checking the existence of collinearity, the best way is

conditional index. Since the conditional index values scored around 1, 2, 5 and 7, basing on

this, the researcher can say that there is no multicollinearity among independent variables.

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4.2.4 Testing for (Autocorrelation Durbin-Watson Test)

Table 4:7

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

1 .835a .698 .617 19.09188 1.851

a. Predictors: (Constant), LNTA, CAR, NPL, CLA

Source: SPSS Regression Output

The absence of autocorrelation is supported by the Durbin Watson (DW) Test. The Durbin

Watson statistic of 1.851 indicates the absence of autocorrelation hence the model fulfils the

assumptions of the Ordinary Least Squares regression. The Durbin Watson statistic of 1.851

is also greater than the R squared of 0.698 and hence it rules out the possibility of spurious

regression function.

4.2.5 Test for Normality of Residuals

Table 4:8

Source: SPSS regression output

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One of the major assumptions of linear regression analysis is that the residuals are normally

distributed, at the mean of zero and standard deviation of one .The results suggest that the

residual or the error term are normally distributed .The skewness and kurtosis are near to 0.

As can be observed from the histogram and p-p plot, it exhibits normality. Based on these

results, the residuals from this regression appear to conform to the assumption of being

normally distributed.

4.2.6 Normal P-P plot of Regression Standardised Residual

This plot also shows whether the data are normally distributed or not. The error term should

be normally distributed at the mean of 0 and standard deviation of 1, if it is to pass the

normality test. The P-P plot of the regression standardised residual is shown in table 9 below:

Table 4:9

Source: SPSS regression output

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4.2.7 Test for heteroskedasticity

Heteroskedasticity occurs when the variance of the error terms differs across all observations.

When this occurs, residuals follow a horn or lancelet shaped pattern as they either increase or

decrease with increasing X. If the model is well – fitted, there should be no pattern to the

residuals plotted against the fitted values. Visual inspection of residuals plotted against the

independent variables provides the first and crucial step for detecting the problem of

heteroskedasticity. This qualitative method of testing for heteroskedasticity, as evidenced by

the scatterplot below shows that the problem of heteroskedasticity does not exist.

Table 4:10

Source: SPSS regression output

4.2.8 Breuch-Pagan and Koenker Test for Heteroskedasticity

The Breusch-Pagan test is designed to detect any linear form of heteroskedasticity. The

Koenker test is assumed to be a more rigorous test for heteroskedasticity for small sample

sizes. Below are the results of the Breusch-Pagan and Koenker tests:

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Number of predictors (P)

4

Breusch-Pagan test for Heteroskedasticity (CHI-SQUARE df=P)

1.526

Significance level of Chi-square df=P (H0: homoskedasticity)

.8221

Koenker test for Heteroskedasticity (CHI-SQUARE df=P)

1.483

Significance level of Chi-square df=P (H0:homoskedasticity)

.8297

Appendix 3

Source: SPSS regression output

The Breusch-Pagan tests tests the null hypothesis that the error variances are all equal versus

the alternative that the error variances are a multiplicative function of one or more variables.

In this test, the Breusch-Pagan has a chi square value of 1.528 and a significance level of

0.8221. Since the p-value 0.8221 is greater than 0.05, we therefore accept the null hypothesis

of homoskedastity and conclude that there is no problem of heteroskedasticty.

Further, the Koenker test also has a chi square value of 1.483 and a significance level of

0.8297. This shows that the p-value of 0.8297 is greater than 0.05 and we therefore reject the

alternative hypothesis of heteroskedasticity as there is homoskedasticity.

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4.2.9 Model Summary

Table 4:11

Source: SPSS regression output

Explanatory Power of the Model

From the above table, it can be seen that, the explanatory power of the model was also high

as evidenced by the coefficient of determination (the %age variation in the dependent

variable being explained by the changes in the independent variables) R of 0.835. This means

that the predictors of CAR, NPL, CLA and LNTA explain 83.5% of changes in the ROE of

commercial banks, leaving only 16.5% to be explained by other variables.

R2 is the proportion of the variation in the dependent variable explained by the regression

model. Its value ranges between 0-1; furthermore, a small value indicates that the model does

not fit the data well. As the table above indicates the independent variables explained the

dependent variable by 69.8%. However, in order to minimise the risk of overestimating the

amount of variation that is explained by the model, in cases when the model covariates are

large relative to the sample, the adjusted R2

is more useful. This model produces an adjusted

R2 of 61.7%. This means that 61.7% of the fluctuations in the profitability of commercial

banks in Zimbabwe can be explained by the independent variables of CAR, NPL, CLA, and

LNTA.

