SOLUSI UNIVERSITY
FACULTY OF BUSINESS
GRADUATE STUDIES-MBA
APPROVAL SHEET
Date: November, 2015
In partial fulfilment of the requirements for the degree Master in Business
Administration, this thesis entitled"The extent to which credit information systems
are used as a tool for improving loan quality at commercial banks in
Zimbabwe."has been approved by the Thesis Defense Committee with a mark
of____________.
Timely Chitate, PhD
Chair
Bongani Ngwenya, PhD
Panel Member
Barnold Baidya, PhD
Panel Member
Ivonne Ndlovu, MBA
Panel Member
Sophie Masuku, PhD
Panel Member
ACCEPTANCE SHEET
Date: November, 2015
This thesis entitled"The extent to which credit information systems are used as a tool
for improving loan quality at commercial banks in Zimbabwe."is hereby accepted in
partial fulfilment of the requirements for the degree Master in Business Administration.
Timely Chitate, PhD
Dean, Faculty of Business
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ii
Copyrights© 2015
All Rights reserved
No parts of this thesis may be reproduced or transmitted in any form or by any means,
electronic or mechanic including photocopy, recording or any information retrieval
system, without prior permission from the author.
Silence Chigariro
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ACKNOWLEDGEMENTS
This thesis has been completed through the support, collaboration and sacrifices made by
a number of individuals. First and foremost I would like to thank the Lord Almighty or
his protection and care that enabled me to complete this thesis.
I thank the East
Zimbabwe Conference and Nyahuni High school management for allowing me to carry
out this study.
My most profound gratitude goes to my lecturer and advisor Dr Timely Chitate for her
guidance during the preparation of this thesis. Her advice and feedback made this
dissertation successful. My gratitude and appreciation also goes to all the panel members
namely Dr B Baidya, Dr. S. Masuku, Ps B. Mahaso and Mr P. Dziva who made this
thesis complete.
I wish to express my gratitude to my fellow graduates and colleagues for their
unwavering support company and spirit. I am especially grateful to all my lecturers from
the business department for their support and guidance. Also I would like to express my
gratitude to Tafadzwa Taskman Gondo for the support he gave me during the writing up
of this thesis.
Special thanks to my wife Buhlebenkosi for her continual encouragement, love, patience,
understanding, and moral support that I will cherish forever. Special thanks to my
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daughter Thaboluhle Anouyaishe Chigariro for her kind unwavering support and
understanding during my long absence from home.
I would like to thank my brothers Raphael, Bright, Traver and Tinashe for their moral
and financial support that has made this thesis to become reality. Thank you, Tatenda,
Siyabonga!
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DEDICATION
This thesis is dedicated to my beautiful wife Buhlebenkosi, our lovely daughter,
Anouyaishe Thabsie, my bothers, Raphael, Bright, Traver and Tinashe, my mother
Elizabeth and my late father Joseph Chigariro.
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ABSTRACT
Title: The Extent To Which Credit Information Systems Are Used As A Tool For
Improving Loan Quality At Commercial Banks In Zimbabwe.
Name of Researcher:
Silence Chigariro
Name of Advisor:
Dr T. Chitate
Date of Completion:
November 2015
Statement of the Problem
A banking system that is vibrant and well operational is the backbone of any economy as
it facilitates the distribution of resources which are always in scarcity. Some of the loans
advanced to clients by commercial banks have not been backed by adequate collateral
and the creditworthiness of some borrowers is not really known as some borrowers have
multiple loans from multiple lenders, which some fail to service. Information asymmetry
has resulted in the deterioration of the asset qualities of most commercial banks as most
borrowers exercise gross indiscipline and over indebtedness. Therefore this study
attempts to investigate the extent to which commercial banks in Zimbabwe use credit
information systems as a tool to improve loan quality at commercial banks in Zimbabwe.
Methodology
A descriptive quantitative research method was used by the researcher so as to
understand the respondents’ views as well as to seek new insights on the subject topic of
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the extent to which credit information systems are used as a tool for improving the loan
quality of Commercial Banks.
Statistics include variance and standard deviation, averages and correlations. The
research design enabled the researcher to capture quantitative data to provide in depth
information on respondents’ perceptions on the extent to which credit information
systems are used as a tool for improving loan quality. The research instrument was
administered on nine (9) banks with two (2) respondents per bank making a total sample
of eighteen (18) respondents in Harare and Chitungwiza.
Findings
The findings of the study were as follows:
1. Statistics shows that credit Information systems were often used in banks to help
improve loan quality as shown an average mean of 4.2778 and an average
standard deviation of 0.23570
2. The findings showed that credit approval authority is used every time by
commercial banks in trying to improve loan quality. This was supported by an
average mean of 4.0238 and an average standard deviation of 0.19752.
3. Responses were homogeneous in that commercial banks in Zimbabwe almost
every time use risk pricing for authorising loans to improve loan quality as
supported by an average mean of 3.8333 and an average standard deviation of
0.26813.
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4. On portfolio management use as a credit Information System, the responses
homogeneously stated that commercial banks in Zimbabwe are to a higher extent
using the credit Information System in improving loan quality as supported by an
average mean of 4.0444 and an average standard deviation of 0.14642.
5. The responses homogeneously asserted that commercial banks in Zimbabwe use
Private Credit register almost always as shown by an average mean of 3.7037
and an average standard deviation of 0.18573
6. Research findings presented that credit approval authority, risk pricing, portfolio
management and public credit register have no significant impact on loan quality.
Table 4.7 shows an adjusted R square value of 0.201 which means that the private
credit registers have a 20.1% effect on loan quality. The positive R value of 0.498
which is the correlation value shows that there is a good relationship between
private credit register use and loan quality. This means that the more we use the
information from private credit registers the better our loan quality improves.
Conclusion
The study revealed that commercial Banks in Zimbabwe are using credit information
systems which include Credit approval authority, risk pricing, portfolio management
and private credit registers. Furthermore, the study also presented that the commercial
banks are not using any public credit registry.
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In addition, the study further revealed that of the credit information systems used by
commercial banks highlighted in this research study, only private credit registers have
a significant effect of 20.1% on loan quality. This could be attributed to the fact that
private credit registers are external sources of credit information which may end up
increasing their reliability in vetting as compared to the credit approval authority,
portfolio management and risk prising.
Recommendations
Based on the research findings, the researcher recommends the following:
1. Commercial bank management should invest more in the use of private credit
registers since it has a significant effect on loan quality.
2. Management at commercial bank should recommend and assist in the formation
of a public credit registry by the Reserve Bank of Zimbabwe so as to reduce
effects of information asymmetry.
3. Management of commercial banks should not have few big clients constituting
the majority of loan balance in their books as this can be very fatal in case of
those clients defaulting or liquidating. They should spread the loans to different
clients in different types of industries thereby safeguarding their investments in
the event of one industry facing challenges.
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Recommendations for Further Study
In future study should be carried on the following:
1. An analysis of the extent to which credit information systems are used as a tool
for improving loan quality by commercial banks in Zimbabwe.
2. Alternative strategies to guard banks against credit risk in Zimbabwe.
3. Determinants of credit risk in the Zimbabwean banking industry.
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TABLE OF CONTENTS
DEDICATION.............................................................................................................................. vi
ABSTRACT ................................................................................................................................. vii
Recommendations for Further Study ........................................................................................ xi
TABLE OF CONTENTS ........................................................................................................... xii
LIST OF TABLES ..................................................................................................................... xiv
LIST OF APPENDICES .......................................................................................................... xvii
Chapter 1: INTRODUCTION ..................................................................................................... 1
Background of the Study ................................................................................................................ 1
Statement of the Problem ................................................................................................................ 6
Purpose of the Study ....................................................................................................................... 7
Research Questions ......................................................................................................................... 7
Research Hypothesis ....................................................................................................................... 8
Figure 1: Conceptual Framework ................................................................................................... 8
Significance of the Study ................................................................................................................ 9
Limitations of the Study.................................................................................................................. 9
Delimitations of Study .................................................................................................................. 10
Definition of Terms....................................................................................................................... 10
List of Acronyms .......................................................................................................................... 11
Organization of the Study ............................................................................................................. 12
Chapter 2 :LITERATURE REVIEW ....................................................................................... 13
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Introduction ................................................................................................................................... 13
Overview of Credit Information Systems ..................................................................................... 13
Credit Approval Authority ............................................................................................................ 17
Risk Pricing................................................................................................................................... 17
Portfolio Management .................................................................................................................. 18
Private Registries .......................................................................................................................... 19
The Process of Credit Scoring ...................................................................................................... 21
Regulations Governing Credit Bureaus ........................................................................................ 28
Non-Performing Loans in Zimbabwe ........................................................................................... 33
Challenges Faced In Developing Credit Reports .......................................................................... 36
Chapter 3: RESEARCH METHODOLOGY........................................................................... 39
Introduction ................................................................................................................................... 39
Research Design............................................................................................................................ 39
Research Population...................................................................................................................... 40
Research Sample ........................................................................................................................... 41
Instrumentation ............................................................................................................................. 41
Chapter 4: PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA ........... 44
Introduction ................................................................................................................................... 44
CHAPTER 5: SUMMARY, CONCLUSION AND RECOMMENDATIONS...................... 58
Introduction ................................................................................................................................... 58
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Summary ....................................................................................................................................... 58
Recommendations for Further Study ............................................................................................ 62
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LIST OF TABLES
Table 1: The Effect of Credit Bureaus on the SME Market in Latin America...............28
Table 2: Effects of Non-Performing Loans.....................................................................33
Table 3: Trends in the Banking Sector............................................................................34
Table 4: Zimbabwean Bank NPL’s as at June 2014............................... ........................35
Table 5: Zimbabwean NPL Trends as at June 2015........................................................36
Table 6: Population and Sample Representation.............................................................40
Table 7: Table of Verbal Interpretation..........................................................................42
Table 8: Reliability Statistics..........................................................................................42
Table 9: Loan Quality Measure......................................................................................45
Table 10: Credit Approval Authority.............................................................................47
Table 11: Risk Pricing.....................................................................................................49
Table 12: Portfolio management.....................................................................................51
Table 13: Private Credit Registers..................................................................................52
Table 14: Model Summary..............................................................................................54
Table 15 : Anova Table....................................................................................................55
Table 16: Coefficients......................................................................................................56
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LIST OF FIGURE
Figure 1: Conceptual Framework…………………….………………………………8
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LIST OF APPENDICES
Appendix A: Introductory Letter..............................................................................72
Appendix B: Questionnaire........................................................................................73
Appendix C: Curriculum Vitae………………….…………….....…………….…...78
Appendix D: SPSS Results………..…………………………………...….....……...80
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1
CHAPTER 1
INTRODUCTION
Triggered by the rise in nonperforming loans and defaulting by borrowers, this study
seeks to investigate and discover whether effective credit information systems can help in
improving the quality of loans and reduce default risk. This chapter gives a background
to the study outlining the problem area behind the evaluation as driven by the research
question and set objectives. It sets out the significance of the study, delimitations of the
study, basic assumptions as well as limitations of the study and finally it gives the
definition of terms.
Background of the Study
The credit system is an integral function in any economy as it sustains the entire money
creation process and thus economic development. Banks as financial intermediaries
facilitate the transfer of funds from deficit to surplus spending units through among other
functions, the advancement of loans. However, a poor credit system can be detrimental to
an economy as can be evidenced by the global financial crisis of 2007. This crisis
popularly termed the sub-prime mortgage crisis due to its origins was a result of generally
bad lending practices by American banks. As the name suggests the loans were ‘subprime’ meaning they were issued to undeserving borrowers whose creditworthiness was
questionable.
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A shareholder suit against one subprime lender Fremont General Corporation claims that
it marketed adjustable rate mortgages to subprime borrowers in an unsafe and unsound
manner and without adequately considering the borrower’s ability to repay,
Schwarcz(2008). The consequences of subprime lending was deterioration in the asset
quality of banks as a result of high levels of defaulting by borrowers, the assets were thus
termed ‘toxic assets’.
According to Staten et al (2000) the United States of America of any country in the world
keeps the largest complete credit history on the adult population. Its credit bureau data on
borrowers has become the cornerstone of the underwriting decisions for consumer loans.
Lenders use credit scoring models to approximate a clients’ credit risk with remarkable
accurateness and this has helped in progressing the efficiency on their credit markets
upwards bringing consumers lower prices and equitable treatment.
Barron (2000) states that when lenders cannot distinguish good borrowers from bad
borrowers all borrowers are charged an average interest rate that reflect their pooled
experience. Therefore, the presence of credit information leads to fair and lower prices
being charged. Credit systems information has also availed a wider variety of credit
products to billions of family units which would have been denied as risky a generation
ago.
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Data sharing and liberated flow of information has been instrumental to the United States
of Americas economy’s flexibility and resilience. The Federal Reserve Bank’s president
said in a speech that the portability of information makes the United States open to
change. The accessibility of objective date results in the reduction of risk connected with
the dissolution of old partnerships and beginning of fresh ones because the information
assists in setting up new working relationships much faster. The accessibility of credit
information sharing has been the push behind the growth rates experienced by the
financial market in the United States of America.
Countries like the Philippines and Vietnam are slowly incorporating the use of credit
bureaus in their credit systems. However HURI and DIR (2008) observed that they are
facing challenges such as inadequate data capture and the legal regulatory structure to
effectively monitor the activities of credit bureaus. For SME data, availability of hard
information is limited, for example financial statement data. In instances, where it is
available, it tends to be inaccurate because in most cases the management of SMEs do not
understand the accounting standards based on which financial data should be compiled.
This is partly because the financial standards themselves are imperfect.
The South African credit reporting system is well developed and advanced, Turner et al
(2008). Credit bureaus in South Africa include, TransUnion ITC, Experian, Compuscan,
XDS and KreditInform. The capacity for and use of information analytics is very
advanced in South Africa and banks have the skills and capacity to use this information.
3
Banks in South Africa have used credit scores for more than a decade and both
TransUnion and KreditInform have developed and marketed several business credit
scores. The availability of information has resulted in the reduction of the costs of
lending as it lowers the cost of assessing risk. However, a weakness that Turner observed
in South Africa’s reporting system is the unavailability of collateral information of
borrower. Borrowers in this case can put up the same collateral for multiple loans.
The credit market in Zimbabwe is fast becoming more complex to manage risk
effectively. The lack of information on the behaviour and exposure of clients increases
the risk of financial losses. According to Page Properties website on centralised credit
database in Zimbabwe, they reported that the continued existence of a lenders and real
estate agents depended on their capability to pull together and process data effectively
and efficiently in vetting clients and in evaluating and monitoring their performance.
They reported that the Chairman of the Estate Agents Council, Mr Oswald Nyakunika
reportedly said that the non-payment rate by property buyers is likely to keep on rising
and this will have depressing impact on the values of property overtime. He reiterated
that properties that were being attached due to failure in debt honouring were being
auctioned at less than their market value and this presented a large loss property
managers and owners alike in the sector.
According to the RBZ report (2011), the banking industry in Zimbabwe has had its fair
share of misfortunes in lending. In 2010 there were strong calls by fiscal and monetary
4
authorities that banks were reluctant to extend loans, banks thus complied and went on
what can be called a ‘lending overdrive’. The report states that loans increased from
US$1, 81 billion in January 2011 to US$1, 88 billion in February 2011, while deposits
reached US$2, 36 billion in January, increasing to US$2,4 billion in February
representing a loan deposit ratio of 76 per-cent.
Zimbabwe Ministry of finance (2011) report further highlights that a number of banks
however were advancing loans without demanding adequate security and banks ran the
risk of loaning to people and organizations buckling under credit from other lenders.
Treasury statistics released in July 2011 showed that of the $2 billion dollars that was
disbursed in the first half of the year 37% were non-performing loans.
According to the Financial gazette of 7th of august 2014, THE Reserve Bank of
Zimbabwe (RBZ)governor, John Mangudya reported at the Confederation of Zimbabwe
Industries (CZI) annual congress held in Mutare in July thatthey are working on a
national credit bureau to minimize bad debts in the banking sector. He said the bureau
would enhance the verification process of borrowers, enabling bankers to assess credit
risk and reduce the level of non-performing loans (NPLs) in the banking sector.
Speaking at the same function, Bankers association of Zimbabwe (BAZ) president, Sam
Malaba, said government was making progress in addressing banking sector
vulnerabilities. He further stated thatthe Bankers Association of Zimbabwe estimates
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non-performing loans (NPLs) to be at about 25 percent of total deposits hence the need
for a credit bureau.
The same report stated that Zimbabwe’s financial sector would also change dramatically
once the National Credit Bureau becomes operational, as absence of such a critical
facility has created information asymmetry in credit vetting. Information asymmetries
arise as a result of the absence of credit information systems in Zimbabwe, borrowers are
able to exploit this weakness and thus borrow against the same cash flow from various
banks. A situation where firms and individuals are over borrowed in Zimbabwe is very
common and thus has contributed to the current trend of over indebtedness and cases of
untenable rates of loan default in the financial sector. This study therefore seeks to
establish the significance of credit information systems in improving Commercial Banks
in Zimbabwe’s loan quality.
Statement of the Problem
A banking system that is vibrant and well operational is the backbone of any economy as
it facilitates the distribution of resources which are always in scarcity. This system once
there are disturbances, leads to losses that may be tragic to the wellbeing of the economy.
Some of the loans advanced have not been backed by adequate security and the
creditworthiness of some borrowers is not really known as some borrowers have multiple
loans from multiple lenders, which some cannot service. These information asymmetries
have resulted in the deterioration of the asset qualities of most commercial banks as most
borrowers exercise gross indiscipline and over indebtedness. Therefore this study
6
attempts to investigate the extent to which commercial banks in Zimbabwe use credit
information systems as a credit risk management strategy.
Purpose of the Study
The primary objective of this research is to explore the significance of credit information
systems as a tool for improving commercial bank loan quality. The research also seeks to
achieve the following objectives:

