THE RELATIONSHIP BETWEEN CREDIT DEFAULT RISK AND
CARDHOLDER CHARACTERISTICS, CREDIT CARD
CHARACTERISTICS, BEHAVIORAL SCORING PROCESS
AMONG COMMERCIAL BANKS IN KENYA
BY
MOKAYA CLIFFORD NYAMONGO
D61/73703/2009
A Project Report submitted in Partial Fulfillment of the Requirement
for the Degree of Master of Business Administration (MBA) of the
University of Nairobi
OCTOBER 2011
DECLARATION
I, the undersigned declare that this is my original work and has not been submitted to any
other college or university.
Signed: ---------------------------------------------------------- Date--------------------------------MOKAYA CLIFFORD NYAMONGO
D61/73703/2009
This project report was presented with my approval as the university supervisor
Signed: ---------------------------------------------------------- Date--------------------------------DR. SIFUNJO KISAKA
ii
DEDICATION
I dedicate this research project report to my family; my dearest Dad Tom Mokaya and
Mum Immaculate Magoma, for their encouragement and support during the study.
iii
ACKNOWLEDGEMENT
I wish to acknowledge the wise and constructive support of my supervisor Dr. Sifunjo
Kisaka throughout the research process. I also wish to thank the Card Centre Officers in
the Commercial banks in Kenya involved in the study for their support and willingness to
participate in provision of the information required for this study.
iv
TABLE OF CONTENTS
DECLARATION ................................................................................................................ ii
DEDICATION................................................................................................................... iii
ACKNOWLEDGEMENT ................................................................................................. iv
TABLE OF CONTENTS.....................................................................................................v
LIST OF TABLES........................................................................................................... viii
ABSTRACT....................................................................................................................... ix
CHAPTER ONE: INTRODUCTION ..............................................................................1
1.1 Background to the Study................................................................................................1
1.1.1 History and Evolution of Plastic Money in Kenya .....................................................2
1.2 Statement of the Problem...............................................................................................5
1.3 The Objective.................................................................................................................6
1.4 Importance of the study .................................................................................................6
CHAPTER TWO: LITERATURE REVIEW.................................................................7
2.1 Introduction....................................................................................................................7
2.2 Theoretical Review ........................................................................................................7
2.2.1 Financial Economics Approach ............................................................................7
2.2.2 Agency theory.......................................................................................................8
2.2.3 New Institutional Economics................................................................................9
2.2.4 Stakeholder Theory.............................................................................................10
2.3 Determinants of credit card default risk.......................................................................10
2.3.1 The effect of Card-holder Characteristics on Credit Card Default .....................10
2.3.2 Credit Card Characteristics that Affect Credit Card Default ..............................15
2.3.3 The Effect of Behavioral Scoring Process on Credit Card Default ....................19
2.4 Empirical Evidence on Credit card Risk of Default in Developed Markets................22
2.5 Empirical Evidence on Credit Card Default Risk in Kenya ........................................23
2.6 Chapter Summary ........................................................................................................24
v
CHAPTER THREE: RESEARCH METHODOLOGY ..............................................25
3.1 Introduction..................................................................................................................25
3.2 Research Design...........................................................................................................25
3.3 Target Population.........................................................................................................25
3.3 Data and Data Collection Methods ..............................................................................26
3.4 Models specification ....................................................................................................26
3.4.1 Conceptual Models .............................................................................................26
3.4.2 Analytical Model ................................................................................................28
3.5 Data Analysis ...............................................................................................................29
3.6 Data Reliability and Validity Controls ........................................................................30
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION .................31
4.1 Introduction..................................................................................................................31
4.2 Summary Statistics.......................................................................................................31
4.2.1 Length of Service ..............................................................................................31
4.2.2 Management Level ............................................................................................32
4.3. The Relationship between Credit Risk of Default and Mitigation Approaches .........35
4.3.1 The Effect of Card-Holder Characteristics on Credit Card Default ...................35
4.3.2 Credit Card Characteristics that Affect Credit Card Default ..............................39
4.3.3 The Effect of Behavioral Scoring Process on Credit Card Default ....................42
4.3.4 What can be done to Reduce Credit Card Default..............................................44
4.4 Discussion ....................................................................................................................47
4.4.1 Summary of the Card-Holder Characteristics on Credit Card Default ...............47
4.4.2 Summary of Credit Card Characteristics that Affect Credit Card Default .........49
4.4.3 Summary of the Effect of Behavioral Scoring Process on Credit Card Default.51
4.4.4 Summary of what can be done to Reduce Credit Card Default..........................52
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ...54
5.1 Introduction..................................................................................................................54
5.2 Summary of the Study .................................................................................................54
5.3 Conclusion ...................................................................................................................55
vi
5.3.1 The Effect of Card-Holder Characteristics on Credit Card Default ...................55
5.3.2 Credit Card Characteristics that Affect Credit Card Default ..............................56
5.3.3 The Effect of Behavioral Scoring Process on Credit Card Default ....................56
APPENDICES ..................................................................................................................64
APPENDIX I: Questionnaire.............................................................................................64
APPENDIX II: List of Commercial Banks in Kenya .......................................................69
APPENDIX III: Correlation Data......................................................................................71
vii
LIST OF TABLES
Table 4.2.1 Years Worked at Card Centre.........................................................................31
Table 4.2.2 Management Level .........................................................................................32
Table 4.2.3 Years of Experience Managing Credit Cards .................................................33
Table 4.2.4 Percentage of shopping expenses Charged to Credit Cards ...........................34
Table 4.2.5 Percentage of Credit Obligation is carried Forward Every Month.................34
Table 4.2.6 Card Holder’s Level of Income ......................................................................35
Table 4.3.1 The Effect of Card-Holder Characteristics on Credit Card Default ...............36
Table 4.3.2 Credit Card Characteristics that Affect Credit Card Default..........................40
Table 4.4.1 Card-Holder Characteristics on Credit Card Default......................................47
Table 4.4.2 Credit Card Characteristics that Affect Credit Card Default..........................49
Table 4.4.3 Behavioral Scoring Process on Credit Card Default ......................................51
Table 4.4.4 Credit Card Default Mitigation Approaches...................................................52
viii
ABSTRACT
The Development of credit card industry has been a laudable success. However, the
environment that Commercial Banks in Kenya operate in is full of a considerable number
of numerous risks and uncertainties. The risks include credit risk, operational risk, and
liquidity risk, among other risks. Credit risk is one of the major risks that Banks have to
content with. This is due to the fact that lending is the backbone of all Banks business.
The objective of this study is look at the Relationship between Credit Card Default Risk
and Cardholders Characteristics, Credit Card Characteristics, Behavioral Scoring Process
among Commercial Banks in Kenya and how they mitigate against credit card Default
Risk. As a matter of fact, credit card business in Kenya is still not fully developed. The
lending Banks ensure personal credit applications presented for approval are duly
analyzed and related lending risks identified and also Monitoring of excesses. Banks
always focus on a customer’s ability to pay the credit card loan. Customers are required
to furnish the Bank with all requisite information to assess their creditworthiness.
However, most of the lending practices border the traditional approaches of appraising
and managing credit risk of conventional loans yet credit card loans are very unique as
discussed in the paper. Commercial Banks should stick to the guidelines put in place by
the Central Bank of Kenya and other Credit Card Payment Companies such as Visa and
MasterCard. The Central Bank of Kenya should equally restructure policy papers
addressing the unique credit risk of default of credit cards. They should also come up
with their own internal mechanisms to manage credit card default risk depending on the
kind of credit card loans they advance.
In summary, the entire study will seek to answer the question “The Relationship Between
Credit Card Default Risk and Cardholders characteristics, Credit Card Characteristics and
Behavioral Scoring Process Among Commercial Banks in Kenya?” By and large The 17
Commercial Banks issuing credit cards and any entering the credit card business should
develop proper and accredited credit risk management techniques/methods which will
assist in coming up with sound credit policies which to a large extent will reduce the high
levels of bad loans as a result of credit card default.
ix
CHAPTER ONE
INTRODUCTION
1.1 Background to the Study
The wider distribution of credit cards, what some have called the Democratization of
Credit has a host of benefits. The new card holders get a convenient form of payment and
a line of credit, while the banks earn fees and interest. However, the same
Democratization may have a downside due to the potential risk of loan defaulting.
A credit card system is a type of retail transaction settlement and credit system named
after the small plastic card issued to the user of the system. A credit card is therefore,
simply a plastic card issued by a commercial bank or any other institution that allows the
holder to purchase goods and services on credit up to an agreed limit set at specific place
where these cards are accepted. A credit card is a financial instrument that allows the
cardholder to obtain funds at interest from a financial institution at his/her own discretion,
up to some limit (Edward Paul and Robert, 1997)
In modern business transactions, credit cards are increasingly becoming an essential tool.
A credit card offers a cardholder convenience safety, higher purchasing power and a host
of fringe benefits as most cards come with a number of privileges. This is over and
above the basic benefits of serving in place or cash. However, screening out credit risky
customers is a crucial step in card application acceptance process (Mbijiwe J.M.2005). If
repaid within a certain period usually within a month, the loan interest is free. If not, the
loan may be carried for an indefinite period, always accruing new interest charges, by
paying a minimum amount each month.