The results obtained from the regression model show that there is an effect of credit risk

management on profitability on reasonable level with 61.7% possibility of CAR,NPL, CLA

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

1 .835a .698 .617 19.09188 1.851

a. Predictors: (Constant), LNTA, CAR, NPL, CLA

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and LNTA in predicting the variance in ROE of commercial banks. So, the credit risk

management strategy defines profitability level to a significant extent for commercial banks

in Zimbabwe.

4.2.10 Results and Discussion

Table 4:12 Results Discusion

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 215.878 166.727 1.295 .215

CAR 1.717 .584 .512 2.938 .010

NPL -2.202 .761 -.463 -2.895 .011

CLA -1.176 .242 -.831 -4.859 .000

LNTA -9.851 8.592 -.173 -1.146 .270

a. Dependent Variable: ROE

From the above table, the established simple linear regression equation thus takes the form:

Relationship between CRM and Profitability

The results of the study show that credit risk management has an effect on the profitability of

commercial banks in Zimbabwe. The dependent variables of CAR, NPL, CLA and LNTA

which are proxies for credit risk management play a major role in determining commercial

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bank performance in terms of profitability whose proxy in this research, is ROE. CAR has

been observed to have a positive influence on the levels of commercial bank profitability in

Zimbabwe as evidenced by its positive coefficient of (1.72). This implies that a unit increase

in CAR will cause the profitability of Zimbabwean commercial banks to also increase by

1.72 units. This is consistent with the findings of other scholars such as Kurawa, Garbi,

(2014) in Nigeria; Li, Zou (2013) in Europe, although their research concluded that the

relationship between CAR and ROE was not significant. However, this result runs counter to

the findings of Tefera (2011) in Ethiopia whose study showed that there exists a negative

relationship between CAR and profitability of commercial banks in that country.

However, as is the theoretical expectations, the relationship between NPLs and commercial

bank profitability is negative and is strong as shown by the coefficient of (-2.02) This means

that each unit increase in NPL would stir a decline in the profitability of commercial banks by

2.02 units. Similarly, CLA exhibits a negative relationship with commercial bank

profitability, implying that a unit increase in the levels of profitability by 1.17 units. This is in

line with the findings of other researchers such as Kurawa and Garbi in Nigeria.

Overall, the research satisfies the primary objective of ascertaining the relationship between

credit risk management and the profitability of commercial banks in Zimbabwe. These

findings are also supported by those of other scholars such as Afriyie (2012) in Ghana and

Haneef et al. (2012) in Pakistan. However, in Li and Zou(2013) failed to found an

inconclusive relationship between ROE, CAR and NPL in a study of 46 commercial banks in

Europe. However, the time horizon of their study includes the period runningconcurrently

with the Global Financial Crisis, and this could explain the inconclusive results of their study,

as other risks and economic shocks could have influenced commercial bank profitability in

that region. However, the findings of this study also run contrary to those of Khithinji (2010)

in Kenya whose research concluded that there exists no relationship between credit risk

management and profitability.

Applicability of Results to Zimbabwe

The results of this study also mirror the present situation in Zimbabwe`s banking sector at

present. There has been an upward trend in the level of non-performing loans in Zimbabwe`s

banking sector. For instance, in 2012 total NPLs stood at 13.5%, rising to 15.92% in 2013 up

to December 2014 when the figure averaged at 16% after factoring out closed banking

institutions RBZ, (2015). Concurrently, loan impairments have also been edging higher, for

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example, growing from US$38,8 million in 2012 to US$65,9 million in 2013, while profits

for the sector slumped by 32% to US$93,1 million in that period CASE Handbook, (2014).

MMC, (2015) notes that in 2014, the banking sector after-tax profit dipped 17% to US$78,69

million amid rising impairments. This was also evidenced by the number of banks that posted

losses in this period. Whereas three banks (NMB, MetBank and POSB) recorded losses in

2013, this number rose to five, with ZB Bank, BancABC, FBC Bank and MetBank also

recording losses on the back of spiralling impairment charges. Effectively, there exists a

relationship between credit risk management and profitability of commercial banks in

Zimbabwe. Sound and improved credit risk management techniques and policies will

invariably lead to increased profitability of commercial banks.