To establish the use of credit information systems as a credit risk management
strategy by Zimbabwean Commercial Banks.

To evaluate the extent to which credit information systems are employed by
Commercial Banks in Zimbabwe.
Research Questions
In order to explore credit information systems as a tool for improved loan quality, it was
important to come up with questions that could be answered during the research which
are as follows:
1) To what extent is the use of credit information system in commercial banks
influencing non performing loans in Zimbabwe.
2) To what extent are Commercial Banks in Zimbabwe using the following credit
information systems in managing credit risk?
1. Credit approval authority
2. Risk pricing
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3. Portfolio management
4. Private Credit Registers
3) To what degree has Commercial Banks in Zimbabwe benefited from using
information systems in improving loan quality?
Research Hypothesis
Ho1 Commercial Banks in Zimbabwe do not use credit information systems and there are
no benefits derived from the use of the aforementioned systems.
Figure 1: Conceptual Framework
Independent Variables
Dependant Variable
Credit Information Systems

Credit approval authority

Risk pricing

Portfolio management

Loan Quality

levels of Non-Performing
Loans
Private Credit Registers
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Significance of the Study
The nature of lending entails a lot of risk and thus lenders should ensure stringent
measures are taken to avoid incurring losses. The research seeks to investigate whether
the quality of loans can be improved in any way if credit information systems are
employed as a credit risk management tool. This research will be helpful to:

Commercial Banks in Zimbabwe can further improve loan quality.

Borrowers will be able to know that they need to keep a good clean credit.

Financial institutions who will be able to know the makeup of Zimbabwean credit
business and thereby help facilitate growth.

Government will be able to see how policies affect interest rates thereby affecting
lender and borrower relationships.

Investors in Zimbabwe will make informed decisions when looking for credit.
Limitations of the Study
As is the case with most researches, conditions are not always ideal with some limitations
present. The following are some of the limitations that may restrict and affect the
researcher in this research:

The success of this study is dependent on the availability of reliable and accurate
information from the respondents.

Some commercial banks in Zimbabwe are reluctant to give information and
assistance that is pertinent to the success and meaningful execution of the study.
9

Financial constraints will be a major problem as the researcher will be funding
himself to go to Harare and get the information and this may have an impact on
sample size.