A credit card is distinguished from other financial instruments by the entitlement. It
gives borrowers to determine the size of the loan and the pace at which it is repaid and as
flexible and readily available source of funds for consumption, may be used as a shield
against the hardships of income loss (Asubu, 1991).
1
Credit card plays a role in the strategic plans of many banks (comptroller’s handbook,
October, 1996). A bank can be a card issuer, merchant acquirer or agent bank when it
comes to credit card business. Issuing banks bear the risk because they hold or sell credit
card loans. A merchant bank enters into agreement with the merchant to accept deposits
generated by credit card transactions. It is possible that a merchant bank is exposed to
some transaction risk arising from customers’ change of banks. An agent bank agrees to
participate in another bank’s credit card program. This requires that the agent bank turn
over its applications for credit card to the bank administering the program.(comptroller’s
handbook, October, 1998).
In Kenya today commercial banks and petroleum, companies are the main issuers of
different forms of plastic money. Commercial banks – ATM cards, visa electron debit
card, the total voyage fueling cards and the Barclay card are some form of plastic money
that exist in the Kenyan market. (The Daily Nation, 30th March, 2004).
The paper has investigated the relationships of default on credit card debt by users of
credit cards in Kenya. It focuses on the relationship between default and outcome of
financial choice consumers make within the constraints of the contract terms set by credit
card issuer and looks into factors therefore will play part in determining default. The
project has attempted to obtain information on behavioral aspects of the credit card users
in Kenya.
1.1.1 History and Evolution of Plastic Money in Kenya
Electronic payments technology can substitute not only for checks, but also for cash, in
the form of electronic money (e-money)-money that exists only in electronic form. The
first form of e-money was the debit card. Debit card which look like credit cards, enable
consumers to purchase goods and services by electronically transferring funds directly
from their bank accounts to merchants’ account. (Mishkin F.S and Eakins S.G. 2010)
2
According to Timberlake (1987), the lack of adequate denominations of cash in the USA
currency as well as the importance of coal mining and lumbering in the 1885, stimulated
the private production of money, which was known as scrip money. He considered the
scrip to be an underdeveloped form of plastic money as it took the dimensions of a fuel
and pre-paid card of entertainment cards, because it was honored at local general stores
(fuel prepaid cards) and entertainment cards are honored only at establishments that have
issued these cards. Scrip money took the form of printed cards, which were replaced by
metallic money. He states that scrip money was developed to serve as a medium of
exchange due to the fact that regions around the coal mines were hilly and with marginal
agriculture and commercial development. The mining companies set up to establish
infrastructure, residence, churches, schools, water works and company stores or
commissionaires.
All those developments led to the birth of first modern credit card issued by diners club in
1950 that was developed by two Americans namely; Frank Mcnamara and Ralph
Schneider. Interestingly, in the year before that, Mcnamara had dined in restaurant in
New York, after the meal he realized that he had forgotten his wallet, and his wife had to
pay for him to get him put of the embarrassing situation. This incident made him
determined to come up with a payment system that requires a card to pay for all the
purchases (Dinors club website July, 2007).
Amid significant strides in the development of cash-less societies especially in
Developed economies, emerging markets remain behind. The Kenyan payment system is
still dominated by paper based instruments such as cash, checks and in some parts
commodity money. This remains to be the key yardsticks of settling indebtedness in
Kenya. In 1984, the Southern Credit Banking Corporation issued a credit card called the
Senator; in 1990 Barclays Bank introduced the Barclaycard, in 1995 Kenya Commercial
Bank issued its first credit Card and in 1996 Commercial Bank of Africa issued its Credit
Card and many more Credit Cards. These include; Cooperative Bank, NIC Bank, Fidelity
Commercial Bank, Prime bank, National Bank, CFC Bank, Imperial Bank, Post bank and
I & M Bank (Mucheru S. 2008)
3
Kenya is Visa’s fastest growing market in Africa outside South Africa, with $452 million
processed through the vis Credit and Electron debit cards in 2003.that was 43 percent
growth over the previous year, increasing the number of visa cards in the market to over
557,000 with acceptance in over 500,000 outlets. Based on the phenomenal growth over
the past 18 months, we anticipate over 2 million cards will be in use in Kenya within the
next three years, ‘said Mr. Winter (Visa International).
Banking Industry in Kenya
Commercial banks are licensed and regulated under the Banking Act, Cap 488 and
Prudential Regulations issued there-under. There are currently 45 commercial banks in
Kenya. Out of the 45 institutions, 33 are locally owned and 12 are foreign owned. The
locally owned financial institutions comprise 3 banks with significant government
shareholding and 28 privately owned commercial. The foreign owned financial
institutions comprised 8 locally incorporated foreign banks and 4 branches of foreign
incorporated banks. Of the 42 private Banking institutions in the sector, 71% are locally
owned and the remaining 29% are foreign owned (CBK, 2010).
The Domestic credit provided by banking sector (% of GDP) in Kenya was reported at
40.09 in 2008, according to the World Bank. The Commercial Banks have been selected
for the study because of the recent emphasis on Risk Management and the increasing
levels on credit card default among the commercial banks in Kenyan. Financial
liberalization was initiated in the 90s to make the banking system profitable, efficient,
and resilient. The liberalization measures consisted of deregulation of entry, interest rates,
and branch licensing, as well as encouragement to state owned banks to get listed on
stock exchanges. With the liberalization came risks that banks needed to manage. It is
therefore a suitable time to perform an analysis of the determinants of credit card default
and credit card risk management strategies employed by Commercial Banks in Kenya.
The Basel-II norms, which include a move towards better risk management practices,
also necessitate such a study (CBK, 2010).
4
1.2 Statement of the Problem
Much of the early work on consumer debt focused on traditional loans which are unlike
credit card loans in several key respects. Jaffe and Russell (1976), Stiglitz and Weiss
(1981), Kegode (2006) conducted studies on credit risk of default of traditional loans.
Whereas traditional loans involve predetermined loan amounts and fixed payments
schedules, with credit card loans, the actual borrowing is at the consumer’s discretion
after receiving a fixed line of credit. Debt repayment on credit cards is flexible, with
monthly repayment being fixed on the total balance.
Secondly, unlike many traditional loans, credit card borrowing does not require
consumers to post collateral which may place a greater risk on the lender. Jaffee and
Russell (1976) and Stiglitz and Weiss (1981), as well as others, studied the tradition loan
market theoretically using the tools of asymmetric information and adverse selection.
However, with the growth of credit card debt in the U.S. economy in the last decade,
researchers have increasingly turned their attention to various aspects of this unique
credit instrument, Ausubel (1991), who was one of the first to carry out an empirical
study of this market, found that abnormally high profits and high and sticky interest rates
exist in the industry in spite of its seemingly competitive structure with over 6000 card
issuers. He speculated that search/switching costs and type of irrational consumer
behavior might be involved in these paradoxical market outcomes.
The closest local studies so far in regards to credit cards were by Mbijiwe (2005) on
application of discriminant model of credit scoring process; a case of Barclaycard and
Mucheru (2008) on investigation into credit card risk management a case study of
Imperial Bank.
It is evident from the above review that most of the studies were conducted in developed
countries. There are limited studies targeting the emerging markets like Kenya. The local
studies conducted examine individual commercial banks. This study therefore sought to
examine factors influencing credit card default risk among the commercial banks in
Kenya. It answers the following research question:
5
What is the relationship between cardholder characteristics, credit card characteristics,
behavioral scoring process and credit card default risk among commercial banks in
Kenya?
1.3 The Objective
The objective of this study was to find out the relationship between credit card default
risk and cardholder characteristics, credit card characteristics, behavioral scoring process
and credit card default risk among commercial banks in Kenya. Secondly, the study
sought to establish the methods that Commercial Banks in Kenya use to mitigate against
credit card default and how to robust on the same.
1.4 Importance of the study
In order to take adequate measures to revert credit card default trends, policymakers need
to have a clear understanding of the factors that are more likely to have caused increase in
credit card defaults. This study sheds more light on the factors antecedent to credit card
default. The study aids in understanding the sudden surge of credit card loan default in
commercial banks in Kenya and determines the different credit card management
techniques employed by the commercial Banks.
The research informs on the commercial bank’s decisions regarding the maximum
permissible credit that they should reasonably allow for each card holder. The study
elucidates on more robust ways of the credit risk management approaches to be employed
in managing credit card debt owing to the uniqueness of the same as compared to
traditional loans. The research enlightens card holders on the factors that impact on their
defaulting which would help them make informed decisions when utilizing the credit card
facility. The study adds to available pool of knowledge in credit card risk management
since the area is still suffering from a dearth of information.
6
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter reviews literature by previous scholars and authors on the topic of study.
Section 2.2 examines the Factors Influencing Credit Card Default Risk. Section 2.3
examines the Empirical Evidence on Credit Card Default Risk in Developed Markets.
Section 2.4 focuses on the Empirical Evidence on Credit Card Default Risk in Kenya.