4.3 Reliability of the Results

Bryman, Bell (20 refer to reliability is “the consistency of a measure of a concept”. One of

the important factors when considering whether a measure is reliable is stability. It focuses on

the stability of measure over time; therefore the results from that measure will have little

variation (Bryman, Bell, (2011). In our research, the data we use is numerical and objectively

collected. Thus this study is stable because it would be impossible to get different results

from identical research in any other times. In addition, to maintain the accuracy of this, the

test must be conducted in the consistent method and identical source of data (Bryman, Bell

(2011). Another very similar criterion to reliability is replication which emphasizes the

capacity of replication to the research Bryman, Bell (2011).

4.4 Conclusion

The chapter presented the results of the estimated model. The results as interpreted show a

direct relationship between credit risk management and the profitability of commercial banks

and this seems to support the expected sign and stated hypothesis. Therefore the null

hypothesis was rejected and the alternative hypothesis which states that there is a correlation

between CAR, NPL, CLA and ROE of commercial banks was accepted.

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CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

5.0 Introduction

This chapter summarizes the findings as analysed in the previous chapter. The summary is

based on the objectives of the study and research questions. The purpose of the study was to

study the relationship between credit risk management and the profitability of commercial

banks in Zimbabwe between the periods 2009-2014. Suggestions for further academic

research on this area will also be given in this section. Accordingly, the chapter will begin

with the highlighting the empirical findings and the discussion of the results.

Recommendations will then be offered.

5.1 Model Results Revisited

The model was found to be statistically significant at the 5% level of significance. The data

was tested for multicollinearity and the tests proved that the problem of multicollinearity did

not exist. There was no correlation among independent variables (NPLR, CAR and CAR)

which means each of the independent variables explained the dependent variable separately.

The Durbin-Watson test was also employed, which showed that there was no autocorrelation

in the data thus the model fit with the assumptions of OLS regression and spurious regression

was also ruled out.

A test for the normality of residuals showed that the error term collapsed to 0 and that, the

error term are normally distributed at the mean of zero and standard deviation of 0.889 which

is close to 1, indicating normality. Data also showed linearity, in line with one of the

assumptions of OLS regression. The Breusch-Pagan-Koenker test for heteroskedasticity

dismissed the problem of heteroskedasticity. The regression model produced an adjusted R2

of 61.7%, which is the explanatory power of the model. This means that 61.7% of the

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fluctuations in the profitability of commercial banks in Zimbabwe can be explained by the

independent variables of CAR, NPL, CLA, and the control variable LNTA.

5.3 Conclusions

This study sought to review the relationship between credit risk management and the

profitability of commercial banks in Zimbabwe. A sample of four banks was chosen for the

purposes of the study through the use of purposive sampling technique. The selection criteria

was the non-performing loans to total advances metric of the 16 commercial banking

institutions in operation as at 31 December 2014. In order to test the relationship between the

two abstract concepts, it was necessary to employ proxies for both credit risk management

and the profitability of commercial banks. Capital Adequacy Ratio, Non-performing loans

and Cost per Loan Asset were used as proxies for credit risk management and Return on

Equity was taken to be an indicator of commercial bank profitability. Secondary data for the

sample banks was subsequently drawn from the audited financial statements and annual

reports of the respective commercial banks. Two hypotheses were made in this study and a

single regression analysis model was constructed to test the relationship for the 5 year period.

The statistics package SPSS was then used to analytically test the relationship between the

variables using the ordinary least squares regression method.

Positive Relationship between CAR and ROE

Firstly, our results showed that there is a positive relationship between CAR and ROE,

implying that an increase in CAR would in turn lead to a rise in the level of profitability of

commercial banks. A unit increase in the CAR would cause profits to rise by 1.72 units. This

direct relationship found is consistent with the findings of other scholars such as Li, Zou

(2013). However, it is increasing to note that at a time when Zimbabwean commercial banks

have been increasing their capital levels in order to comply with the new gazetted minimum

capital requirements, on average, the profitability of the whole banking sector has been

declining since 2009 as discussed in the previous chapter. This may point to the effect that

though the relationship between CAR and profitability is positive, it may be a weak indicator

of the link between credit risk management and profitability in Zimbabwe. Other proxies

such as NPLs may provide a statistically sound explanation of this link.