Time constraints may also hinder the researcher from acquiring all the
information required.
Delimitations of Study
The study solely focuses on all Commercial Banks in Zimbabwe branches operating in
Harare and Chitungwiza, with the targeted population being mainly credit personnel and
branch managers, while the research is confined to Harare due to proximity and
budgetary constraints of the researcher.
Definition of Terms
Non-performing Loans:
These are financial assets from which banks no
longer receive interest or instalment payments as
scheduled.
Loan Quality:
It is an evaluation of bank loans (asset) to ascertain
credit risk associated with it.
Credit Information System:
It is a system that collects data that describes credit,
processes the data and makes credit information
available to credit managers to help them make
decisions
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Sub-prime:
Below standard
Private Credit Bureau:
A private firm that maintains a database on the
creditworthiness of borrowers in the financial
system and facilitates the exchange of credit
information among banks and financial institutions.
Default:
Failure by borrower to meet financial obligation
GDP:
An aggregate measure of production equal to the
sum of the gross values added of all resident
institutional units engaged in production (plus any
taxes, and minus any subsidies, on products not
included in the value of their outputs).
List of Acronyms
OECD : Organisation for Economic Cooperation And Development
PCR: Private Credit Registry
NPL: Non Performing Loans
GDP: Gross Domestic Product
HURI: Hachinobe University Research Institute
DIR: Daiwa Institute of Research
SME: Small and Medium Enterprise
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Organization of the Study
This section will provide an overview of how the five (5) chapters will be organized.
Chapter One: The first chapter is introduction which includes background of the study,
statement of the problem, objective of the study, significance of the study, scope of the
study, definition of terms and finally the plan of the study.
Chapter Two: This is a review of relevant related literature to the topic. This has to do
with information on what has already studied by other authorities as far as the subject of
study is concerned.
Chapter Three: This chapter provides the research methodology, population,
instrumentation and statistical procedure to be used to analyze data collected.
Chapter Four: This consists of the data collection, presentation and analysis of research
findings, based on research questions and the findings will be linked to literature review.
Chapter Five: This comprises of the summary, conclusion and recommendations based
on the results of the study.
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CHAPTER 2
LITERATURE REVIEW
Introduction
This chapter reviews literature and gives us an overview of credit information systems.
The chapter further highlights the different credit reporting systems, the roles of
information sharing, the benefits derived from employing credit information systems and
the regulation governing these systems. The chapter concludes by giving empirical
literature of credit reporting systems in other countries.
Overview of Credit Information Systems
Biggar and Heimler (2005) stated that in developing countries, competition between
lenders may be made worse because data on the worthiness of prospective clients is not
readily available. This is because without a correct supply of data on clients’ credit merit,
each individual lender carries an informational advantage over any other lender on the
credit worthiness of its clients. New institutions will be very unwilling to loan to clients
of other lenders, if they are not readily knowledgeable on the amount of exposure of each
prospective client.
They further highlighted that an economical financial market, where lenders contend for
clients and prospective customers choose between different lenders as providers of credit,
can only develop if lenders are fully knowledgeable on the maximum credit exposure of
13
each possible client. Otherwise, if data is in the hands of a few players held privately, the
credit market will be divided and lenders will deal only with clients they know
personally.
Robert T. Clair, Senior Economist and Policy Advisor of the Federal Reserve Bank of
Dallas in his paper on Loan Growth and Loan Quality noted that, a bank seeking to
increase its market share might lower its loan requirements standards to attract more loan
customers. The loan requirements standards are exemplified in the non-financial terms of
a loan, including collateral requirements, personal guarantees of borrowers, and loan
covenants.
He further highlighted that if a bank drops non-financial terms to attract new loan
customers, then it is increasing the exposure to risk of the bank by lowering loan quality.
Even if a bank attempts to maintain the same credit standards, the new borrowers it
attracts may be of lower quality as a result of adverse selection.
One of the greatest developments in retail banking has been the use of credit reports in
assessing loan applications. In most developed countries loan approvals no longer take
days but rather are granted in minutes because of the availability of information sourced
from credit reports, Brown and Zehdner (2007). Credit information systems support the
major activities of a credit organization. The information systems collect data that
describe credit operations, process those data and make credit information available to
14
credit managers to help them make decisions. Credit information systems can include a
number of functions including collecting, analysing and distributing information about
how consumers and businesses, large and small, handle their credit obligations.
Straten (2008) states that the extent and intensity of information that is in the credit
reports formed by a system of full file reporting have helped the improvement of
complicated statistical scoring tools. It has been noted that countries with strong credit
scoring systems, credit resolutions are progressively more based on accurate data
concerning a borrower’s own past payment record. Furthermore, Credit scoring has
helped in the evaluation of character and capacity to repay the loans basing on prior
behaviour that has been documented over time as compared to the traditional over the
counter face to face grading of clients’. This has improved the speed and accuracy by
which lenders award lending decisions.
Well-functioning credit systems require the existence of formal information exchange
mechanisms, Miller and Mylenko (2003). Information from credit reference agencies
help in two main purposes: a) Improved credit management by monetary establishments
and reducing loan non-payment rates; b) diminishing asymmetries of information and
allowing financial institutions to boost lending capacity and supply credit to customer
categories. The OECD article on information sharing acknowledges that there is plenty of
evidence that has been provided that supports the fact that institutions involved in credit
15
information sharing has a constructively positive bearing on credit offering to the private
sector.
However, Pagano and Japelli (1993) differ in opinion as they highlight that the effect of
information sharing on lending volumes can be ambiguous. They point out that it is not
always the case that the increase in lending to safe borrowers compensates the decrease in
lending to risky types. Worldwide, the credit reporting industry has seen significant
developments, Djankov et al (2007) as cited in Brown and Zehdner (2007), show there
was an increase in the number of countries with a public credit registry from 21% in 1978
to 53% in 2003.
Below are some of the credit assessment systems that banks have in place in order to
assess the credit worthiness and the risk associated with a client before issuance of a loan.
The credit committee of a financial institution or other credit institutions are the ones that
scrutinize, grant or reject loan applications that ordinary credit officers do not have the power
to process.
To start with, the team makes sure that the credit request meets lending benchmark set by the
institution. If it does meet this benchmark, the team can therefore agree to disburse the credit
applied for with set regulations to be met by the client. This team is responsible for periodic
reviews on all loans that mature. They are also mandated with ensuring that loans that are
past due date be collected. This committee is composed usually of officers in the middle
management level of the firm who have managerial authority.
16
Credit Approval Authority
Dorbec (2006) states that the credit approval committee of any lending institution is the one
with the purpose of scrutinising and eventually approving or rejects any credit submission
which the lower level credit officers have the power to grant. He states that the team ensures
that the credit application basically satisfies the set benchmark depending on the nature of
collateral security required or checking on any supporting documentation that may be
required to see if all is in place.
In the event that the credit application satisfies these conditions, the lending authority may
decide to roll-out the loan with basic terms and conditions to be met by the client. They are
also mandated with ensuring that loans that are past due date be collected. This committee is
composed usually of officers in the middle management level of the firm who have
managerial authority. Credit approval authority helps the bank in reducing the loan default
rate by narrowing down the clientele and creating a quality loan base. Screening of clients
helps in removing possible defaulters who may not be qualified to get access to credit lines as
a result of the loan conditions. Hence, this aspect works well in helping a bank create a high
loan quality base.
Risk Pricing
Barron and Staten (2003) define risk based pricing as a way by which banks set up prices
in accordance with risk associated with the client. Clients that are risky for instance laid
off from work, declared insolvent, or are several payments behind on a mortgage, risk
17
based pricing causes client to pay higher interest rates because of the risk associated with
them. Clients are not denied a chance to access loans because they are deemed risky, but
are afforded the loan at a higher interest rate than the less risky clients.
Hence because of this risk based pricing is not encouraged by other players, and is termed
a predacious bank practice. On the other hand, risk based pricing affords clients a chance
they otherwise would not have had of accessing credit. They can have a loan but at a
much higher cost than their less risky counterparts and this practice can act as an
obstruction to borrowing.
Risky based pricing helps in determining a better loan quality by discouraging would be
borrowers from getting credit lines due to the inhibitive interest rates charges on the loans
due to the nature of risk the bank would be taking on them. The ultimate goal of any bank
is to make a profit in its operations by reducing default rates and increasing interest and
loan principal collection.
Portfolio Management
According to Cornett (2003), rolling out credit is the core business of commercial banks
and stands out to be the largest business activity of any lending institutions. Loan
portfolio therefore is by far the biggest asset and the major source of income. In the same
regard it shelves the major source of risk in loan defaults which erode the working capital
and liquidity of any lending institution. This may be due to relaxed credit regulations,
18
poor risk management tools and factors in the economy. Portfolio management matters
have always stood out as a major concern in lending institutions.
Lon portfolio management and availing of loans are fundamental to the wellbeing of any
bank as well as the safeguarding banks working capital and liquidity thereby improving
loan quality. Loan portfolio management (LPM) is the practice used by lending
institutions to manage and control inherent risk that is embedded in the loan awarding
process. This activity because of its importance and sensitivity to the wellbeing of the
bank, it is the primary function of middle managers in supervisory role. LPM assessment
entails the evaluation of managerial steps taken in controlling and identifying the credit
process risks which focuses on the problem identification tools used by management to
assess inherent credit risk.
Management prefer maintaining loan quality which is positive therefore they begin by
dealing with oversight risk that is inherent in individual loans. Therefore, this states that
if management are able to minimise inherent risks associated with the individual loans, it
would be very possible to have good loan quality hence managing loan performance
remains critically essential.
Private Registries
Japelli and pagano (2005) state that lenders in many developed countries are sharing
information on their client credit scores. This has been facilitated either voluntarily
19
though credit information systems that are opened and operated by credit providers
themselves through credit information bureaus or operated by private third party players
in the financial market or by central banks on a mandatory basis. World Bank (2006) in
their article on financial infrastructure say that the core of any country’s credit reporting
system is made up of public credit registries (PCR) and private credit bureaus (PCB).
World Bank (2006) defines a PCB as a private firm that maintains a database on the
creditworthiness of borrowers in the financial system and facilitates the exchange of
credit information among banks and financial institutions. More advanced PCBs offer
credit scoring services where borrowers are assigned credit scores on the basis of their
capacity and ability to repay debt. These scores are normally calculated using information
from credit reports, credit scores help in creating awareness among borrowers of how the
data collected by credit bureaus affect them. As a result of credit scores from PCBs
borrowers are encouraged to maintain discipline so as to have clean credit records, World
Bank (2012).
PCBs collects a larger volume of both positive and negative information from all sectors
in much greater accuracy and detail. As a result of this, PCBs develop a more
comprehensive picture of a borrower’s financial dealings.
McIntosh and Wydick (2004) cited Japelli and Pagano (2002) who found that the
presence of PCBs is associated with broader credit markets and lower credit risk. They
20
observed that PCRs are more likely to arise where there is no pre-existing PCB and there
are poorly protected credit rights. World Bank (2006) reported public credit registries to
be in operation in about eighty countries with almost half of the countries that rely on
public credit registries being low income countries in Africa. High income countries
which rely exclusively on public credit registries are less than 8%. Most middle and high
income countries rely on private credit bureaus, which have been established in about
eighty countries, or on a combination of both PCB and PCR.
Japelli and Pagano (2005) argue that the use of PCB’s may help in assisting lenders in
procuring quality clients more readily who may help in reducing the rate of default thus
improving working capital and liquidity. Borrowers with already clean records, aspire to
keep them clean while those with stained records on the other hand will be working hard
to improve their records as well.
The Process of Credit Scoring
Bolton (2009) states that Credit scoring is a mechanism used by credit registries to
quantify the risk factors relevant for a borrower’s stability and willingness to pay. Before
the introduction of formal methods of risk assessment in banking, decisions on whether
or not to grant credit were made on judgmental basis. Credit personnel would assess the
creditworthiness of an individual on the basis of personal knowledge of the applicant.
This however was not reliable, not replicable, unable to handle a large number of
applicants, too subjective and generally too risky.
21
According to Anderson (2007), the introduction of credit scoring in 1941 helped credit
assessment to improve as this method was more objective and practical. Caire et al
(2006) state that credit scoring is used worldwide though, mainly in processing small
value consumer loans and also loans to small businesses. Credit scoring is therefore an
integral part of the credit reporting system, Smith (2006). Caire et al (2006) gives the
following as benefits of credit scoring:

The efficiency of credit personnel is improved

Development of consistent evaluation processes

It reduces human bias in the decision to lend

Enables banks to vary credit policies according to classifications of risk for
example monitoring lower risk loans without on-site business inspections

Expected losses for borrowers are better quantified according to different risk
classes
Roles of Information Sharing
In principle, when lenders barter client information on credit scores, there can have four
outcomes:

The borrower’s characteristics are known by lenders thereby reducing
unfavorable selection.
22

Reduce the level of asymmetric information between lenders and borrowers.

Help in allowing lenders to instill discipline on clients as they remove them from
accessing credit.