Section 2.5 presents Summary on The Literature Review.
2.2 Theoretical Review
Four theories are relevant in risk management and credit card risk management and are
therefore discussed. These are the financial economics theory, the agency theory, the new
institutional economics theory, and the stakeholder theory.
2.2.1 Financial Economics Approach
Financial economics approach to corporate risk management has so far been the most
prolific in terms of both theoretical model extensions and empirical research. This
approach builds upon classic Modigliani-Miller paradigm (Miller and Modigliani, 1958)
which states conditions for irrelevance of financial structure for corporate value. This
paradigm was later extended to the field of risk management. This approach stipulates
also that hedging leads to lower volatility of cash flow and therefore lower volatility of
firm value. Rationales for corporate risk management were deduced from the irrelevance
conditions and included: higher debt capacity (Miller and Modigliani, 1963), progressive
tax rates, lower expected costs of bankruptcy (Smith and Stulz, 1985), securing internal
financing (Froot et al., 1993), information asymmetries (Geczy et al., 1997) and
comparative advantage in information (Stulz, 1996). The ultimate result of hedging, if it
indeed is beneficial to the firm, should be higher value-a hedging premium.
7
Evidence to support the predictions of financial economics theory approach to risk
management is poor. Although risk management does lead to lower variability of
corporate value (e.g. Jin and Jorion, 2006), which is the main prerequisite for all other
effects, there seems to be little proof of this being linked with benefits specified by the
theory. One of the most widely cited papers by Tufano (1996) finds no evidence to
support financial hypotheses, and concentrates on the influence of managerial preferences
instead. On the other hand, the higher debt capacity hypothesis seems to be verified
positively, as shown by Faff and Nguyen (2002), Graham and Rogers (2002) and Guay
(1999). Internal financial hypothesis was positively verified by Guay (1999) and Geczy et
al. (1997), while it was rejected by Faff and Guyen (2002) and Mian (1996). Judge
(2006) found evidence in support of financial distress hypothesis. Tax hypothesis was
verified positively by Nance, Smith and Smithson (1993), while other studies verified it
negatively (Mian, 1996; Graham and Rogers. 2002). More recently Jin and Jorion (2006)
provide strong evidence of lack of value relevance of hedging, although some previous
studies have identified a hedging premium (Allayannis and Weston, 2001, Carter et al.,
2006).
2.2.2 Agency theory
Agency theory extends the analysis of the firm to include separation of ownership am
control, and managerial motivation. In the field of corporate risk management agency
issue have been shown to influence managerial attitudes toward risk taking and hedging
(Smiti and Stulz, 1985). Theory also explains a possible mismatch of interest between
shareholder management and debt holders due to asymmetries in earning distribution,
which can result in the firm taking too much risk or not engaging in positive net value
projects (Mayers and Smith, 1987). Consequently, agency theory implies that defined
hedging policies can have important influence on firm value (Fite and Pfleiderer, 1995).
The latter hypotheses are associated with financing structure, and give predictions similar
to financial theory.
8
Managerial motivation factors in implementation of corporate risk management have
been empirically investigated in a few studies with a negative effect (Faff and Nguyen,
200 MacCrimmon and Wehrung, 1990; Geczy et al., 1997). Notably, positive evidence
was found. However, according to Tufano (1996) in his analysis of the gold mining
industry in the US. Financial policy hypotheses were tested in studies of the financial
theory, since both theories give similar predictions in this respect. All in all, the bulk of
empirical evidence seems to against agency theory hypotheses however. Agency theory
provides strong support for hedging as a response to mismatch between managerial
incentives and shareholder interests.
2.2.3 New Institutional Economics
A different perspective on risk management is offered by new institutional economics.
The focus is shifted here to governance processes and socio-economic institutions that
guide these processes, as explained by Williamson (1998). Although no empirical studies
of new institutional economics approach to risk management have been carried out so far,
the theory offers an alternative explanation of corporate behavior. Namely, it predicts that
risk management practices may be determined by institutions or accepted practice within
a market or industry. Moreover, the theory links security with specific assets purchase
(Williamson, 1987), which implies that risk management can be important in contracts
which bind two sides without allowing diversification, such as large financing contract or
close cooperation within a supply chain.
If institutional factors do play an important role in hedging, this should be observable in
the data. First of all, there may be a difference between sectors. Secondly, hedging may
be more popular in certain periods-in Poland one might venture a guess, that hedging
should become more popular with years. A more concrete implication of this theory is
that shareholders may be interested in attracting block ownership by reducing company
risk. Here NIE is similar in its predictions to agency theory. However this theory also
suggests that firm practices may be influenced by the ownership structure in general.
9
2.2.4 Stakeholder Theory
Stakeholder theory, developed originally by Freeman (1984) as a managerial instrument,
has since evolved into a theory of the firm with high explanatory potential. Stakeholder
theory focuses explicitly on equilibrium of stakeholder interests as the main determinant
of corporate policy. The most promising contribution to risk management is the extension
of implicit contracts theory from employment to other contracts, including sales and
financing (Cornell and Shapiro, 1987). To certain industries, particularly high-tech and
services, consumer trust in the company being able to continue offering its services in the
future can substantially contribute to company value. However, the value of these
implicit claims is highly sensitive to expected costs of financial distress and bankruptcy.
Since corporate risk management practices lead to a decrease in these expected costs,
company value rises (Klimczak, 2005). Therefore stakeholder theory provides a new
insight into possible rationale for risk management. However, it has not yet been tested
directly. Investigations of financial distress hypothesis (Smith and Stulz, 1995) provide
only indirect evidence (e.g. Judge, 2006):
2.3 Determinants of credit card default risk.
2.3.1 The effect of Card-holder Characteristics on Credit Card Default
2.3.1.1 Gender and credit card default
According to Abdul-Muhmin and Umar (2007), the tendency to revolve is significantly
higher among males. The relationship with number of cards owned is curvilinear, with
those who own two cards being the most likely to revolve. However, contrary to previous
findings, they note that the probability of card ownership in Saudi Arabia is higher
among the female population. Indeed, Pirog and Roberts (2007) analyzed credit card
misuse scores to determine the effects of demographic variables. Mean credit card scores
were essentially the same for men and women (p > 0.15). This suggests that research on
gender differences is inconclusive.
10
2.3.1.2 Age and credit card default
According to Wickramasinghe and Gurugamage (2009), age has been found to be one of
the significant demographic and socio-economic characteristic in describing consumer
credit card practices. On the effect of age, both positive and curvilinear relationships have
been suggested. In the latter case, the evidence suggests that usage intensity is heavier
among middle-aged consumers than lower- and old-aged consumers (Abdul-Muhmin and
Umar, 2007). However, credit cards are particularly problematic for young adults
(Joireman, Kees and Sprott, 2010). It is estimated that 91% of college seniors have at
least one credit card and 56% carry four or more cards. The average college student will
graduate with more than $2,800 in credit card debt and up to one- fifth carry a credit card
debt of $10,000 or more (Mae, 2005).
Pirog and Robert (2007) in their study established that the correlation between age and
credit card scores was found to be significant (r = 0.147, p < 0.05). Hamilton and Khan
(2001) conducted a research using Linear Discriminant Analysis and Logistic Regression
on a sample of 27,681 bank credit card holders who had held and used their card in the 14
month sample period to identify the characteristics of active card holders with the
greatest propensity to revolve (i.e. pay interest). Their results established that people aged
under 35 were significantly more likely to become revolvers and the older one gets, the
less likely they are to revolve.
2.3.1.3 Income and credit card default
The shift of consumer debt from installment debt to credit card debt, combined with the
jk pattern of credit card pricing, has made consumers’ debt burdens much more sensitive
to changes in income. When consumers’ incomes are high, they are likely to pay their
credit card bills in full, and therefore their debt burden is low and they pay little or no
interest. But when incomes decline, consumers are likely to pay late or to pay the
minimum on their credit cards, so that their debt burdens increase and they pay much
more in interest and fees. Although credit cards allow consumers to smooth consumption
when their incomes fall, the cost of doing so is extremely high and may cause some
debtors to enter a state of ongoing financial distress (White, 2007).
11
Unpredictable expenses can lead people into credit card debt. And once people take on
debt, the credit card companies react by doing at least one of the following: (1) increase
interest rates; (2) charge fees/penalties; and (3) increase credit limits; all of these actions
help to raise the likelihood a debtor will not pay off their debt quickly. Therefore, trying
to change people’s attitudes toward over-consumption will not solve this problem,
because buying basic necessities is not over-consumption but merely maintaining
existence. In other words, for many credit card debtors, over-borrowing is due to a lack of
sufficient income (Scott, 2007). A majority of card users are “credit users,” who have
relatively high propensities to consume and have limited monetary assets—otherwise
they would not continue to pay high interest rates on unpaid balances (Stauffer, 2003).
There is evidence that the ownership and use of credit cards by low-income families has
increased and credit card holders have become more risky. It has also been shown that
credit card companies have taken greater risk to earn abnormal returns and credit card
debt is related to bankruptcy filings (Kidane and Mukherji, 2004).