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Inverse Relationship between NPLs, CLA and ROE

Secondly, this research also indicated that there exists an inverse relationship between the

levels of NPLs, CLA and the profitability of commercial banks in Zimbabwe. This study

established that each unit increase in NPLs and CLA would in turn reduce the profitability of

commercial banks by 2.02 and 1.17 units respectively. The higher the NPL rate, the higher

the impairment charges which in turn eat into the core capital of the banks, leaving less

capital available to invest into other growth initiatives. On the other hand, the higher the

CLA, indicates inefficiencies by banks in administering the loans to customers, which then

lead to higher operating expenses and this may negatively affect their net interest margins as

well as total income and revenues.

Positive Relationship between CRM and Commercial Bank Profitability

The study concluded that there exists an individual and uniting relationship between credit

risk management and the profitability of commercial banks in Zimbabwe. Therefore, we

reject the null hypothesis that there exists no correlation between credit risk management and

profitability of commercial banks in Zimbabwe and thus accept the alternative hypothesis that

there is a correlation between credit risk management and the profitability of commercial

banks in Zimbabwe. In effect, the relationship is direct, signifying that good credit risk

management leads to increased profitability, and the opposite also holds. On the whole, it

was established that the better the credit management practices employed by commercial

banks, the higher their profitability, and conversely, the weaker the credit risk management

practises, the diminished will be the profitability of commercial banks. The results of this

research were also found to be statistically significant and are consistent with the present

realities in Zimbabwe`s banking sector where rising non-performing loans since 2009 , have

led to a cumulative decline in the profitability of commercial banks, largely as a result of

growing loan impairments.

Combined with the findings from the three proxies (CAR, NPLR and CLA) for credit risk

management, we conclude that there is a positive relationship between credit risk

management and profitability of commercial banks. That is to say, the better the credit risk

management is, the higher the profitability of commercial bank is. This is also in line with the

work of Al-Khouri (2011) and Kurawa; Garbi (2014) who affirmed that CRM indicators

affect profitability of bank, but also contradicts the study of Khithinji (2010) whose research

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did not find any correlation between the variables, implying that factors other than credit risk

management indicators explain the profitability of commercial banks.

Major Cause of NPLs in Zimbabwe

In addressing the secondary research objectives, this study also established that the major

cause of non-performing loans in Zimbabwe was ineffective credit risk management policies

of commercial banks, followed by the effects of a poorly performing economy due to the fact

that there was lower aggregate demand in the economy such that borrowers, mainly

corporates were now finding it difficult to repay loans and then to some degree, imprudent

lending practices by banks. Furthermore, it was also established that the major effects of high

NPLs was the reduction of profitability as a result of increased impairments and write-offs.

Measures of Reducing Credit Risk in Zimbabwe

In order to effectively reduce credit risk in Zimbabwe, this study found out that the

establishment of long term relationships with borrowers such that there is a clear

understanding of the attitudes, credit history, and capacity of the borrower to repay the loans

was found to be the most effective method of lowering credit risk. As far as the recovery of

NPLs is concerned, the study established that the most effective method to recover these

delinquent loans was to extend or lower interest rates, fees and payments to struggling

customers.

5.4 Implications and Recommendations of the Study

Generally since the introduction of the multi-currency regime in 2009, Zimbabwe`s banking

sector has been beset by high NPLs, indicating poor credit risk management. There have been

a couple of exceptions to this problem however, with Barclays Bank being the most notable

one. When the multi-currency era was introduced there was an influx of borrowers in the

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market as businesses sought to retool and recapitalise their operations and as households

sought to supplement their disposable income after the economic hardships of the pre-

dollarisation era.

This brought with it a major problem of adverse selection wherein, those borrowers who were

the least likely to repay were the ones who were actively borrowing in the market. Even as

the interest rates continued to rise as a result of economic growth losing steam, this set of

borrowers continues to accumulate more debt. Further, as a result of the absence of a credit

reference bureau in the market, these bad borrowers could borrow from one bank to the next,

as there was no information sharing mechanism within the banking system of the credit

history of borrowers. The result was that people were heavily borrowed to several banking

institutions concurrently and failing to meet their loan obligations. For the corporates, low

aggregate demand in the economy meant that there was weaker demand for their products

and output, leading to reduced profitability. Their situation was further compounded by the

cheap influx of imports flooding the market. Consequently, they too were unable to service

the loans, thereby contributing to the NPLs in the banking sector.