Help in the elimination of the need to draw from multiple credit sources thereby
reducing over-indebtedness by clients (Japelli and Pagano 2005).
Information Sharing, Asymmetric Information and Improved Loan Quality
Organisation for economic cooperation and development (OECD) report gives the
following as ways information sharing can alleviate the problems arising as a result of
information asymmetry in the lending market:

Countering Unfavorable Selection: Pagano and Jappelli (1993) state that
information asymmetry reduction between lending institutions and consumers
supported by credit registries permits credit to be awarded to borrowers who may
have been discredited resulting in higher quality lending.

Countering Ethical Hazard: Padilla and Pagano (2000) stated that if institutions
are involved in credit sharing the clients cost of non-payment is increased hence
encouraging debt repayment.

Countering Information Domination: Banks that are involved in information
sharing help reduce information domination by one player in the industry on
customers. Padilla and Pagano (1997) reiterate that the relationship between
23
banks and consumers may end up allowing banks to hold monopolizing data
giving those institutions advantages over other .

Over Indebtedness Reduction. Bennardo, Pagano and Piccolo (2009) states that
when institutions share information they end up rejecting individuals who have
multiple debts thereby assisting in reducing multiple lending. This in essence help
in giving less credit scores on those highly indebted customers.
Although, information sharing can help in overcoming moral hazards on the borrower’s
part, banks may be reluctant to share information, especially where credit markets are
competitive, sharing information with close competitors is deemed unhealthy for the
bank. However, in some countries participation in private credit bureaus is dependent, on
whether a bank provides information about its clients.
As a result, reluctance in information sharing might mean that a bank also does not obtain
credit information when it requires it and therefore some information asymmetries will
still exist. Japelli and Pagano (2002) support this view as they say that lenders that
contribute data are the ones that can acquire access to two way flow of data concerning a
loan applicant. This can be done upon request of a credit report from the bureau.
World Bank (2012) gives another role of information sharing as that of supporting bank
supervision and credit risk monitoring. Credit information systems are an effective
mechanism for supervising and monitoring credit risk in banks as well as credit trends in
the economy. Since regulators require that banks set provisions such as loan loss
24
provisions, they use information from credit bureaus to assess whether the provisions are
adequate or not and also to carry out an analysis of the developments in credit markets.
Benefits of Employing Credit Information Systems
Barron and Staten (2003) realize that credit bureau data has conveyed lower prices to
consumers, more impartial handling, and a variety of credit services and products to
many of family units who may have been declined as risk. This has helped consumers to
seek better opportunities elsewhere and made it cheaper to dissolve old relations.
However, developing countries are yet to realize the full benefits of comprehensive credit
reporting as well as other developed countries.
This is because there is a diverse quantity of credit information available to institutions
for evaluating credit risk in the world today. Information that was negative especially on
bankruptcies and nonpayment used to be shared historically but now good information is
now available on the market. Straten (2008) acknowledges that availability of allinclusive data on customer credit records has considerably improved competition and
lowered the cost of borrowing by making it possible for credit providers to try to win
customers nationwide by permitting companies, save for financial organizations to
commence offering competitive financial products and services and by making it possible
for new players to rise above the advantage of recognized lenders in evaluating new
clients.
25
All-inclusive credit reporting systems have also helped in creating macroeconomic
development advantages for the host country, which includes higher resistance to
household income disruption and better mobility for both human resources and capital
investments. According to Minetti et al (2009), there is a decrease in delinquent
payments on loans thus repayment performance improves if lenders enter credit
information sharing institutions. Zehnder et al (2007) go to the extreme of saying that
there would be a collapse in the lending market in the absence of an information sharing
institution.
This is however up for debate as there are some countries without really an established
information system but still have rather functional lending markets. Their study went on
to show that by establishing credit registries borrowers are encouraged to repay their
loans as lenders would have identified borrowers with favourable payment histories.
Lenisa (2007) gives the following as some of the benefits of credit bureaus for
institutions:

Decreases loans’ losses and personal bankruptcies by providing crucial
information needed for lenders to more accurately assess the profile of an
individual borrower.

Serves as self-regulating and goal oriented catalyst in the process of availing
credit to applicants.
26

Minimize the costs of assessing risk by availing access to the complete credit
information draw together much quicker and which is accurate for decision
making.

Improves the protection of privacy of clients by providing a methodical basis for
availing credit to clients devoid of the difficulty of long supporting documents
usually required by many credit providers.

They help in the reduction of fraud by offering extra information that assist credit
givers to recognize and circumvent would-be fraudulent loan applications.

Makes it possible for lenders to have quicker access to records and information
for credit decision making.

Enable Commercial Banks branches to put forward a large range and variety of
products and services to meet a broader range of consumer desires. Hence,
consumers who have a good credit score can enjoy being availed more
competitive pricing.

Financial institutions can also experiment in new markets by extending credit
because they can lessen the risk and more accurately estimate the credit risk.

Credit bureaus help in the creation of repayment culture by clients thereby
increasing credit scores.
In graph below, McKinsey (2009) gives some effects of credit bureau on the small and
medium enterprise (SME) market.
27
Table 1: The Effect of Credit Bureaus on the SME Market in Latin America
50
40
30
20
without credit bureau
10
with credit bureau
0
% reporting
financial
constraints
Probability of
loan granting to
SME (%)
Source: McKinsey (2009)
It can be seen that where credit registries are present, financial constraints are less than
where there are no credit registries. Also the probability of loans being granted to SMEs
is greater in the presence of credit bureaus.
Regulations Governing Credit Bureaus
The regulations of credit treatment differ significantly around the world. Thus Mylenko
et al (2003) outlined the key attributes in regulation of credit treatment and its
repercussion. In some countries credit bureaus are obligated to be registered with an
information protection entity and to employ a representative in charge of fulfillment with
the information protection guidelines. When information is transferred to credit registries
for the first time, some European countries require notification of data subjects. This
28
informs the borrower about the information that will be sent to credit bureaus such as
name and intention of use of the data in the credit register.
Legislation in most countries also calls for approval of a client to sanction issuance by
credit registry of credit score report. Standard practice in application for credit is to
include, in the loan application, a statement requesting approval of a loan applicant for a
credit registry to avail a credit statement. The availing of a notice to the applicant when a
loan application is rejected is one of the most helpful instruments for preserving quality
and accurateness of data in the data bank. It should inform the credit applicant that the
decision to refuse credit was in whole or part based on the credit information obtained
from the credit registry, specifying its name.
Consumers also have the right to examine their credit statements from the credit registry
and notify the database reporting inaccuracies since the client who is subjected to the
credit test best knows if the information in the credit statement is accurate or inconsistent
with what is factual. If variance between the credit registry and the customer occur over
the legitimacy of data, the customer must be capable of adding a statement on credit
report stating the variance.
There is need to put timeframe on the period that credit record is availed to a credit
provider. All data in the record should be held in reserve for a given timeframe.
29
People often misunderstand the role of credit reports and hardly ever think about or
evaluate their credit until they need credit assistance. The credit regulator has an
important role of educating the consumers to ensure consumers are able to exercise their
basic rights and also to encourage the development of the industry. The regulator also can
request that notice of an unfavorable action generated basing on information from credit
based history also include information concerning the customers’ rights in regards to the
law.
Factor That Affect Loan Quality
FDIC (2011) explains that asset quality reflects the quantity of existing and potential
credit risk associated with the loan and investment portfolio. The evaluation of asset
quality should weigh the exposure to counterparty, issuer, or borrower default under
actual or implied contractual agreements. Prior to assigning asset quality, an important
factor to consider is the level, distribution, severity and trend of problem, delinquent and
non-performing assets for both on and off-balance sheet transactions, the diversification
and quality of the loan and investment portfolio.
Adhikary (2005) defines non-performing loans as financial assets from which banks no
longer receive interest or installment payments as scheduled. The term ‘non-performing’
is derived from the idea that the loan ceases to perform or to generate income for the
bank. Mannan et al (2005) point out that when a loan cannot be recovered within a
certain stipulated time period governed by some respective laws it becomes non30
performing. If it is used for a purpose other than what was intended for it, then it is also
non-performing. Adhikary also says that if borrowers create non performing loans
willingly, the effects might be contagious and might drive good borrowers out of the
financial markets as they would now also prolong the repayment period.
Pestora et al (2011) gives two groups of factors which influence loan quality, that is,
bank specific and macroeconomic factors. Bank specific factors included growth of
lending activities, interest rates, cost and operational efficiency and ownership structure.
Macroeconomic conditions are; GDP growth, unemployment, inflation and exchange
rates among others. Quagliariello (2007) also found the influence of the pre-crisis credit
boom on the asset quality of banks and this confirms the influence of management
strategies on the sensitivity of banks to credit risk.
Results of the research carried out by Mamonov (2011) revealed some of the factors
influencing loan quality of which the rate of credit growth was identified as one. This is
because rapid credit growth of loans is achieved by decreasing lending standards and
therefore lowering interest rates. This leads to borrower adverse selection and therefore
results in the rise of non-performing loans. Another reason is high lending rates which
increase the costs of servicing debt therefore eventually resulting in bad loans.
Mannan et al (2005) also give the following as some of the causes of non-performing
loans:
31