2.3.1.4 Education and credit default
Information on education is usually accurately known by banks and provides key
background information that can influence their rate offer. They are likely to influence
search costs as well (Kim, Dunn and Mumy, 2005). A study conducted by Lopes (2008)
found out that the default rate is decreasing in the education level and households with
less education are more likely to borrow strategically. On the other side, if households
with college education are considered, the results described are reversed. Typically,
financial education includes background on economics which relates to the choices we
make in a world where we can't have everything we want and the consequences of those
choices (Roberts, 2005).
2.3.1.5 Lifestyles and credit default
The subject of consumer credit is closely connected to philosophical debates over what
constitutes a socially and culturally appropriate level of material affluence and how to
distinguish between opulent and frugal lifestyles (Cohen, 2005). Credit and money
attitudes of individuals are good indicators of individual’s spending patterns; his/her
12
perceived economic wellbeing and acceptable debt levels. When need for security, safety
and sustenance are not fully satisfied, people place a strong focus on materialistic values,
desires and turn to buying in an attempt to share up or claim status (Alex and
Raveendran, 2008). Credit cards allow many people the ability to reach what, in their
minds, equates to living in the next higher class level. Today, more than at any other
period in history, the prevalence of conspicuous consumption is highest because of the
vast number and variety of goods and services available in the economy (Scott, 2007).
2.3.1.6 Locus of Control and credit default
It has been argued that an individual’s locus of control play an important role in
indebtedness. A review by Erdem (2008) established that unsuccessful credit users
appeared to have greater external locus of control, lower self-efficacy, considered money
as a source of power and prestige, took fewer steps to retain their money, shows lower
risk-taking and expressed greater anxiety about financial situations than successful ones.
According to Perry (2008), psychologists regard locus-of-control as a key personality
variable due to its link to motivation and performance in a wide variety of settings.
Consumers’ locus of control is likely to affect their payment behavior above and beyond
the influence of financial resources or situational circumstances. Individuals with an
internal locus of control, generally expect that their actions will produce predictable
outcomes and thus are more action-oriented than externals. Individuals with an external
locus of control perceive events as being under the control of luck, chance or powerful
others, and as such are less likely than internals to master the skills necessary to
accomplish their goals. A research conducted by Yang, James and Lester, (2005)
revealed that the number of credit cards owned was positively associated with affective
and behavioral attitudes toward credit cards. This was found for the simple correlations
and for the partial correlations after controlling for age and gender.
2.3.1.7 Compulsiveness and credit default
The study by Joireman et al. (2010) suggest that a person scoring high in compulsive
buying is in considerably more danger of accumulating large amounts of debt if that
13
person is also highly concerned with the immediate consequences of his or her actions.
Compulsive consumption has been defined as ‘a response to an uncontrollable drive or
desire to obtain, use or experience a feeling, substance or activity that leads an individual
to repetitively engage in a behaviour that will ultimately cause harm to the individual
and/or others’ (Norum, 2008). Consumers make choices about activities they will engage
in that will enhance or hamper their future wellbeing. Consumers who engage in risktaking behaviours, are more limbkely to have present-orientation rather than a future
orientation (Finke and Huston, 2003). They are more likely to desire immediate
satisfaction rather than delay gratification. Consumers become more short-sighted as their
time preference for the present become greater. In economic terms, they have a high
discount rate for future utility. Thus, in certain cases, it is ‘rational’ for consumers to
ignore the long-run consequences of their choices (Finke and Huston, 2003).
2.3.1.8 Other Cards Held and credit default
There are some consumers seeing credit cards as a lifestyle choice rather than a method
of payment. These people generally hold more than one credit card (Tunal and Tatoglu,
2010). In recent years, there has been a dramatic growth in credit card offers, both in
terms of quantity and credit card features. Nowadays, it is not uncommon for one
household to own more than one credit card (Lope, 2008). An increase in the number of
cards on which a consumer has reached the borrowing limit is also found to increase
default (Kim, Dunn and Mumy, 2005). For instance, a research conducted by Johnson
(2001) established that three-quarters of bankrupts in US had at least one credit card
within a year after filing.
According to Pirog and Roberts (2007), the relationship between materialism and credit
card use is straightforward. Those more desirous of material possessions are particularly
conscious of the possessions of others. Pursuing materialistic ideals is a competitive and
comparative process. To achieve a position of social power or status, one must exceed the
existing community norm. As long as others are also attempting to signal their social
power through possessing and displaying material goods, the level of goods required to
make a powerful social statement continually rises. A logical result, according to Roberts
14
(2007) is increasing credit card misuse as one attempts to “keep up with the Joneses.”
According to Cohen (2005), interpersonal comparison is an important gauge of life
satisfaction and readily available consumer credit allows people to lead lifestyles, at least
for a time, beyond their immediate financial means.
2.3.1.9 Assets Held and credit default
Previous research suggests that there is a relationship between the level of assets held by
card holders and credit card default. According to Lopes (2008), the default rule is always
such that there is an optimal asset level below which default is triggered. In other words,
if in a given period the consumer has a low-income realization, outstanding debt is at its
limit, and his assets are below an equilibrium trigger level, he chooses to default.
2.3.1.10 Loans Held and credit card default
A study conducted by Hamilton and Khan (2001) found out that people who held other
interest-charging products (i.e. a loan) were more likely to become revolvers. According
to Roszbach (2004) a lending institution's decision to grant a loan or not and its choice of
a specific loan size can greatly affect households' ability to smooth consumption over
time, and thereby their welfare. At a more aggregate level, consumer credit makes up a
significant part of financial institutions' assets, and the effects of any loan losses on
lending capacity will be passed through to other sectors of the economy that rely on
borrowing from the financial sector. For this reason, the properties and efficiency of
banks' credit-granting process are of interest not merely because the factors determining
the optimal size of financial contracts can be examined. At least as important are the
implications these contracts have for the welfare of households and the stability of
financial markets. Thus it is important for credit card payment companies to consider the
interest –charging products revolvers are carrying to avoid potential credit card defaults.
2.3.2 Credit Card Characteristics that Affect Credit Card Default
2.3.2.1 Interest Rates and Credit Card Default
The widespread use of credit cards has raised concerns whether consumers fully
understand the costs and implications of using credit cards and whether credit cards have
15
encouraged widespread over-indebtedness, particularly among those least able to pay
(Durkin, 2000). Banks have found that cardholders will respond positively to an offer of a
very low interest rate (often zero) for an introductory period. However, once credit card
debt is established, a combination of high interest rates, fees, and insufficient income
usually keeps people from paying off their debt (Peterson, 2001). The high interest rates
and penalties can quickly multiply the original debt, so that a modest number of
purchases can leave consumers deeply mired in debt (Littwin, 2008). According to Scott
(2007), a distinguishing insight provided by Veblen is that credit card companies extend
credit to people arbitrarily, and when people fail to pay their credit card balance in full
even once, they raise defaulters’ interest rates to often absurdly high levels.
Kim, Dunn and Mumy (2005) proceeded to test a theoretical model which shoed that a
consumer's credit card interest rate does not depend solely on risk class but rather on a
complex balance of several features of the credit card market. The main feature behind
this complexity is differences in search incentives among consumers. Because of
differences in search incentive, identically risked cardholders with a borrowing motive
will actually end up having lower interest rates in equilibrium than the average interest
rate of their counterparts in the same risk pool who have only a transactions motive. They
found out that card issuers are more likely to assign higher interest rates to defaulters, and
a high interest rate could possibly contribute to a cardholder's default.
2.3.2.2 Penalty Fees and Credit Card Default
According to Desear (2009), new cardholders generally do not expect to pay penalty fees,
such as the fees levied for late payments or the fees imposed for exceeding the particular
cardholder's credit limit. Consequently, banks have been able to increase these penalty
fees, in many cases to multiples of what they had been in earlier periods, without
materially cutting into card origination and retention volumes. In agreement, Scott (2007)
further argues that the credit card companies often compound the problem of default
problem by charging penalties and fees and increasing debtors’ credit limits. Using one of
Veblen’s metaphors, Scott recounts that credit card debt accumulation starts a
parasite/host relationship between credit card companies and their indebted borrowers.
16
Once this relationship is established, the parasites (credit card companies) drain their
hosts (borrowers) of money by charging numerous fees and penalties, which account for
over $90 billion in revenue for credit card companies each year. And this relationship is
dissolved only when the borrowers free themselves of their credit card companies.
While credit card borrowing is debt for consumers it equates to surplus profits for
businesses, and businesses get this additional profit without increasing incomes. These
companies do not make money on people that pay off their balances in full each month.
Profits are made off people who accumulate considerable debt. Over 30 percent of credit
card companies’ profits are generated from penalty fees; this number has more than
doubled in ten years. Therefore, it is sensible for companies to maximize borrowers’
financial indiscretion in any manner possible (Scott, 2007).