More disturbing though was the laxity in corporate governance structures as far as insider

lending was concerned. For instance, in 2014, of the total insider loans of $175,3 million, two

thirds of those loans were in the NPL category, amounting to$116.86 million CASE (2014).

All these issues point to the failure of the credit risk management mechanisms of local banks

to anticipate such credit risks and effectively contain them before they affected the whole

banking system because of the interconnectedness and contagion effects of financial markets.

The effects of poor credit risk management has been dire, causing some banking institutions

to close altogether whilst the overall profitability of the sector has been reduced. This

ultimately leaves a weaker economy as banks cannot efficiently perform their role in the

economy as financial intermediaries. As such, based on the findings of this research, the

researcher therefore presents the following recommendations which will be useful to various

stakeholders:

The consensus is that there is generally weak credit risk management policies in

Zimbabwe`s banking sector as evidenced by the spiralling NPLs. There thus must be

concerted efforts to improving the credit risk management policies in the country in

line with International Best Practice in credit risk standards. Credit risk management

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in local banking institutions would vastly improve if Zimbabwean commercial banks

comply with the Basel III Committee guidelines on bank supervision and monitoring.

Banks should have in place a comprehensive credit risk management process to

identify, measure, monitor and control credit risk and all material risks and where

appropriate, hold capital against these risks.

Credit officers and personnel in the credit departments of commercial banks must also

undergo continuous training and development programs such that they remain well

versed with trends regarding to sound credit risk management policies and techniques.

This would leave them in good stead to make good decisions when it comes to

lending money to customers.

It is imperative for commercial banks to leverage on relationship banking through

building long term, mutually beneficial relationships with their customers. This would

help in the credit process as they will have deeper insights into the attitudes, credit

history and the capacity and willingness of borrowers to repay loans, and this would

aid the screening process of borrowers.

Bank executives must also change their view of profits and overall performance of

their banks. There has to be a paradigm shift such that high margin, high rate loans are

avoided and a more long term view of profitability is adopted by bank management.

Awarding of substantial insider loans must also be avoided as this greatly

compromises the whole credit risk management framework, In Zimbabwe, as has

been previously stated in this chapter, the vast majority of these loans fall in the NPL

category and this gives rise to a significant agency problem.

As Zimbabwean banks struggle to recover loans, it is the recommendation of this

study that repayment periods interest rates and other related fees be extended or

lowered for struggling borrowers as continued hikes of rates will not increase the

chances of recovery of such loans.

The supervisory authority has to take a more stern position insofar as breaches of

sound credit risk management policies by bank agents are concerned. Regulations

criminalising imprudent lending by bankers must be enacted in order to curb bad

lending practices especially in cases of insider lending and also make the Board of

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Directors criminally liable. High prevalence of non-performing insider loans have led

to the collapse of several banks in Zimbabwe.

It is also fundamental for Zimbabwean banks to practice scientific credit risk control

(application of risk evaluation techniques), improve their efficacy in credit analysis

and loan management to secure as much as possible their assets, and minimize the

high incidence of non-performing loans and their negative effects on profitability.

It may also be prudent for higher and tertiary education institutions to include courses

on credit risk management as part of the core courses in the curriculum of Banking

and Finance programmes at both undergraduate and postgraduate level to prepare

prospective bank employees with the requisite training to combat credit risks.

Full and effective use of the recently established credit reference bureau must be

implemented such that it is a mere bureaucratic institution, but banks must be

compelled to utilise it fully before lending out money. It this is done, and lending

decisions are made basing on a borrower`s credit history, credit risk will be greatly

reduced.

Management need to be cautious in setting up a credit policy that will not negatively

affects profitability and also they need to know how credit policy affects the operation

of their banks to ensure judicious utilization of deposits and maximization of profit.

5.5 Theoretical and Practical Contribution to Knowledge

Since there has not been any real efforts to study exactly how credit risk management affects

commercial bank profitability and in so doing increase shareholder value in Zimbabwe, this

research therefore plays a crucial role in unveiling this shrouded mystery in Zimbabwe`s

banking system. Previous works carried out on credit risk management such as the study by

Njanike K, (2009) have been generalistic in nature, as they have focused solely on bank

survival rather than profitability. A bank can continue to survive without being profitable.