Reduced attention to borrowers

Increased loan size

Lack of proper credit risk management tools

Borrowers probe a credit operations weaknesses

Loans sanctioned by corruption
Other reasons might include inadequate collateral security, unethical lending practices
such as advancing excessive related party loans and lending as a result of anxiety for
income.
Effects of Non-Performing Loans
Having non-performing loans in books of Commercial banks is an unfavourable situation.
As can be seen from the diagram above the effects of these are detrimental to the
operation and survival of any commercial banking institution. Loss of revenue, erosion of
capital, high risk premium, high loan price and low rate of investment all work against
the main objective of commercial Banks, that is, to make a profit. Therefore, banks need
to come up with ways of reducing non-performing loans in the loan books. The diagram
below gives some of the effects of non-performing loans.
32
Table 2: Effects of Non-Performing Loans
Non- performing
loan
Loss of
current
revenue
High risk
premium
High
loan loss
provision
Erosion
of banks
capital
High
loan
price
Low rate of
investment
Financial
crisis
Low
economic
growth
Source:Adhikary (2005)
Non-Performing Loans in Zimbabwe
Post dollarization there has been a steady increase in the levels of nonperforming loans
(NPL’s). This has cause a lot of problems for the banking industry which had to work
extremely hard to try and save their asset value thereby improving loan quality. The
troubled and Insolvent banks policy onpage 77 to 79 states that there are categories
ofNon-performing Loans which are Watch list (nplsinexcessof10%but lessthan15%.),
33
Close
Monitoring
(thresholdisbetween15%and25%)
and
Mandatory
remedial
action(nplsabove25%).The table below show banking industry trend of non-performing
loans which show that on average banks between 30 June 2009 and 31 December 2014
have been trading within the close monitoring group.
Table 3: Trends in the Banking Sector
Extracted from Reserve Bank of Zimbabwe Presentation to the Institute of Chartered
Accountants(ICAZ) on 28 February 2015
Non -performing loans have been on a steady rise as shown above and on the table below
in graphical representation of the state of affairs in the banking industry between
December 2013 and June 2014. The graph show that Allied bank rose from 63-74%
34
while ZB rose from 17-23%. Standard Chartered Bank, MBCA and Barclays are the ones
with low levels o 7%, 3% and 2% respectively of non- performing loans.
Table 4: Zimbabwean Bank NPL’s as at June 2014
Extracted from https://www.fbc.co.zw/stockbroking/sites/fbc.co.zw.
The situation that’s prevailing in Zimbabwe in the banking industry needs urgent
remedial action that will help the sector to contribute meaningfully to the economic
activities by relaying resources to different segments of the economy.
35
This issue has been so pertinent such that there has been a trend analysis in the RBZ
monetary policy. According to the current published statement, the NPL’s have seen been
on rise and have dropped slightly from 15.91% to 14.52% by 1.39%. The loans have
moved from close monitoring category to watch list category which is below 15% of
NPL’s.
Table 5: Zimbabwean NPL Trends as at June 2015
Adapted from http://rbz.co.zw/assets/monetary-policy-july-2015.pdf
Challenges Faced In Developing Credit Reports
Mylenko (2008) gives one major challenge in developing credit reports in Africa as the
small size of its credit markets. For example, a country with a population of fifteen to
twenty million people is likely to have about two hundred thousand credit applicants.
According to a ZIMSTATS census report (2012), Zimbabwe with a population of around
thirteen million people is likely to have fewer applicants. However, credit bureaus need
36
to recover their large initial investments through the sale of credit reports, in the case of
private registries, thus rely on economies of scale. Size will therefore be a challenge.
Without financial literacy credit bureaus and other credit reporting systems do not work
as clients will go into over-indebtedness via informal financial services providers. Rhyne
et al (2011) notes that Bolivia and South Africa experienced an over-indebtedness crisis
that led to them developing stronger bureaus. Recent developments have also shown that
to maintain the prudence of the sector, the need for credit bureaus and credit information
systems is of utmost importance.
Miller and Mylenko (2003) state that well-functioning credit systems require the
existence of formal information exchange mechanisms, however another challenge faced
in credit reporting is the problem of free riders. This is when banks want to use data of
other banks but do not wish to provide the system with their own data. This defeats the
concept of reciprocity of information and results in credit information available not being
as comprehensive as it would be if all institutions participated equally. Therefore, the
system of credit reporting has its challenges but a number of countries have recorded
successes in the setting up and utilization of credit information systems.
Credit Information Systems in Other Countries
A number of credit registries have been developed worldwide which have served the
credit markets well and have resulted in positive transformations in lending.
37
In Developing Countries
IFC (2006) gives some of the credit registries and bureaus in developing countries.

TransUnion in Central Africa (TUCA) established in 1999 is a private credit
bureau that provides services in Guatemala, Honduras, El Salvador, Costa Rica
and Nicaragua. The creation of a single cross-border private credit bureau enables
the delivery of standardized products and services that have superior information
quality.

SIMAH, the Saudi Arabian credit bureau began operations in 2004 and is jointly
owned by ten banks. Currently, it contains records relating to approximately four
million borrowers. The credit bureau provides in excess of one hundred and
twenty thousand individual credit reports per month. SIMAH has also created a
commercial reporting business which complements its retail credit bureau
operation and this has resulted in the expansion of credit to small businesses.