2.3.2.3 Hidden Costs and Credit Card Default
As a credit instrument, credit cards are inherently more costly than other credit types. To
begin with, as they are uncollateralized, loans extended through credit cards expose banks
to higher default risk. Credit cards also entail high liquidity risk. Banks commit to
lending any amount up to the credit card limit, and the utilization of this credit, by
withdrawing cash for instance, is solely at the discretion of consumers (Akin, et al. 2010).
2.3.2.3 Ease of Access to Credit and Credit Card Default
According to White (2007), until the 1960s, consumer credit generally took the form of
mortgages or installment loans from banks or credit unions. Obtaining a loan required
going through a face-to-face application procedure with a bank or credit union employee,
explaining the purpose of the loan, and demonstrating ability to repay. Because of the
costly application procedure and the potential embarrassment of being turned down, these
loans were generally small and went only to the most creditworthy customers. This
changed with the introduction of credit cards in 1966, since credit cards provided
unsecured lines of credit that consumers could use at any time for any purpose.
17
Scott (2007) suggests that when people are given easy access to credit many of them will
use it out of necessity, want, or both. There is a large portion of credit card debtors who
over spend using credit cards simply because they are given credit too easily without
consideration to whether they can really handle the amount of credit issued. For instance,
Wickramasinghe and Gurugamage (2009) noted that despite the substantial risks to
lenders that they will be unable to pay their bills on time, working and middle-class
families often pay high rates of interest.
Further, White (2007) argue that credit card loans, in contrast, allow lenders to change
the interest rate at any time and allow debtors to choose how much they repay each
month, subject to a low minimum repayment requirement. Consumers who repay in full
each month use credit cards only for transacting; they receive an interest-free loan from
the date of the purchase to the due date of the bill. In contrast, consumers who repay less
than the full amount due each month use credit cards for both transacting and borrowing;
they pay interest from the date of purchase. If borrowers pay late or exceed their credit
limits, then lenders raise the interest rate to a penalty range and impose additional fees.
2.3.2.4 Convenience and Credit Card Default
Consumers have different motives for holding credit cards. Some hold them for their
convenience as a payment instrument, while others hold them as a means of obtaining
revolving credit to finance consumption. These variations in motives affect the extent to
which cardholders actually charge purchases to the cards, the intensity of such usage,
situations in which the cards are used, and the particular products purchased (AbdulMuhmin and Umar, 2007). While credit cards are a convenient way to pay for products
and services, consumers can sometimes use credit unwisely, carry high balances, and
frequently pay only the minimum amount on each card they hold (Joireman, Kees and
Sprott, 2010). According to King (2004), there is also evidence that would caution
against believing that the total effect of holding a credit card on money demand is due to
convenience usage. People are also using credit cards as a means of borrowing, which
suggests that at least some credit card-holders may have a higher propensity to consume
than do non-card-holders.
18
2.3.2.5 Transaction Rewards and Credit Card Default
According to White (2007), over time, competition among issuers has led them to offer
increasingly favorable introductory terms and increasingly onerous post-introductory
terms. The favorable introductory terms include zero annual fees, low or zero
introductory interest rates on purchases and balance transfers, and rewards such as cash
back or frequent flier miles for each dollar spent. The favorable introductory terms
encourage consumers to accept new cards, while the rewards programs encourage them
to charge more on the cards and the low minimum repayment requirements encourage
them to borrow. The format of the monthly bills also encourages borrowing, since
minimum payments are often shown in large type while the full amount due is shown in
small type. Minimum monthly payments are low – typically the previous month’s interest
and fees plus 1 percent of the principle – which means that debtors who pay only the
minimum each month still owe nearly half of any amount borrowed after five years. After
the introductory period, terms become much more onerous: the average credit card
interest rate is 16 percent, interest rates rise to 24 to 30 percent if debtors pay late, and
penalty fees for paying late or exceeding the credit limit are around $35. In addition,
Lopes (2008) observes that there are credit cards with and without annual fee, with a low
introductory rate that give cash-back, air miles, and so on. All these rewards make credit
card transactions so attractive that cardholders overlook any impending costs
2.3.3 The Effect of Behavioral Scoring Process on Credit Card Default
2.3.3.1 Minimum and Maximum Balances and Credit Card Default.
It has been assumed that credit card banks cannot observe a direct measure of risk types,
and therefore they have reasonably taken balance size to be the major indictor of default
risk (Kim, Dunn and Mumy, 2005). Kim, et al. (2005) found in their analysis that in any
of the possible equilibria, the risk pool of credit cardholders who have a borrowing
motive will end up with a lower interest rate than their counterparts in the same risk pool
who have only a convenience motive in using their cards (i.e., who do not borrow). This
results from the greater search incentive of the borrowers. They also found that when the
interaction of banks and cardholders is properly controlled, the size of cardholder's total
balance will on net negatively affect the average percentage rate to which he or she is
19
subject, because presumably higher balances give a greater incentive to search for a lower
interest rate.
2.3.3.2 Number of Missed Payments and Credit Card Default
According to White (2007), consumers fall into two groups based on their attitudes
toward saving: rational consumers versus hyperbolic discounters. Rational consumers
apply the same discount rate over all future periods. Hyperbolic discounters, in contrast,
want to save more starting at some point in the future, but in the present they prefer to
consume rather than save. In another context, a hyperbolic discounter can be a person
who always wants to start dieting tomorrow, but never today. As credit card loans have
become more widely available and borrowing opportunities have increased, the
difference between rational consumers and hyperbolic discounters has become more
important.
Laibson, Repetto, and Tobacman (2003) found in simulations that hyperbolic discounters
borrow more than three times as much as rational consumers, regardless of whether both
types pay the same interest rate or hyperbolic discounters pay higher rates. Applying
these results to credit card pricing suggests that rational consumers are likely to use credit
cards purely for transacting, while hyperbolic discounters are more likely to use them for
borrowing. Also, allowing consumers to choose how much to pay on their credit cards
each month makes it likely that hyperbolic discounters will accumulate high levels of
credit card debt, because each month they resolve to start paying off their debt, but when
the next bill arrives they consume too much and postpone repaying for another month.
Because hyperbolic discounters borrow more on their credit cards than rational
consumers, they are also more likely to pay high interest rates and penalty fees. Thus,
hyperbolic discounters are more likely than rational consumers to accumulate steadily
increasing credit card debt.
2.3.3.3 Overdraft and Debit Turnover and Credit Card Default
There is information asymmetry in the credit card market in the sense that the borrowers
know their own ability and willingness to repay the debt better than the card issuers.
20
Given the risk associated with credit card lending, it is important for card issuers to
identify consumer risk types as early as possible to prevent risky consumers from
borrowing too much before default occurs and to customize their marketing strategies to
different customer groups (Zhao and Song, 2009). They posit that consumers who fully
intend to borrow on their credit card accounts are not ideal customers for the card
company. They have bad credit risk, borrow large sums, and often default.
2.3.3.4 Credit and Debit Turnover and Credit Card Default
The essence of banking is the determination as to whether a potential borrower is
creditworthy, that is, whether the potential borrower meets the bank’s credit standards
(Gorton and He, 2008). In the light of aggressive marketing by credit card companies and
consumer concerns about credit card debt, evidence that credit card companies take
greater risk and credit card debt is associated with bankruptcy filings raises the question
whether credit card companies deliberately target risky customers (Kidane and Mukherji,
2004). Tunal and Tatoglu (2010) argues that banks distribute too many credit cards to
consumers without care for whether the consumer in question should have one or not.
They give credit cards to consumers without adequate income mainly because they try to
create volume.
2.3.3.5 Number of Cash Advances and Credit Card Default
Notwithstanding its competitiveness, consumer credit remains an extremely lucrative
activity because accounts have the potential to generate earnings from steep interest rates
and a variety of costly penalties (Cohen, 2005). Banks realize that they need to retain
profitable customers by at least maintaining or, better still, increasing customer loyalty by
encouraging customers to conduct an increased percentage, if not all, of their banking
business with one institution (Baumann, Burton and Elliott, 2007).
The low-risk but occasionally delinquent consumer segment is a good source of revenue
for credit card companies because these consumers pay the interest on the overdue
amount and will eventually pay off their debts (Zhao and Song, 2009). Mislabeling these
low-risk and profitable consumers as part of the high-risk group and imposing an
21
unfavorable credit policy on them simply because of occasional delinquency would
decrease the credit card company’s revenues.
2.4 Empirical Evidence on Credit card Risk of Default in Developed
Markets
Unlike many traditional loans, credit card borrowing does not require consumers to post
collateral which may place a greater risk on the lender. Jaffe and Russell (1976) and
Stigliz and Weiss (1981), et al studied the traditional loan market theoretically using the
tools of asymmetric information and adverse selection.
However, with the growth of credit card debts in the US economy in the last decade,
researchers have increasingly turned their attention to the various aspects of this unique
credit instrument. Asubel (1991) who was the first to carry out an empirical study of this
market found that abnormally high profit and sticky interest rates exist in the industry
inspite of its seemingly competitive structure with over 6,000 card issuers. He speculated
that search/switching costs and a type of irrational consumer behaviour might be involved
in these paradoxical market outcomes. Responding to Ausubel’s argument, Brito and
Hartley (1995) introduced the aspect of the liquidity service of credit cards, which save
consumers the opportunity cost for holding money for payment. Therefore they argue that
it is rational for consumers to hold positive balances even in the face of high interest
rates.