Another limitation of prior studies has been the temporal scope of those studies which has not

factored in the medium term effects of dollarisation and the new economic landscape banks

are operating in.

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Another practical contribution is that banks now have a deeper understanding of how credit

risk management affects their profitability, and will thus be able to make informed decisions

when it comes to their credit risk policies. Bank regulators are also assisted in understanding

whether their stipulated minimum capital adequacy ratios are effective in managing the credit

risk management of commercial banks. It also provides supervisors with an expression

whether the regulated ratio has effect on banks’ profitability. Since there is a negative

relationship between non-performing loan ratios and profitability’s indicators, supervisors

might consider have more requirements to cushion commercial banks against the non-

performing loans and so help them to operate more efficiently.

Banks also benefit from getting a clear direction on the specific steps to take in order to

mitigate credit risk in their operations as well as the most effective methods to pursue in their

debt recovery efforts. A posteriori, commercial banks are able to look at the exact causes of

the high incidents of NPLs following dollarisation of the economy, and will be equipped not

to make the same mistakes in the future.

5.6 Suggestions for Further Research

In order to gain a better understanding of the relationship between credit risk management

and the profitability of commercial banks, it is necessary for the sample size of banks being

analysed to be increased. This study only used a small sample size of four commercial banks

which are deemed to be representative of the whole banking sector. However, should time

and resources permit, the researcher recommends for the sample size to be increased.

Another suggestion is for more indicators of both credit risk management and profitability to

be included in the research model. This will increase the explanatory power of the model and

give the research model a stronger overall fit in explaining the relationship between the

variables. Future researchers can therefore enhance the accuracy of this model by including

several other variables.

Another way of improving the research would be to carry it out in a totally different

geographical area, such as other sub-Saharan Africa countries or the global banking system in

order to effectively test for the relationship. Besides, the results of the same topic in different

97

areas are not same. Thus comparing the similar research by discussing the differences among

these researches is also valuable.

This research focuses on credit risk management and profitability of commercial banks.

Further research suggestion could be move the core of credit risk management to other risks

management. For the banking industry’s development, diversified types of banks have built

to satisfy the demand of innovation in financial markets. In this study, the researcher focuses

on commercial banks while some of them are also investment banks. Further research can

focus on the risk management measurement of the investment banks. Except the credit risk

management, liquidity risk, market risk, operational risk or reputational risk can also be taken

into consideration. In addition, profitability is only one aspect of banks’ financial

performance. Exploring the other aspects of financial performance is also an interesting

expansion for this research.

5.7 Chapter Summary

This chapter provided a summary of the findings of this research. The model results were

analysed in terms of the objectives of the study as well as the research questions. Implications

of the results as well as the policy recommendations are also included in this chapter. The

theoretical contribution of this research to academics is also included, together with

suggestions for future research on the relationship between CRM and commercial bank

profitability.

98

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103

APPENDIX 2: REGRESSION OUTPUT

Descriptive Statistics

Mean Std. Deviation N

ROE 5.2625 30.84349 20

CAR 18.3990 9.20260 20

NPL 3.7168 6.48745 20

CLA 38.7702 21.79782 20

LNTA 19.1265 .54145 20

Correlations

ROE CAR NPL CLA LNTA

Pearson Correlation ROE 1.000

CAR .325 1.000

NPL -.443 -.445 1.000

CLA -.412 .467 -.286 1.000

LNTA .097 .028 -.060 -.274 1.000

Sig. (1-tailed) ROE . .081 .025 .035 .342

CAR .081 . .025 .019 .453

NPL .025 .025 . .111 .400

CLA .035 .019 .111 . .121

LNTA .342 .453 .400 .121 .

N ROE 20 20 20 20 20

CAR 20 20 20 20 20

NPL 20 20 20 20 20

CLA 20 20 20 20 20

LNTA 20 20 20 20 20

104

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

1 LNTA, CAR, NPL,

CLAa . Enter

a. All requested variables entered.

b. Dependent Variable: ROE

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 .835a .698 .617 19.09188 1.851

a. Predictors: (Constant), LNTA, CAR, NPL, CLA

b. Dependent Variable: ROE

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 12607.604 4 3151.901 8.647 .001a

Residual 5467.497 15 364.500

Total 18075.101 19

a. Predictors: (Constant), LNTA, CAR, NPL, CLA

b. Dependent Variable: ROE

105

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95% Confidence

Interval for B Correlations

Collinearity

Statistics

B

Std.