In Vietnam, the state bank operates a public registry called a credit information
center (CIC) which is primarily a supervisory tool to identify systemic risk in the
banking industry. Beyond supervision, the CIC sends information back to lenders
on potential borrowers by way of credit reports.
38
CHAPTER 3
RESEARCH METHODOLOGY
Introduction
This chapter looks at the methodology used to carry out the research on credit
information systems as a tool for improving the loan quality of Commercial Banks in
Zimbabwe. Research methodology refers to the theory of how the research should be
undertaken, that is the tools and techniques which will be used to collect and analyze
data. These include interviews and questionnaires. This chapter thus serves as a guide to
the implementation of the research study towards the realisation of objectives set. It
highlights the research design, population, data collection methods and the research plan.
Research Design
A descriptive quantitative research method was utilized by the researcher so as to
understand the respondents’ views as well as to seek new insights on the subject topic of
the extent to which credit information systems are used as a tool for improving the loan
quality of Commercial Banks. Descriptive research designs are used when the data
collected describes persons, organisations, settings or phenomena. Statistics include
variance and standard deviation, averages and correlations.
The research design enabled the researcher to capture quantitative data to provide in
depth information on respondents’ perceptions on the extent to which credit information
39
systems are used as a tool for improving loan quality. The research instrument was
administered on nine (9) banks with two (2) respondents per bank making a total sample
of eighteen (18) respondents in Harare and Chitungwiza.
Research Population
According to Mark Sunders, et al (2009) in their book, “research methods for Business
Students” population is defined as a complete set o cases or group members from which a
sample is taken. According to the Reserve Bank of Zimbabwe, June 2015 quarterly
report, there are 13 registered commercial Banks operating in Zimbabwe. From these we
targeted 26 people which are 13 managers and 13 loan officers. This study seeks to
establish the effects, if any of credit information systems on the loan quality of
Commercial Banks in Zimbabwe hence it was only appropriate to have this as the
population.
Table 6: Population and Sample Representation
Population
Sample
Commercial
Bank
Loan
Total
Comercial
bank
loan
Total
Banks
Managers Officer Population
Banks
manager Oficers sample
13
13
13
26
9
9
9
18
40
Research Sample
The researcher used convenience and purposive sampling and selected seventy percent
(70%) of the population as a sample for the research in question. The researcher selected
all the members or respondents according to personal judgment and relevance to the
discussion area basing on accessibility, availability and probability of obtaining
information. There are thirteen (13) commercial banks that are operated in Zimbabwe and
of these; the researcher selected18 respondents from the nine (9) banks as a sample with
two respondents per branch.
The researcher took all nine (9) banks from Harare and Chitungwiza provinces due to
accessibility and affordability in terms of financial requirements and probability of
obtaining information. The researcher targeted credit personnel and branch managers as
these are well versed with credit issues and could help in providing reliable and helpful
information for the research.
Instrumentation
A questionnaire consisting of structured, closed ended questions was designed. The
closed ended questions were to allow for easy scoring and measurement of the strength of
an individual’s response per item. Each statement or closed ended question required the
respondent to select one of the five choices using likert Scale of five points ranging from
(1) never to (5) Always. This was meant to assist the respondent so that they do not have
41
to use too much time trying to resolve the best response. This saved time and thereby
increased the rate of response. Findings were measured against a mean of 3.5, thus all
cases falling below 3.5 were considered as ineffective.
Table 7: Table of Verbal Interpretation
Scale Responses Mean Interval
Effect
1
2
3
4
5
Never
Sometimes
Seldom
Often
Always
0.00 to 1.50
1.51 to 2.50
2.51 to 3.50
3.51 to 4.50
4.51 to 5.00
Verbal Intepretation
Usage
Extent
no effect
Never
low
low effect
Almost Never
slightly high
moderate effect Occassional /Sometimes moderately high
high efect
Almost Everytime
very high
very high effect
Every time
Extremely high
Validity and reliability
The researcher submitted the instrument to the statisticians at Solusi University to ensure
content and face validity. The reliability was tested through pilot study which the
researcher conducted with 18 randomly selected bank managers and loan officers from 9
banks in Rusape, Marondera and Mutare.
Table 8: Reliability Statistics
Cronbach's Alpha
Number of Items
.721
36
From the reliability table above the Cronbach’s Alpha is 0.721 representing a high level
internal consistency.
42
Data Collection
The researcher obtained a letter from the MBA department of Solusi University which
was then presented to all commercial banks in Harare and Chitungwiza in Zimbabwe
together with the research instrument that the researcher had designed for the study. The
research questions was distributed to the sample of 18 respondents and then returned to
the researcher after an agreed timeframe. The researcher administered the instrument to
the respondents and personally collected them when they were completed. The data
gathered was then used to determine whether the extent information systems use affects
loan quality in commercial banks in Zimbabwe.
Data Analysis
The data collected in quantitative form was coded and the Statistical package for Social
Science (SPSS) was used to analyse the data in order to calculate measures of central
tendency, significant tests and regression. A quantitative method of data analysis was
used. Also descriptive statistics were used to calculate the characteristics of the available
data gathered by the researcher.
43
CHAPTER 4
PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA
Introduction
This chapter provide analysis and interpretation of the data obtained from empirical
study. The results analysed based on the concepts discussed in the literature review.
Descriptive statistics in form of tables have been used to present the data. To reach a
thorough conclusion, data is analysed, presented and interpreted in line with the research
question and research objectives.
The demographics of the study revealed that there were seven male and two female bank
managers. Only two bank managers who are male had diplomas while the remainder had
university degrees. All managers who responded to the instrument had between 11 to
20years’ experience in the banking industry.
The loan officers who responded, seven had university degrees while two had diplomas.
The demographical distribution was that there were six male officers and three female
officers in the loan departments. Four of the officers had 11-20 years in the banking
industry while five were below 10 years in the banking industry.
44
Research Question 1:
To what extent is the use of credit information system in commercial banks affecting
loan quality?
Table 9: Loan Quality Measure
N
Mean
Std. Deviation
31.The experience at our bank is that Non-Performing Loans Affect the Bank’s
18
3.72
.752
18
4.67
.485
18
4.61
.502
18
4.11
1.023
18
4.28
.461
18
4.28
.461
Loanqave
18
4.2778
.23570
Valid N (listwise)
18
Loan Quality
32.At our bank the relationship between loan Quality and Non-performing loans
is adverse.
33.The experience at our bank is that failure to use Credit Information Systems
Erode Loan Quality
34.The experience at our bank is that failure to use Credit Information Systems
increase Non-performing loans
35.Our bank uses credit information systems to improve working capital which
is adversely affected by Non-performing loans
36.At our bank it has been experienced that non-performing loans can be
reduced rapidly if a combination of systems are used
Table 9 show an average mean of 4.2778 and an average standard deviation of 0.23570.
This shows that on average the use of credit information system in commercial banks is
high. The standard deviation of 0.23570 shows that the responses were homogeneous.
This shows that on average respondents unanimously responded uniformly on the high
45
extent to which credit information systems are used by commercial banks. However,
World Bank (2012) states that there are other factors that affect loan quality like the role
of information sharing. A client can have multiple loan application in Zimbabwe and still
get all loans approved. The issue of the CIS’s working to better client selection ends up
being countered by information asymmetry.
Furthermore, there are other factors also that have an impact on the loan quality like
natural disasters for example in the agriculture and manufacturing sectors of the
economy, integrity of the borrower, market conditions and government policy.
Agricultural loans are usually affected by climatic conditions. Most farmers end up
defaulting on loan payments when their crops fail even when the vetting process was
meticulously done.
In addition, some political and economical factors may affect the loan repayments for
example 2008-2013 were years when the economy was recovering. Post 2014 the
economic environment was stabilising and we expected growth yet companies started to
wind down. These factors in as much as there may been a likelihood of growth and a lot
of loans had been rolled out, ended up affecting the loan quality due to high levels of
default rates.
Integrity of borrowers is when the borrower gives wrong information to the banks
knowingly. This is when fraudulent working information in form of contracts and
46
payslips is used. This affect the rating process and people who should not get loans end
up receiving the advances. This ends up distorting loan quality.
Research question 2:
To what extent are Commercial Banks in Zimbabwe using the following credit
information systems in managing credit risk?
Table 10: Credit Approval Authority
Std.
N
1.Our Commercial Bank has a lending approval committee
Mean
Deviation
18
5.00
.000
18
5.00
.000
3.At our Bank all loan applications pass through the committee
18
3.78
.647
4.Loans at our commercial bank are granted by a committee and not individuals
18
4.11
.758
5.Our Bank’s lending committee scrutinised all loans for proper
18
4.78
.428
18
4.67
.485
18
4.17
.707
Creditave
18 4.0238
.19752
Valid N (listwise)
18
2.AT our commercial bank the lending committee meet regularly and follow the stated
procedures to review applications for loans consistently
6.At our bank credit approval committee is mandated with the banks’ periodic credit
reviews of maturing loans
7.The experience at our bank is that the Credit Approval committee actions help improve
loan quality
47
Table 10 presents findings regarding the credit approval authority in Zimbabwean
commercial banks. Findings show an average mean score of 4.0238 and an average
standard deviation of 0.19752. The average mean shows that almost every time
commercial banks in Zimbabwe use credit approval authority in trying to improve the
bank’s loan quality. The standard deviation of 0.19752, which is below 1 show that the
respondents responded homogeneously. The homogeneity of the response states that on
average the respondents acknowledge that to a very high extent credit approval authority
is used by commercial banks in managing loan quality.
In as much as there is high usage of credit approval authority, Dr Mangudya in the June
2015 monetary policy reading stated that there are other factors that may counter credit
approval like the weakening borrowers capacity to repay their debt. In addition to this
assertion, World Bank report (2012) states that the role of information sharing can also be
instrumental in making or breaking the credit market in that there may be information
that other lenders may have that may not be available to a new credit provider hence the
client may default many loans before they are discovered.
48
Table 11: Risk Pricing
N
8.At our bank a loan interests are established in relation to risk of the
borrower.
9.The use of Risk based pricing at our bank causes borrowers to pay
generally more in the form of a higher interest rate
10.At our bank the use of Risk based pricing gives its clients an
opportunity to borrow instead of being denied.
11.Risk based pricing use at our bank helps in determining a better loan
quality by discouraging would be borrowers from getting credit lines due
to the inhibitive interest rates.
12.At our bank clients are properly advised that they are paying more on
interest because they are rated as risky.
13.The experience at our bank is that Risk based pricing helps in
minimizing loan default
Riskave
Valid N (listwise)
Mean
Std.
Deviatio
n
18
4.28
.669
18
4.28
.669
18
3.89
.758
18
3.78
.647
18
4.06
.639
18
2.72
.461
18
18
3.8333
.26813
Table 11 shows an average mean of 3.8333 and an average standard deviation of
0.26813. The average mean shows that respondents agreed that almost every time
commercial banks in Zimbabwe use Risk pricing to improve the bank’s loan quality. The
standard deviation of 0.26813 which is below 1, show that the respondents are
homogeneous. The homogeneity of the respondents show that on average the respondents
agree that to a very high extent risk pricing is used by commercial banks in managing
loan quality.
Question 13 has the lowest mean of 2.72 which shows that some respondents noted that
risk based pricing use helped in reducing loan default in Zimbabwe while other did not
49
concur. This may be as a result of availability of non-risk based pricing loans being
awarded such that banks do not often use them to determine awarding of loans due to
competition.
Risk based pricing rates clients on the low to high risk in relation to ability to repay a
loan. They may look at employment status and income or credit scores to show the risk
associated with a particular client. With the recent labour laws that allowed employers to
issue three months’ notice to employees this may have had an adverse impact on loan
repayment in relation to risky clients.
Dr J Mangudya in the June 2015 monetary policy stated that the absence of public credit
bureaus in Zimbabwe also has an impact in that there is no collective data bank that
assists in sharing information among different players in the market thereby reducing the
information gap.
50
Table 12: Portfolio Management
Std.
Deviati
N Mean
on
14.Loan portfolio management (LPM) at our bank helps reduce risks that
are inherent in the credit process are managed and controlled
15.At our bank management regularly review loan portfolio performance to
curb possible irregularities.
16.At our bank loan portfolio managers concentrate their effort on
prudently approving loans and carefully monitoring loan performance
17.The use of Loan Portfolio Management at our bank helps in minimizing
loan default
18.The experience at our bank is that the use of Loan portfolio
Management actions by management help improve loan quality
Portfolioave
Valid N (listwise)
18
4.06
.416
18
4.17
.618
18
4.00
.000
18
4.00
.000
18
4.00
.000
18 4.0444 .14642
18
Table 12 shows an average mean of 4.0444 and an average standard deviation of
0.14642. The average mean shows that almost every time commercial banks in
Zimbabwe use portfolio management to improve the bank’s loan quality. The standard
deviation of 0.14642 which is below 1, show that the respondents are homogeneous. The
homogeneity of the response show that on average to a high extent portfolio management
is used by commercial banks in managing loan quality.
In as much as there is a very high level of usage of portfolio management, the banking
sector’s external environment has a bearing on the outcome of the increase in NPL’s.
Competitive rivalry on the domestic banks market has had a great impact on the rise of
NPL’s. Banks are in a drive to get more customers who will translate into more deposits
51
that they would like to tie down for some time with advancement of loans. The analysis
on the FBC bank website extraction of the half year June 2014 bank NPL’s shows that
the least affected banks are the ones that are foreign owned like Standard Chartered
Bank, Stanbic Bank, MBCA and Barclays including CBZ that has government as the
largest single investor but not majority shareholder ownership . The remainder show an
increase in NPL’s which may be best explained by information asymmetry.
Table 13: Private Credit Registers
Std.
Deviati
N
Mean
on
25.Our bank uses private registries in checking credit worthiness
18
4.56
.511
26.When checking the creditworthiness using private registries the prices are inhibitive
18
4.50
.514
18
4.22
.647
28.The use of Private registries information at our bank help improve loan quality
18
3.56
.511
29.At our bank the use of private registries reduces the levels of non-performing loans
18
4.56
.511
18
4.39
.502
27.Commercial Banks voluntarily shares information’s with other lenders through private
registries.
30.Use of Private credit Bureaus generated Information on Clients by our bank help
improve loan quality by reducing default rates
Privateave
18
Valid N (listwise)
18
3.7037 .18573
Table 13 shows an average mean of 3.0737 and an average standard deviation of
0.18573. The average mean shows that respondents agreed that almost every time
52
commercial banks in Zimbabwe use Private credit registers to improve the bank’s loan
quality. The standard deviation of 0.18573 which is below 1, show that the respondents
are homogeneous. The homogeneity of the respondents show that on average the
respondents agree that to a very high extent private credit registers is used by commercial
banks in managing loan quality.
The information that these private registries provide is independent and objective since
the client is the bank and not the person applying for the loan. Lenisa (2007) stated that
private credit registries help reduce fraud by providing additional information on top of
that gathered by credit approval authority and portfolio management that allow risk
assessors to recognize potentially falsified loan applications.
Dr John Mangudya reiterated in the august 2015 monetary policy statement that the
absence of information sharing has led to the collapse of the in the lending market. Hence
the private registries help bridge that gap.
53
Research Question 3:
To what degree has Commercial Banks in Zimbabwe benefited from using
information systems in improving loan quality?
Table 14: Model Summary
Analysis results presented that credit approval authority, risk pricing, portfolio
management and public credit register have no significant impact on loan quality. Table
14 shows an adjusted R square value of 0.201 which means that the private credit
registers have a 20.1% effect on loan quality. The positive R value of 0.498 which is the
correlation value shows that there is a good relationship between private credit register
use and loan quality. This means that the more we use the information from private credit
registers the better our loan quality improves.
The reason why private registries have more significant impact is that by nature they are
external participants who are capable of facilitating appropriate analysis of credit
worthiness of a client with greater transparency. Furthermore, private credit
registers(PCRs) also collect positive information about the borrower which Japelli and
Pagano (2005) argue that this may lead lenders to discover reputable clients readily,
resulting in better loan quality .
54
Table 15 : Anova Table
Model
1
Sum of Squares
Df
Mean Square
Regression
.234
1
.234
Residual
.711
16
.044
Total
.944
17
F
Sig.
5.267
.036a
a. Predictors: (Constant), privateave
b. Dependent Variable: loanqave
In table 15 an F Value of 5.267 which is significant at P value of 0.036 . The P value is
compared to 0.05, and since it is less with a value of 0.036 it means that private credit
registers and loan quality model is very significant hence it can be used to make valuable
assumptions and make recommendations.
The significance of the relationship is positive in that the more commercial banks used
information from private credit registers. They are capable of analysing and selecting
high rated clients who have a clean credit record. The private registries use information
from varied sources such that would be defaulters are quickly identified and blacklisted
as risky. This then help identify high quality clientele for the commercial banks.
Hence, significant relationship shown by an F Value of 5.267 which is significant at P
value of 0.036 disregards the null hypothesis that stipulated that commercial banks in
55
Zimbabwe do not use credit information systems and that there are no benefits derived
from the use of the systems by commercial banks in Zimbabwe.
Table 16: Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
1.939
1.020
privateave
.632
.275
Coefficients
Beta
t
.498
Sig.
1.900
.076
2.295
.036
a. Dependent Variable: loanqave
Table 16 which is the coefficients table has a positive Beta value of 0.498. We are more
interested in knowing the sign whether its positive or negative. Since a positive value
means that there is a positive relationship, it means that if we increase the independent
variable which is private credit registers then we improve the dependent variable which is
the loan quality.
This means that private credit registers use has more significant impact on loan quality
and reduction of Non-performing loans. Irrespective of the above, the research findings
state that in Zimbabwe commercial banks have not benefitted fully on the use of credit
information systems in improving loan quality since the levels of nonperforming loans
have been on the rise despite the results of the study stating that there is high usage of
credit approval authority, portfolio management, private credit registries and risk pricing.
56
The study only recognised the private credit registers as having a significant impact on
loan quality as compared to the other variables which are commercial bank internal
measures to curb the rise of NPL’s. These internal measures in as much as they are used,
the bank personnel may be having a bias to the extent of overlooking pertinent
information like inconsistency in salary deposits, inconsistency in salary amount deposits
and rate of salary withdrawals. This may assist in rating the level of risk associated with a
client.
Commercial banks in Zimbabwe have been meticulously using the information systems
in trying to improve loan quality. The average means on the testing of the extent to which
they use information systems. However, it has been proven by statistics that in as much
as they have been used widely in the industry, they have a significant effect to the quality
of loans hence they have to a lesser extent benefited the Commercial banks in answering
the question of loan quality
57
CHAPTER 5
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Introduction
This Chapter presents the research summary, conclusions and recommendations,
formulated from the research findings in chapter 4. An area of further study is also
presented in this chapter.
Summary
This research was to assess the significance of credit information systems which include
credit approval authority, risk pricing, portfolio management and private credit registers
as a tool for improving commercial bank loan quality. The research also sought to
achieve the following objectives:

To establish the use of credit information systems as a credit risk management
strategy by Zimbabwean Commercial Banks.