Mesler (1994) also pointed out that high and sticky interest rates could exist without
irrationality on the part of consumers because of information problems for the credit card
banks. Park (1997) explains the situation by referring to the open ended nature of credit
card loans and the high risk involved with this for banks; while Stavins (1996) found that
defaulters had higher interest elasticities and this could induce banks to keep their interest
rates high.
22
Colem and Mester (1995) test the argument of Ausubel’s 1991 paper that irrational
consumers’ behaviour and adverse selection problem account for the failure of
competition in the credit card market. They also examine default in this market and find
that card holders with higher balances have higher probability or defaults.
It is well accepted that borrowing limits on collaterized loans are primarily determined by
amounts of collateral pledged by the borrowers. However, for no collaterized loans, such
as those on credit card, information about borrowers’ repayment ability plays a crucial
role in determining their credit card borrowing limits or credit limits.
Asymmetric information between borrowers and lenders and lack of collateral to mitigate
that informational asymmetry are mainly responsible for credit rationing in some credit
markets. Imperfect information about borrower risks induces banks to refuse credit to
some borrowers even if the latter would accept higher interest rates for their loans. Credit
bureau reports provide crucial information about borrower riskiness, which banks use to
alleviate some of the information asymmetry and to improve the quality of loan supply
decision ( Stigliz and Weiss (1981),
2.5 Empirical Evidence on Credit Card Default Risk in Kenya
Kegode (2006) conducted a study on factors that determine credit worthiness in Kenya
Post Bank. According to her findings married customers were found to be more credit
worthy than single one , the longer a client has stayed in employment that more credit
worthy they were savings accounts holders were more credit worthy than current
accounts holders client with house telephone defaulted twice as much as those with
none, card holders between the age or a land 45paid better than those order than 46,
single with dependents defaulted more than those without the highest default rate was
among
those earning
between
50,000/= and 70,000.00/=. Kegode’s findings are
consistent with previous studies.
23
In modern business transactions, credit cards are increasingly becoming an essential tool.
A credit card offers a cardholder convenience safety, higher purchasing power and a host
of fringe benefits as most cards come with a number of privileges. This is over and
above the basic benefits of serving in place or cash. However, screening out credit risky
customers is a crucial step in card application acceptance process (Mbijiwe J.M.2005)
Mucheko J.G. I (2001) also did a study on determination of nonperforming loans in
privately owned banks in Kenya. His findings were as follows:-Delays in approval were
cited as a critical factor in creation of default in loans, decline in economic growth has
impacted on purchasing power of customers and this has adversely affected the
business’s ability to repay their loans and challenges of managing several of the business
entities. On the other hand, for banks with government shareholding he cited government
influences, the fluctuations in the exchange rate and the ratio of customers to relationship
manager as the main factors influencing non performing of loans.
2.6 Chapter Summary
In a nutshell, the chapter presented a review of studies conducted on factors influencing
credit card default risk. It was evident from the above literature review that most of these
studies conducted targeted Developed Countries. It was also evident that most of the
studies done target credit risk of default on traditional loans. Studies conducted in Kenya
on credit card default risk were mostly case studies; no single study had been conducted
to examine the factors influencing credit card default risk in all the commercial banks in
Kenya. The study thus sought to fill in the identified knowledge gaps.
24
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter covers the research methodology. Section 3.2 covers the Research Design.
Section 3.3 covers the Population and the Sample. Section 3.4 covers Data Analysis.
Section 3.5 entails Data and Data Collection Methods and Section 3.6 Data analysis, Data
Reliability and Validity controls.
3.2 Research Design
Research design is the plan or strategy of shaping the research (Henn, Weinstein and
Ford, 2006). A descriptive design will be used for the study. The independent variables
are: card-holder characteristics, credit card characteristics and behavioral scoring process
while the dependent variable is credit card default
3.3 Target Population
Population refers to the total collection of all elements about which the researcher wishes
to make some references (Denscombe, 2003). Balnaves and Caputi (2001) contend that
populations are operationally defined by the researcher. They further argue that this
population must be accessible and quantifiable and related to the purpose of the research.
The population of my study will consist of all the Commercial Banks in Kenya licensed
and registered under the Banking Act by year 2010.
Cooper and Schindler (2005) states that a sampling frame is a complete and correct list of
the population members only and it comprises all the representative elements in the
population selected for a given study. This list was sourced with permission from the
department of human resource of the 17 Commercial Banks issuing credit Cards in
Kenya. Each commercial Bank was issued 2 questionnaires to enhance the reliability and
validity of the data collected.
25
A sample is a subsection of the population, chosen in such a way that their characteristics
reflect those of the group from which they are chosen (Henn, Weinstein and Ford, 2006).
A sample size of 34 card centre credit risk officers, equivalent to 100% of the population
size was studied.
3.3 Data and Data Collection Methods
Primary data was collected using a semi structured questionnaire. According to Saunders,
Lewis and Thornhill (2003), a questionnaire refers to the general term including all data
collection techniques in which each person is asked to answer the same set of questions
in a predetermined order. It includes structured interviews and telephone questionnaires,
as well as those in which the questions are answered in the interviewers’ absence. Both
closed and open-ended questions will be constructed. Closed ended questions are those in
which the respondents are simply asked to choose ‘Yes/No’ questions, or can be more
lengthy and complex (Henn, Weinstein and Ford, 2006) such as Likert-type questions.
Open ended questions on the other hand, allow respondents the respondents to elaborate
on issues.
Questions regarding card-holder characteristics were adopted from previous researches
conducted by Tunal and Tatoglu (2010) and Hamilton and Khan (2001). They include
demographic and socio-economic variables such as: profession, gender, age, marital
status, education, household size, income, frequency of drawing income from bank
account and investment choices, the main purpose of credit card use, whether using credit
card increases expenditure, the rate of credit card expenditure among others. Further
questions were derived from the research by Pirog and Roberts (2007) and Yang, et al.
(2005) seeking responses about card-holder credit card use.
3.4 Models specification
3.4.1 Conceptual Models
The factors in the literature review constitute the variables of the models. The
hypothetical relationship of each individual factor is derived from the literature review to
26
inform the relationships of the model. The model is a dummy comparison of; Factors
Affecting Credit Card Uses: Evidence from turkey Using Tobit Model (2010)
Three conceptual models as specified below were used
The Card-holder Characteristics on Credit Card Default Conceptual Model
CDR1=F(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,)………...(I)
Where CDR1=Credit Default Risk due to borrower characteristics.
x1= Age of the credit cardholder
x2=Gender of the credit cardholder
X3=Lifestyle of the credit cardholder
x4=Income of the credit cardholder
x5=Education of the credit cardholder
x7=Locus of Control
x8=compulsiveness
x9=Other cards held by the credit cardholder
Credit Card Characteristics that Affect Credit Card Default Conceptual Model
CDR2=F(x11,x12,x13,x14,x15,x16)…………(II)
Where CDR2=Credit Card Characteristics that affect Default
x11=Loans held by the credit cardholder
x12=Interest rates charges on credit cards
x13=Penalty fees charges on credit cards
x14=Hidden costs on credit cards
x15-Ease of access of credit card funds
x16=Convenience of the credit card
Behavioral Scoring Process on Credit Card Default conceptual Model
CDR3=F(x17,x18,x19,x20,x21…………………….(III)
Where CDR3=Behavioral scoring process Credit Card Default
x17=Transaction rewards on credit card
x18=Minimum and maximum payments
27
x19=Overdraft frequency on the credit card
x20=Credit and debit turnover on the credit card
x21=cash withdrawals on the credit card
These variables were measured as follows; Age of the credit cardholders was measured
by number of years lived by the credit cardholders and this will be extracted from the
credit card application forms. Income of the credit cardholders was extracted from the
credit card applications by amount of earnings per month measured in Kenya Shillings.
The Number of cards held by a cardholder was measured by the total number of credit
cards held by an individual as at June, 2011 and this was extracted from the credit
application forms and from Collections Africa Credit Reference Bureau.
Credit and Debit turnover was measured by estimating the percentage of credit obligation
carried forward by credit cardholders and this was extracted from the sample records.
Loans held were measured by total amount of liabilities held by an individual and this
was extracted from the confidential commercial banks records. Minimum payment is
multiplication by a certain percent, and then finance charge and fees added obtained from
the credit card records held with the targeted commercial banks Overdraft frequency was
measured by the number of times a credit cardholder has sought for bonus credit card
funds in addition to the assigned credit card limit. The Number of cash advances was
measured by the amount of cash transactions done by the credit cardholder up to the
period ending 31st June, 2011. Lifestyle, Compulsiveness, Education, and Locus of
Control are construct variables measured qualitatively in prose form. The measurements
were tabulated by scoring with the help of the Likert scale.