Error Beta

Lower

Bound

Upper

Bound

Zero-

order Partial Part Tolerance VIF

1 (Constant) 215.878 166.727 1.295 .215 -139.492 571.249

CAR 1.717 .584 .512 2.938 .010 .471 2.962 .325 .604 .417 .664 1.507

NPL -2.202 .761 -.463 -2.895 .011 -3.823 -.581 -.443 -.599 -.411 .788 1.269

CLA -1.176 .242 -.831 -4.859 .000 -1.692 -.660 -.412 -.782 -.690 .689 1.452

LNTA -9.851 8.592 -.173 -1.146 .270 -28.165 8.463 .097 -.284 -.163 .886 1.128

a. Dependent Variable:

ROE

Coefficient Correlationsa

Model LNTA CAR NPL CLA

1 Correlations LNTA 1.000 -.140 .091 .332

CAR -.140 1.000 .349 -.416

NPL .091 .349 1.000 .123

CLA .332 -.416 .123 1.000

Covariances LNTA 73.828 -.701 .594 .691

CAR -.701 .341 .155 -.059

NPL .594 .155 .579 .023

CLA .691 -.059 .023 .059

a. Dependent Variable: ROE

106

Model

Dimensio

n Eigenvalue Condition Index

Variance Proportions

(Constant) CAR NPL CLA LNTA

1 1 3.945 1.000 .00 .01 .01 .01 .00

2 .824 2.188 .00 .01 .61 .01 .00

3 .140 5.312 .00 .01 .12 .77 .00

4 .091 6.572 .00 .95 .24 .09 .00

5 .000 108.866 1.00 .01 .01 .12 1.00

a. Dependent Variable: ROE

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value -84.1876 32.6681 5.2625 25.75962 20

Std. Predicted Value -3.472 1.064 .000 1.000 20

Standard Error of Predicted Value 5.549 16.148 9.061 3.083 20

Adjusted Predicted Value -57.1812 31.2663 6.3509 22.34183 20

Residual -4.02378E1 28.07532 .00000 16.96358 20

Std. Residual -2.108 1.471 .000 .889 20

Stud. Residual -2.277 1.639 -.018 1.020 20

Deleted Residual -4.69506E1 36.82975 -1.08841 23.29780 20

Stud. Deleted Residual -2.719 1.747 -.028 1.097 20

Mahal. Distance .655 12.643 3.800 3.479 20

Cook's Distance .000 .559 .086 .133 20

Centered Leverage Value .034 .665 .200 .183 20

a. Dependent Variable: ROE

Charts

107

108

109

APPENDIX 3:BREUSCH-PAGAN-KOENKER TEST FOR

HETEROSKEDASTICITY

Regression SS

3.0511

Residual SS

38.1093

Total SS

41.1604

R-squared

.0741

Sample size (N)

20

Number of predictors (P)

4

Breusch-Pagan test for Heteroscedasticity (CHI-SQUARE df=P)

1.526

Significance level of Chi-square df=P (H0:homoscedasticity)

.8221

Koenker test for Heteroscedasticity (CHI-SQUARE df=P)

1.483

Significance level of Chi-square df=P (H0:homoscedasticity)

.8297

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APPENDIX 4: LIST OF FIGURES AND TABLES

Fig 2:1 Economic and financial implications of NPLs

Fig 2:2 Trends of NPLs since dollarisation in Zimbabwe

Fig 2:3 Sectoral distribution of commercial bank loans in Zimbabwe

Fig 2:4 Evaluation of geographical area covered by literature

Fig 2:5 Evaluation of analytical methods used by scholars

Fig 2:6 Evaluation of results obtained by scholars

Fig 4:1 Questionnaire response rate

Fig 4:2 Respondents work experience

Fig 4:3 Causes of NPLs in Zimbabwe

Fig 4:4 Impact of NPLs on commercial banks

Fig 4:5 Relationship between CRM and NPL

Fig 4:6 Credit risk evaluation and assessment tools

Fig 4:7 Effectiveness of CRM policies

Fig 4:8 Effective policy of reducing credit risk

Fig 4:9 Effectiveness of a credit reference bureau in Zimbabwe

Fig 4:10 Loan recovery strategies

Fig 4:11 Effects of compliance with Basel III committee guidelines

Tables

Table 3:1 Study Population

111

Table 3:2 Study Sample

Table 3:3 Summary of Variables

Table 4:1 Variables entered/removed

Table 4:2 Analyses Of Variance

Table 4:3 Descriptive statistics

Table 4:4 Correlation matrix of variables

Table 4:5 Variance inflation factor

Table 4:6 Collinearity diagnostics

Table 4:7 Autocorrelation test ( Durbin-Watson test)