To investigate the value derived from using credit information systems by
Commercial Banks in Zimbabwe

To establish the reasons why borrowers default on loans from Commercial banks

To evaluate the extent to which credit risk information techniques currently
employed by Commercial Banks in Zimbabwe are assisting in improving loan
quality.
58
The research was carried out using quantitative descriptive research. Descriptive
statistics, frequency and factor analysis were used to arrive at the findings. The
population was 13 banks and we sampled seventy percent (70%) of the population to
have a sample of nine (9) banks. The researcher interview two people per bank namely
the manager and the loans officer giving us a sample number of 18 respondents.
The assessment was also meant to check the extent to which commercial banks use Credit
information systems as a tool or improving loan quality. The research was also meant to
come up with recommendations that would assist bank managers and loans officers on
the way forward.
The findings of the study were as follows:
1. Statistics shows that credit information systems were often used in banks to help
improve loan quality as shown an average mean of 4.28 and an average standard
deviation of 0.24
2. The findings showed that credit approval authority is used every time by
commercial banks in trying to improve loan quality. This was supported by an
average mean of 4.02 and an average standard deviation of 0.20.
3. Responses were homogeneous in that commercial banks in Zimbabwe almost
every time use risk pricing for authorising loans to improve loan quality as
supported by an average mean of 3.83 and an average standard deviation of 0.27.
59
4. On portfolio management use as a credit Information System, the responses
homogeneously stated that commercial banks in Zimbabwe are to a higher extent
using the credit Information System in improving loan quality as supported by an
average mean of 4.04 and an average standard deviation of 0.15.
5. The responses homogeneously asserted that commercial banks in Zimbabwe use
Private Credit register almost always as shown by an average mean of 3.07 and an
average standard deviation of 0.19.
6. Research findings presented that credit approval authority, risk pricing, portfolio
management and public credit register have no significant impact on loan quality.
Table 4.7 shows an adjusted R square value of 0.201 which means that the private
credit registers have a 20.1% effect on loan quality. The positive R value of 0.498
which is the correlation value shows that there is a good relationship between
private credit register use and loan quality. This means that the more we use the
information from private credit registers the better our loan quality improves.
Conclusion
The study revealed that commercial Banks in Zimbabwe are using credit information
systems which include credit approval authority, risk pricing, portfolio management
and private credit registers. In as much as they are highly used, the study revealed that
the commercial banks in Zimbabwe have not had a positive benefit derived from the
use of the credit information systems as listed in the conceptual framework as there
has been a marked increase of nonperforming loans in the industry.
60
This can be compounded to a variety of cause ranging from government policies,
market competition as well as economic and social factors that affect business
practices on a daily basis. Hence, the Reserve Bank of Zimbabwe highlighted in the
July 2015 monetary statement that they will set up a public credit registry to help
monitor other elements like information asymmetries to help reduce nonperforming.
In addition, the study further revealed that of the credit information systems used by
commercial banks highlighted in this research study, only private credit register have
a significant effect of 20.1% on loan quality.
Recommendations
Based on the research findings, the researcher recommends the following:
1. Commercial bank management should invest more in the use of private credit
registers since it has a significant effect on loan quality.
2. Management at commercial bank should recommend and assist in the formation
of a public credit registry by the Reserve Bank of Zimbabwe so as to reduce
effects of information asymmetry.
3. Commercial banks should ascribe proper vetting on background client
information so that those who do not qualify for loans approval are picked and
denied so as to safeguard the banks working capital.
4. Management of commercial banks should not have few big clients constituting
the majority of loan balance in their books as this can be very fatal in case of
those clients defaulting or liquidating.
61
Recommendations for Further Study
In future study should be carried on the following:
1. An analysis of the extent to which credit information systems are used as a tool
for improving loan quality by commercial banks in Zimbabwe.
2. Alternative strategies to guard banks against credit risk in Zimbabwe.
3. Determinants of credit risk in the Zimbabwean banking industry.
62
63
REFERENCE
Adhikary, K. B. (2005) Nonperforming Loans in the Banking Sector of Bangladesh:
Realities and Challenges. Bangladesh Institute of Bank Management
(BIBM), Bangladesh
Baer. T., Carassim. M., Miglio A., Fabiani C.,& Ginevra.E. (2009). The national credit
bureau: A key enabler of financial infrastructure and lending in developing
economies. McKinsey working paper on risk.
Barron. J,Straten. M.(2003). The value of Comprehensive Credit Reports: Lessons from
the US experience. Georgetown University, Washington, D.C.
Bennardo. A., Pagano. M., &Piccolo. S (2009). Multiple- Bank Lending, Creditor Rights
and
Information Sharing. CEPR Discussion Paper No. DP7186, London,
United Kingdom
Bolton. C. (2009). Logistic regression and its application in credit scoring. University of
Pretoria, South Africa
Brown M & Zehdner. C.(2007). The Emergence of Information Sharing in Credit
Markets.
Caire. D, Barton. S, Zubiria. A,Alexiev Z, Dyer. J, Bundred. F & Brislin. N.(2006). A
handbook for developing credit scoring systems in a microfinance context.
USAID micro report #66. United States of america
Biggar, D. & Heimler, A. (2005). An increasing role for competition in the regulation of
banks. Antitrust Enforcement in Regulated sectors – Subgroup 1, ICN. Bonn
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Galindo A., & Miller. M. (2001). Can Credit Registries Reduce Credit Constraints?
Empirical
Evidence on
the Role of Credit Registries in Firm Investment
Decisions. Inter-American Development Bank, USA
Gardeva A,& Rhyne E. (2011). Opportunities and Obstacles to Financial Inclusion
Center for
financial inclusion. Publication 12 survey report.
Hachinobe University Research Institute (2008). Daiwa Institute of Research
Development of
Database on Corporate Credit Information ASEAN Plus
Three Financial Ministers
Meeting, Research Group.
International Finance Cooperaton (2006). Credit bureau knowledge guide Washington
DC.International Finance Cooperaton, USA
Mohammed Shofiqul Islam, Nikhil Chandra Shil & Abdul Mannan M.D. (2005), “Non
performing loans – its causes, consequences and some learning.” MPRA Paper.
No 7708
Kallberg J G.,&Udell F G (2003). Private Business Information Exchange in the United
States.
Lenisa Frank (2007). The Importance of Credit Information and Credit Scoring for
Microlending and Microfinance Institutions. Compuscan Information
Technologies. South Africa
Madrid, Minetti (2009). Sharing Information in the Credit Market: Contract-Level
Evidence from U.S. Firms. Journal of Financial Economics Volume 109, Issue 1,
July 2013, Pages 198–223
65
McIntosh, C., Luoto j Wydick B (2004). Credit information systems in less developed
countries: A test with Microfinance in Guatemala. Economic Development and
Cultural Change ( January 2007), vol. 55, no. 2, pp. 313-334
Miller. G.(2001). Credit reporting systems around the globe. The state of the art in public
and private credit registries. World Bank
Mylenko Nataliya (2008). Developing credit reporting in Africa. Opportunities and
challenges. International Finance Corporation
Padilla, A J., &Pagano M. (2000). Sharing default information as a borrower discipline
device. European Economic Review, vol. 44, issue 10, pages 1951-1980
Pagano M., &Japelli T. (2005). Roles and effects of Credit Information Sharing. Working
Paper no. 136. University of Salerno and CSEF
Powel, A., Mylenko, N., Miller M., (2004)Manoni GImproving Credit Information, Bank
Regulation and Supervision: On the Role and Design of Public Credit Registries.
Washington, D.C World Bank Research Working Paper Series .
Saunders, M., Lewi, P. M. & Thornhill, A. (2003.). Research Merthods or Business
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Smith, M.M. (2006). Recent developments in credit scoring. A summary. Federal Reserve
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Staten, M.E.(2008). Maximizing the benefits from credit reporting. TranUnion. LLC
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Turner, A. M., Varghese, R., Walker, P. (2008).Information sharing and SMME
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World Bank. (2006). Financial Infrastructure. International Finance Corporation
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67
APPENDIX A:
INTRODUCTORY LETTER
2843 Unit C
Seke
Chitungwiza
July 7, 2015
Dear Sir/Madam
RE: Research Study Topic: An Analysis of Credit Systems use as A Tool For improving
Loan Quality atCommercial Banks in Zimbabwe.
My name is Silence Chigariro and I am conducting a research to fulfill the requirements
of Masters of Business Administration degree with Solusi University. The purpose of this
research is solely for academic purposes and all information gathered will be treated with
utmost confidentiality.
Your contribution to this study is very important as it will give this researcher first-hand
information on the need of credit information systems in the financial sector.
Your cooperation is greatly appreciated.
Thank you
68
APPENDIX B:
QUESTIONNAIRE
Dear Respondents,
My name is Chigariro Silence, an MBA Student at Solusi University in Zimbabwe. I am
carrying out a research on The extent to which Credit Information Systems are used
as A Tool for improving Loan Quality at Commercial Banks in Zimbabwe. The
information obtained from this study will be used sorely for educational purposes only.
All information will be treated confidentially and will be closely guarded. Please do not
write your name or any personal detail on this questionnaire.
Section A: GENERAL INFORMATION
Please fill in the following by ticking the box that correctly describes your attributes.
General Information:
1. Job title:……Bank Manager
2. Indicate your gender: Female
loan Officer
Male
3. Academic qualifications:…: Ordinary level
Diploma
4. Years of experience:…
Advanced Level
Degree
1 -10
11-20
69
20 and above
Section B
Since dollarization has your bank offered any credit facilities?
Yes
No
5. Which loan facilities have you offered?
Personal
Corporate
SME
Other
All
Other, please specify
..........................................................................................................................................
..........................................................................................................................................
........................
6. What non-performing loan Level or category according to the Troubled and Insolvent
Bank Policy is your bank?
watch list(10%-15%)
Close monitoring(15%-25%)