3.4.2 Analytical Model
This study employed Multiple regressions which is a flexible method of data analysis that
was found appropriate whenever (the dependent) was to be examined in relationship to
any other factors (expressed as independent or predictor variable).Relationships may be
non linear, independent variables may be qualitative or quantitative and one can examine
28
the effects a single or multiple variables with or without the effects of other variables
taken into account,(Cohen, Cohen, West and Aiken,2003).
The analytical models are derived from their respective conceptual model:
The Card-holder Characteristics on Credit Card Default Analytical Model
CDR1=(β0+β1x1+β2x2+βx33+βx4+β5 x5+β6 x6+β7x7 +β8x8+β9 x9+β10 x10+ εt)………………(I)
Credit Card Characteristics that Affect Credit Card Default Analytical Model
CDR2=(β0+β11x11,+β12x12+β13Hs+β14x14+β15x15+β16x16+ εt…………………………………..(II)
The Behavioral Scoring Process on Credit Card Default Analytical Model
CDR3 (β0+β17x17,+β18x18+β19x19+β20x20,+β21 x+εt………………………………………..…….(III)
Where
CDR- Credit card default
β0+ β1----+… β20+ β21 Model coefficient parameters
+εt- Error term representing all other factors impacting on credit card default but not
explained by the model.
3.5 Data Analysis
The data was edited for accuracy, uniformity, consistency and completeness and arranged
to enable coding and tabulation before final analysis. Since the questionnaire was semistructured (with both open and close-ended questions) both qualitative and quantitative
analysis techniques were used. According to Healey (2009), descriptive aspects of
statistics allow researchers to summarize large quantities of data using measures that are
easily understood by an observer.
It consists of graphical and numerical techniques for summarizing data that is, reducing a
large mass of data to simpler, more understandable terms.
Two tests of significance were used to describe the degree to which one variable is
related to the other. These were the t- test and F-test; these are parametric tests-tests will
test the significance of difference between means of the samples. The relevant test
Statistic ‘’t’’ is calculated from the sample data and its corresponding critical value in the
t-distribution table for rejecting or accepting a null hypothesis. t-tests work well when
samples are less than 30 in number and hence F-test will be employed as the number of
29
degree of freedom in number of numerator and denominator increase. F-test was used to
compare number of variances of independent samples. . The data was entered into the
statistical package for the social sciences (SPSS, Ver. 17) to get the correlation
coefficients. The magnitude of the sample coefficient of correlation indicate if it is a
weak correlation or strong linear relationship.
3.6 Data Reliability and Validity Controls
Without rigor, research is worthless, becomes fiction, and loses its utility. Reliability and
validity which parallel concept of “trustworthiness,” containing four aspects: credibility,
transferability, dependability, and conformability.
To ensure attainment of rigor various strategies were employed including but not limited
to; the audit trail, member checks when coding, categorizing, or confirming results with
participants, peer debriefing, negative case analysis. Thinking theoretically was equally
employed where ideas emerging from data were be reconfirmed theoretically.
30
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
The chapter is divided into four sections. Section 4.2 presents the summary statistics of
the study. Section 4.3 covers the various credit card default relationships; card holder
characteristics, credit card characteristics and behavioral scoring process. Section 4.4
covers the Discussion of the results and Section 4.5 the summary of the chapter.
4.2 Summary Statistics
The general information sought in the study included respondents; gender, length of
service, management level, card ownership, level of experience, proportion of shopping
charged to credit card, credit obligations carried forward and majority of cardholder’s
level of income.
4.2.1 Length of Service
The study sought to find out how long respondents had worked at the card centre. The
results are shown in Table 4.2.1 below.
Table 4.2.1 Years Worked at Card Centre
Distribution
Length of service
Frequency
Less than one
Year
1-3 Years
4-5 Years
More than 5
Years
Total
Percent
Cumulative
Percent
9
28.1
28.1
11
6
34.4
18.8
62.5
81.3
6
18.8
100.0
32
100.0
Source: Author Computation 2011
31
4.2.2 Management Level
The distribution of respondents by their management level is shown in table 4.2 below.
Table 4.2.2 Management Level
Responses
Distribution
Frequency
Percent
Cumulative Percent
Junior Mgt
17
53.1
53.1
Middle Mgt
7
21.9
75.0
Senior Mgt
6
18.8
93.8
Other
2
6.3
100.0
Total
32
100.0
Source: Author Computation 2011
Table 4.2.2 above shows that majority of the respondents were in junior Mgt (53.1%),
followed by Middle Mgt (21.9%), Senior Mgt (18.8%), and lastly, level D (6.3%).
4.2.3 Experience
The distribution of respondents by level of experience in years managing credit cards is
shown in table 4.2.5 below.
32
Table 4.2.3 Years of Experience Managing Credit Cards
Distribution
Cumulative
Experience in Years
Less than 3
Frequency
Percent
Percent
12
37.5
37.5
3-5 years
11
34.4
71.9
5-10 years
5
15.6
87.5
4
12.5
100.0
32
100.0
years
more than 10
years
Total
Source: Author Computation 2011
Table 4.3 shows that 37.5% of the respondents had less than 3 years of experience while
34.4% had 3 – 5 years of experience. Further, 15.6% of the respondents had 5 – 10 years
of experience while only 12.5% had more than 10 years of experience managing credit
cards.
4.2.4 Shopping Expense Charged to Credit Card
The study sought to establish the approximate percentage of shopping expenses
cardholders charged to credit card monthly. Table 4.4 below shows that 34.4% of card
holders charged 24% of shopping expenses to credit card, followed by 31.3% who
charged between 25 – 49%. The table further shows that 21.9% of card holders charged
between 50 – 74% whereas some 12.5% of the respondents charged between 75 – 100%
of shopping expenses.
33
Table 4.2.4 Percentage of shopping expenses Charged to Credit Cards
Percentage of shopping
Distribution
expenses
Cumulative
Frequency
Percent
Percent
24%
11
34.4
34.4
25%-49%
10
31.3
65.6
50%-74%
7
21.9
87.5
75%-100%
4
12.5
100.0
32
100.0
Total
Source: Author Computation 2011
4.2.5 Credit Obligations Carried Forward
The study sought to establish the percentage of card holder’s credit obligations carried
forward every month.
Table 4.2.5 Percentage of Credit Obligation is carried Forward Every Month
Percentage of credit card
Distribution
obligations
Cumulative
Frequency
Percent
Percent
24%
5
15.6
15.6
25%-49%
8
25.0
40.6
50%-74%
8
25.0
65.6
75%-100%
11
34.4
100.0
Total
32
100.0
Source: Author Computation 2011
Table 4.2.5 above shows that majority of the card holders (34.4%) carried forward 75%100% of their credit card obligations every month. Twenty five percent (25%) of the card
holders carried forward 50–74% of the obligations whereas another 25% carried forward
34
between 25% - 49% of their credit card obligations. Only 15.6% of the respondents
carried forward up to 24% of their credit card obligations every month.
4.2.6
Card Holders’ Level of Income
The study sought to establish card holder’s level income. The distribution of the income
levels is shown in Table 4.2.6 below.
Table 4.2.6 Card Holder’s Level of Income
Distribution
Level of income
4
22
Percent
12.5
68.8
Cumulative
Percent
12.5
81.3
6
18.8
100.0
32
100.0
Frequency
0-25,000 Kshs
25,001-50,000 Kshs
50,001-100,000
Kshs
Total
Source: Author Computation 2011
The table shows that majority of the card holders (68.8%) were earning between Kshs.
25,000 – 50,000 followed by those earning between Kshs. 50,001 – 100,000. Lastly,
some 12.5% of the card holders earned Kshs. 25,000 or less.
4.3. The Relationship between Credit Risk of Default and Mitigation
Approaches
4.3.1 The Effect of Card-Holder Characteristics on Credit Card Default
The card holders’ characteristics examined in the study included gender, age, income,
education, lifestyle, self control, other cards held, assets held and loan held.
35
Table 4.3.1 The Effect of Card-Holder Characteristics on Credit Card Default
Card holder
Characteristic
Gender
Responses
Not all
Very small extent
Small extent
Large extent
Very large extent
Age
Very small extent
Small extent
Large extent
Very Large extent
Income
Small extent
Large extent
Very large extent
Education
Small extent
Large extent
Very large extent
Lifestyle
Small extent
Large extent
Very large extent
Self control
Very small extent
Small extent
Large extent
Very large extent
Cards held
Not at all
Very small extent
Small extent
Large extent
Very large extent
Assets held
Not at all
Very small extent
Small extent
Large extent
Loans held
Not at all
Very small extent
Small extent
Large extent
Very large extent
Source: Author Computation 2011
36
Distribution
Frequency
1
7
11
9
4
4
13
13
2
1
12
19
12
10
10
4
16
12
4
6
9
13
1
8
8
10
5
6
14
8
4
5
8
5
8
6
Percent Cumulative
Percent
3.1
3.1
21.9
25.0
34.4
59.4
28.1
87.5
12.5
100.0
12.5
12.5
40.6
53.1
40.6
93.8
6.3
100.0
3.1
3.1
37.5
40.6
59.4
100.0
37.5
37.5
31.3
68.8
31.3
100.0
12.5
12.5
50.0
62.5
37.5
100.0
12.5
12.5
18.8
31.3
28.1
59.4
40.6
100.0
3.1
3.1
25.0
28.1
25.0
53.1
31.3
84.4
15.6
100.0
18.8
18.8
43
62.5
25.0
87.5
12.5
100.0
15.6
15.6
25.0
40.6
15.6
56.3
25.0
81.3
18.8
100.0
From table 4.3.1 above (34%) respondents were of the opinion that gender of the
respondents affected credit card default to a small extent and 21.9% said gender did
affect credit card default to a very small extent while 3.1% said it had no effect.