Table 4:8 Test for normality of residuals

Table 4:9 P-P plot for regression standardised residuals

Table 4:10 Scatterplot

Table 4:11 Model Summary

Table 4:12 Regression Results

112

APPENDIX 5: QUESTIONNAIRE

Dear Sir /Madam

Re: Request for Contribution to an Academic Research

My name is Perry T Munzwembiri a final year student studying towards a Bachelor of

Commerce Honours Degree in Finance with the National University of Science and

Technology (NUST). As a requirement for the fulfilment of the Honours Degree, I am

undertaking a study on the relationship between credit risk management and profitability of

commercial banks in Zimbabwe. May you kindly spare a few minutes of your busy schedule

to explore the consequences of credit risk management patterns in commercial banks, and

how the profitability of the same banking institutions is subsequently affected. This

questionnaire will take not more than 10 minutes to complete.

Please note that this research is strictly for academic purposes only and will be treated with

strict confidentiality. The findings of this survey will not be used for any other purpose

besides that intended for this research. For further clarifications regarding this study, please

feel free to contact the researcher on 0777122737 or email, munzwembirip@gmail.com.

Your cooperation is essential for the results of the survey to be regarded as valid and reliable.

Yours faithfully,

Perry T Munzwembiri

113

1. Please state your work experience

Less than 1 year 1-3 Years

4-10 Years More than 10 years

2. In your opinion, what has been the major cause of Non-performing loans in

Zimbabwe?

a) Imprudent lending practices by banks

b) Poor design & implementation of lender protection features

c) Poorly performing economy

d) Unwillingness by borrowers to repay loans

e) Ineffective credit risk management techniques

f) Other (please explain briefly below)

…………………………………………………………………………………………

…………………………………………………………………………………………

3. In your considered view, what is the major impact of rising non-performing loans on

the performance of Zimbabwean commercial banks?

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

…………………………………………………………………………………………………

4. Is there any relationship between credit risk management and the commercial bank

profitability?

114

Yes No

5. In your answer in 4. above is yes, please briefly explain how poor credit risk management

affects the profitability of a commercial bank?

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

…………………………………………………………............................................................................

....................................................................................................................................................................

6. Which credit risk evaluation and assessment tools do you use to mitigate credit risk

exposure?

Internal Rating CAMPARI

Stress Testing Credit Scoring Models

SWOT Analysis 5Cs

7. Please list any other techniques that are used in credit risk management process, but

not given above.

…………………………………………………………………………………………………

………………………………………………………………………………………………….

8. How would you rate the credit risk management policies of commercial banks in

Zimbabwe?

Strong Weak Average

9. Which Credit Risk Management policy do you think can be best employed by Zimbabwean

commercial banks to reduce credit risk?

a) Screening and monitoring

115

b) Credit rationing

c) Collateral requirements

d) Building long term customer relationships

e) Automated end-to-end credit management system

10. Will the creation of a national credit reference bureau assist in reducing credit risk in the

market?

To a greater extent To a lesser extent Moderately

No effect

11. What strategy or strategies would you recommend as the most effective for use by

Zimbabwean commercial banks to use in order to recover non-performing loans?

a. Debt Scoring

b. Centralised debt collection function

c. Legal Strategies (such as obtaining repossession orders or demand notices)

d. Extension or lowering of interest rates, fees & payments to struggling customers

12. What do you think needs to be done to improve credit risk management for commercial banks

in Zimbabwe?

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

13. Are there any positive effects to be realised by Zimbabwean commercial banks if they comply

with the Basel Committee guidelines on bank supervision?

Yes No Not Sure

116

14. Further, does the implementation of international credit risk standards ensure profitability of

Zimbabwean Commercial Banks?

Yes No

If yes, please explain why

……………………………………………………………………………………………………………

……………………………………………………………………………………………………………

…………………………………………………………………………………………………………..

End of Questionnaire, Thank You