Mandatory remedial
action(Above 25%)
Section C
Instructions to Respondents
Please kindly indicate with a mark the extent to which you agree or disagree with each
one of the following statements, based on the explanation of items in the rating scale:
70
Always (5) Often (4) Seldom (3) Sometimes (2) Never (1)
ITEM
5
4
3
2
1
5
4
3
2
1
CREDIT APPROVAL AUTHORITY
7.
Our Commercial Bank has a lending approval committee
8.
AT our commercial bank the lending committee meet
regularly and follow the stated procedures to review
applications for loans consistently
9.
At our Bank all loan applications pass through the
committee
10.
Loans at our commercial bank are granted by a committee
and not individuals
11.
Our Bank’s lending committee scrutinised all loans for
proper
collateral and supporting documentation
12.
At our bank credit approval committee is mandated with
the banks’ periodic credit reviews of maturing loans
13.
The experience at our bank is that the Credit Approval
committee actions help improve loan quality
RISK PRICING
14.
At our bank a loan interests are established in relation to
71
risk of the borrower.
15.
The use of Risk based pricing at our bank causes borrowers
to pay generally more in the form of a higher interest rate
16.
At our bank the use of Risk based pricing gives its clients an
opportunity to borrow instead of being denied.
17.
Risk based pricing use at our bank helps in determining a
better loan quality by discouraging would be borrowers
from getting credit lines due to the inhibitive interest rates.
18.
At our bank clients are properly advised that they are paying
more on interest because they are rated as risky.
19.
The experience at our bank is that Risk based pricing helps
in minimizing loan default
PORTFOLIO MANAGEMENT
20.
5
Loan portfolio management (LPM)
at our bank helps
reduce risks that are inherent in the credit process are
managed and controlled
21.
At our bank management regularly review loan portfolio
performance to curb possible irregularities.
22.
At our bank loan portfolio managers concentrate their effort
on prudently approving loans and carefully monitoring loan
performance
72
4
3
2
1
23.
The use of Loan Portfolio Management at our bank helps in
minimizing loan default
24.
The experience at our bank is that the use of Loan portfolio
Management actions by management help improve loan
quality
PRIVATE CREDIT BUREAUS REGISTRY (PCB)
25.
Our bank uses private registries in checking credit
worthiness
26.
When checking the creditworthiness using private registries
the prices are inhibitive
27.
Commercial Banks voluntarily shares information’s with
other lenders through private registries.
28.
The use of Private registries information at our bank help
improve loan quality
29.
At our bank the use of private registries reduces the levels
of non-performing loans
30.
Use of Private credit Bureaus generated Information on
Clients by our bank help improve loan quality by reducing
default rates
LOAN QUALITY AND NON-PERFORMING LOANS
31.
The experience at our bank is that Non-Performing Loans
73
Affect the Bank’s Loan Quality
5
32.
At our bank the relationship between loan Quality and Nonperforming loans is adverse.
33.
The experience at our bank is that failure to use Credit
Information Systems Erode Loan Quality
34.
The experience at our bank is that failure to use Credit
Information Systems increase Non-performing loans
35.
Our bank uses credit information systems to improve
working capital which is adversely affected by Nonperforming loans
36.
At our bank it has been experienced that non-performing
loans can be reduced rapidly if a combination of systems are
used
THANK YOU.
74
4
3
2
1
75
APPENDIX C:
CURRICULUM VITAE
Silence Chigariro
CELL#: +263774622355
EMAIL ADDRESS: [email protected]
PERSONAL DETAILS
Sex
Male
Date of Birth
19 December 1980
ACADEMIC QUALIFICATIONS
Solusi University (Zimbabwe)
Name of University
B.B.A-Accounting
Field of Study
Date completedSeptember
2004
Name of UniversitySolusi University
(Zimbabwe)
Field of Study
MBA
Date completed
Pending
Current Employment
East Zimbabwe Conference of S.D.A Church (Zimbabwe)- Accountant
Period of service
May 2011- Current
Previous Employment
76
1. Camelsa Chartered Accountants (Johannesburg (SA)-Bookkeeper
Period of service
February 2008-June 2008
2. KFML Holdings-Port Elizabeth (South Africa) -Junior Bookkeeper
Period of service
September 2007- January 2008
3. Ramathe Chartered Accountants-Port Elizabeth (South Africa) -Bookkeeper
Period of service
September 2006- August 2007
Sun accounting system
Microsoft office (MS Excel, MS Word)
Responsibilities Held:
Class Treasurer (Senior Class 2004)
SolusiUniversity
Assistant Youth Director 2003-2004
SolusiUniversity
REFEREES
Michelle McDermid (Accountant)
Mr. Chris Ndohlo (Manager)
FML Wholesale & Yellow Zebra Optical
Ramathe Chartered Accountants (SA)
P.O Box 12479
2A McAdam Street
Centralhill, Port Elizabeth, 6006
NewtonPark, Port Elizabeth, 6000
Office: +27415065955
Cell:
+27839537962
Curly Bvuma (Office Manager)
Mrs. F Ndakavambani (Secretary-President)
Camelsa Chartered Accountants (SA)
East Zimbabwe conference
6 Smuts House
Seventh-day Adventist church
VornaValley, Midrand, 1686
4 Thorn road Waterfalls, Harare, Zimbabwe
Tel: 0118051027
Cell: +26391306978
77
APPENDIX D:
SPSS RESULTS
Std.
Credit Approval Authority
N
1.Our Commercial Bank has a lending approval committee
Mean
Deviation
18
5.00
.000
18
5.00
.000
3.At our Bank all loan applications pass through the committee
18
3.78
.647
4.Loans at our commercial bank are granted by a committee and not individuals
18
4.11
.758
5.Our Bank’s lending committee scrutinised all loans for proper
18
4.78
.428
18
4.67
.485
18
4.17
.707
Creditave
18 4.0238
.19752
Valid N (listwise)
18
2.AT our commercial bank the lending committee meet regularly and follow the
stated procedures to review applications for loans consistently
6.At our bank credit approval committee is responsible mandated with the
banks’ periodic credit reviews of maturing loans
7.The experience at our bank is that the Credit Approval committee actions help
improve loan quality
78
Risk Pricing
Std.
N
8.At our bank a loan interests are established in relation to risk of the borrower.
Mean
Deviation
18
4.28
.669
18
4.28
.669
18
3.89
.758
18
3.78
.647
18
4.06
.639
18
2.72
.461
Riskave
18 3.8333
.26813
Valid N (listwise)
18
9.The use of Risk based pricing at our bank causes borrowers to pay generally
more in the form of a higher interest rate
10.At our bank the use of Risk based pricing gives its clients an opportunity to
borrow instead of being denied.
11.Risk based pricing use at our bank helps in determining a better loan quality
by discouraging would be borrowers from getting credit lines due to the inhibitive
interest rates.
12.At our bank clients are properly advised that they are paying more on interest
because they are rated as risky.
13.The experience at our bank is that Risk based pricing helps in minimizing loan
default
79
Std.
Portfolio Management
Deviati
N
Mean
on
14.Loan portfolio management (LPM) at our bank helps reduce risks that are inherent in
18
4.06
.416
18
4.17
.618
18
4.00
.000
18
4.00
.000
18
4.00
.000
the credit process are managed and controlled
15.At our bank management regularly review loan portfolio performance to curb possible
irregularities.
16.At our bank loan portfolio managers concentrate their effort on prudently approving
loans and carefully monitoring loan performance
17.The use of Loan Portfolio Management at our bank helps in minimizing loan default
18.The experience at our bank is that the use of Loan portfolio Management actions by
management help improve loan quality
Portfolioave
18
Valid N (listwise)
18
80
4.0444 .14642
Private Registries
Std.
N
Mean Deviation
25.Our bank uses private registries in checking credit worthiness
18
4.56
.511
26.When checking the creditworthiness using private registries the prices are inhibitive
18
4.50
.514
18
4.22
.647
28.The use of Private registries information at our bank help improve loan quality
18
3.56
.511
29.At our bank the use of private registries reduces the levels of non-performing loans
18
4.56
.511
18
4.39
.502
27.Commercial Banks voluntarily shares information’s with other lenders through
private registries.
30.Use of Private credit Bureaus generated Information on Clients by our bank help
improve loan quality by reducing default rates
Privateave
3.703
18
.18573
7
Valid N (listwise)
18
81
Std.
Loan Quality
N
Mean
Deviation
31.The experience at our bank is that Non-Performing Loans Affect the Bank’s Loan
18
3.72
.752
18
4.67
.485
18
4.61
.502
18
4.11
1.023
18
4.28
.461
18
4.28
.461
Loanqave
18 4.2778
.23570
Valid N (listwise)
18
Quality
32.At our bank the relationship between loan Quality and Non-performing loans is
adverse.
33.The experience at our bank is that failure to use Credit Information Systems Erode
Loan Quality
34.The experience at our bank is that failure to use Credit Information Systems
increase Non-performing loans
35.Our bank uses credit information systems to improve working capital which is
adversely affected by Non-performing loans
36.At our bank it has been experienced that non-performing loans can be reduced
rapidly if a combination of systems are used
82
Std.
RegressionDescriptive Statistics
Dev
iatio
Mean
Loanqave
n
N
4.277 .235
18
8
Creditave
70
4.023 .197
18
8
Riskave
52
3.833 .268
18
3
Portfolioave
13
4.044 .146
18
4
Publicave
42
1.592 .085
18
6
Privateave
22
3.703 .185
18
7
83
73
Correlations
Loanqav
e
Pearson Correlation
portfolioave publicave
privateave
1.000
-.271
.233
-.152
.190
.498
Creditave
-.271
1.000
.212
.019
.444
-.598
.233
.212
1.000
.100
.286
.000
-.152
.019
.100
1.000
-.192
.296
Publicave
.190
.444
.286
-.192
1.000
.184
Privateave
.498
-.598
.000
.296
.184
1.000
Loanqave
.
.139
.176
.274
.225
.018
Creditave
.139
.
.200
.470
.033
.004
Riskave
.176
.200
.
.347
.125
.500
portfolioave
.274
.470
.347
.
.223
.116
Publicave
.225
.033
.125
.223
.
.233
Privateave
.018
.004
.500
.116
.233
.
Loanqave
18
18
18
18
18
18
Creditave
18
18
18
18
18
18
Riskave
18
18
18
18
18
18
portfolioave
18
18
18
18
18
18
Publicave
18
18
18
18
18
18
Privateave
18
18
18
18
18
18
portfolioave
N
riskave
Loanqave
Riskave
Sig. (1-tailed)
creditave
84
Variables Entered/Removeda
Variables
Model Variables Entered
Removed
Method
1
Stepwise (Criteria:
Probability-of-F-toprivateave
. enter <= .050,
Probability-of-F-toremove >= .100).
a. Dependent Variable: loanqave
Model Summary
Change Statistics
Std. Error
Model
R
1
.498a
R Square
F
Adjusted R
of the
R Square
Chan
Square
Estimate
Change
ge
.248
.201
.21073
df1
.248 5.267
df2 Sig. F Change
1
16 .036
a. Predictors: (Constant), privateave
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
.234
1
.234
Residual
.711
16
.044
Total
.944
17
85
F
5.267
Sig.
.036a
Std.
RegressionDescriptive Statistics
Dev
iatio
Mean
Loanqave
n
N
4.277 .235
18
8
Creditave
70
4.023 .197
18
8
Riskave
52
3.833 .268
18
3
Portfolioave
13
4.044 .146
18
4
Publicave
42
1.592 .085
18
6
a. Predictors: (Constant), privateave
b. Dependent Variable: loanqave
86
22
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
Beta
(Constant)
1.939
1.020
privateave
.632
.275
t
.498
Sig.
1.900
.076
2.295
.036
a. Dependent Variable: loanqave
Excluded Variablesb
Collinearity
Statistics
Model
1
Beta In
t
Sig.
Partial Correlation
Tolerance
creditave
.042a
.150
.883
.039
.642
riskave
.233a
1.079
.298
.268
1.000
-.328a
-1.499
.155
-.361
.912
.102a
.450
.659
.115
.966
portfolioave
publicave
a. Predictors in the Model: (Constant), privateave
b. Dependent Variable: loanqave
87