However, 28.1% of the respondents said it did to a large extent whereas 12.5% said
gender affected influenced credit card default to a very large extent.
Further table 4.3.1 shows that 40.6% of the respondents felt that card holder’s age
contributed to credit card default to a large extent whereas another 40.6% felt that it did
to a small extent. Six percent (6.3%) of the respondents felt that age affected credit card
default to a very large extent whereas 12.5% claimed that it did to a very small extent.
The correlation results showed an inverse relationship existed between card holder’s age
and rate of default (r=-.093, p>.05). This suggests that age could possibly influence credit
card default, with the rate going down as the card holder advances in years.
The table shows that all of the respondents felt that income affected credit card default.
Majority of the respondents (59.4%) were of the opinion that income levels affected the
rate of default to a very large extent and 37.5% felt that it did to a large extent. Lastly,
3.1% of the respondents said it did to a small extent. The correlation matrix (Table 4.7)
showed that an inverse relationship existed between card holder’s level of income and
credit card default (r=-.763, p=.000) suggesting that the rate of default reduced as card
holder’s income rose.
Majority of the respondents according to table 4.3.1 felt that education did affect credit
card default to a large extent (31.3% large extent and 31.3% very large extent). However,
37.5% of the respondents felt that it did to a small extent. The study established that a
positive but insignificant correlation existed between card holder’s education and credit
card default (r=.006, p>.05).
According to table 4.3.1, Fifty percent (50%) of the respondents felt that the lifestyles of
card holders affected credit card default to a large extent and 37.5% felt that it did to a
very large extent. Lastly, 12.5% of the respondents felt that it did to a small extent. The
37
study established an insignificant positive correlation between card holder’s lifestyle and
credit card default (r=.138, p>.05). This means that the positive relationship was nothing
more than random variation.
Forty percent (40.6%) of the respondents were of the opinion that card holder’s self
control influenced the rate of credit card default to a very large extent and 28.1% felt that
it did to a large extent. However, 18.8% felt that self control affected credit card default
to a small extent whereas 12.5% felt that it did to a very small extent. An inverse
relationship was established between card holder’s self control and credit card default
(r=.181, p>.05).
Table 4.3.1 further shows that 31.3% of the respondents felt that cards held with other
banks did affect credit card default to a large extent and 15.6% felt it did to a very large
extent. Twenty five percent (25%) of the respondents felt that other cards held affected
the rate of default to a small extent and another 25% felt that it did to a very small extent.
Lastly, 3.1% of the respondents saw no effect of other cards held on credit default at all.
The study found out that a direct relationship existed between the number of other cards
held and the rate of default (r=.297, p>.05) suggesting that the default rate increased with
additional credit card held elsewhere.
According to table 4.3.1 above, majority of the respondents felt that assets held affected
credit card default to a small extent. The table shows that 43.8% were of the opinion that
it affected it to a very small extent and 255 felt that it did to a small extent. Further,
18.8% saw no effect of assets held on credit card default. However, 12.5% were of the
opinion that assets held affected credit card default to a large extent. The study did not
establish any significant correlation between assets held and credit card default (r=.108,
p>.05).
38
The study sought to find out the extent to which loans held by the card holder affected the
rate of default. Twenty five percent (25%) of the respondents observed that it did to a
large extent and 18.8% were of the opinion that it affected the rate of default to a very
large extent. However, 25% of the respondents felt that it did to a very small extent,
15.6% to a small extent and another 15.6% said not at all. The study established that a
negative but insignificant correlation existed between loans held and credit card default
(r=-.093, p>.05).
4.3.2 Credit Card Characteristics that Affect Credit Card Default
The variables examined in this section included: interest rates, penalty fees, hidden costs,
credit limits, credit access, convenience, merchant fees and transaction rewards.
39
Table 4.3.2 Credit Card Characteristics that Affect Credit Card Default
Credit card
Characteristic
Interest Rate
Responses
Very small extent
Small extent
Large extent
Very large extent
Penalty fees
Very small extent
Small extent
Large extent
Very Large extent
Income
Small extent
Large extent
Very large extent
Hidden costs
Not at all
Very small extent
Small extent
Large extent
Very large extent
Credit limits
Not at all
Very small extent
Small extent
large extent
Very large extent
Credit Access
Very small extent
Small extent
Large extent
Very large extent
Convenience
Very small extent
Small extent
Large extent
Very large extent
Merchant fees
Not at all
Very small extent
Small extent
Large extent
Very large extent
Transaction
Not at all
Rewards
Very small extent
Small extent
Large extent
Very large extent
Source: Author Computation 2011
Distribution
Frequency
6
7
9
10
4
8
8
12
1
12
19
8
10
6
3
5
2
9
7
8
6
4
6
13
9
6
8
12
6
12
9
6
2
3
13
9
5
3
2
40
Percent
18.8
21.9
28.1
31.3
12.5
25.0
25.0
37.5
3.1
37.5
59.4
25.0
31.3
18.8
9.4
15.6
6.3
28.1
21.9
25.0
18.8
12.5
18.8
40.6
28.1
18.8
25.0
37.5
18.8
37.5
28.1
18.8
6.3
9.4
40.6
28.1
15.6
9.4
6.3
Cumulative
Percent
18.8
40.6
68.8
100.0
12.5
37.5
62.5
100.0
3.1
40.6
100.0
37.5
56.3
75.0
84.4
100.0
6.3
34.4
56.3
81.3
100.0
12.5
31.3
71.9
100.0
18.8
43.8
81.3
100.0
37.5
65.6
84.4
90.6
100.0
40.6
68.8
84.4
93.8
100.0
Table 4.3.2 shows that 31.3% of the respondents said interest rates affected credit card
default to a very large extent and 28.1% said it did to a large extent. On the other hand,
21.9% said it did to a small extent while 18.8% felt that it had an effect to a very small
extent. The study established a direct, though insignificant correlation between interest
rate and credit card default (r=.123, p>.05).
Table 4.3.2 further shows that 37.5% of the respondents were of the opinion that penalty
fees affected the default rate to a very large extent, 25% said it did to a large extent
whereas on the other hand, 25% said it did to a small extent and a further 12.5% said it
affected credit card default to a very small extent. The correlation results showed a
positive but insignificant relationship between penalty fees and credit card default (r =
.109, p>.05).
According to Table 4.3.2, 75% of the respondents observed that the effect of hidden cost
on credit card default was small or it did not exist at all. The table shows that 31.3% were
of the opinion that it affected credit card default to a very small extent and 18.8% felt that
it did to a small extent and 25.0% said not at all. However, some 15.6% felt that it did to
a very large extent and 9.4% said it affected credit default to a large extent. The study
found out that an insignificant positive correlation existed between hidden costs charged
and the rate of default (r=.181, p>.05).
Twenty five percent (25%) of the respondents said credit limit affected the rate of default
to a large extent and 18.8% said it affected default to a very large extent. However,
21.9% said it affected to a small extent, 28.1% said it affect the default rate to a very
small extent whereas 6.3% did not notice any effect all. The study found out that there
was no significant correlation between credit limit and credit default (r=.181, p>.05).
Table 4.3.2 shows that 40.6% of the respondents said that easy access to credit affected
credit card default to a large extent and 28.1% said it did to a very large extent. On the
other hand, 18.8% saw that it affected the rat of default to a small extent and 12.5% said
41
it did to a very small extent. The study established an inverse, although insignificant
relationship between ease of credit access and credit default (r=-.131, p>.05).
According to table 4.3.2 majority of the respondents were of the opinion that convenience
affected credit default to a large extent (37.5% said it did to a large extent and 18.85 said
it did to a very large extent). However, 25% felt that it affected credit card default to a
small extent while 18.8% said the effect was very small. A positive, although
insignificant relationship was established between convenience and credit card default
(r=.042, p>.05).
The study sought to establish the extent to which merchant fees such as transaction fees
charged at the point of sale affected credit card default. Majority of the respondents
(37.1%) said that merchant fees did not affect credit card default at all while 28.1% said it
did to a very small extent and another 18.8% said it did to a small extent. On the other
hand, 6.3% of the respondents said merchant fees influenced the rate of default to a large
extent and an additional 9.4% said it did to a very large extent. The study did not
establish any significant relationship between merchant fees and credit card default
(r=.038, p>.05).
Majority of the respondents (40.6%) did not relate transaction rewards to credit card
default at all while 28.1% said it affected credit card default to a very small extent.
Further, 15.6% said it affected it to a small extent whereas 9.4% said it did to a very large
extent and a further 6.3% said it did to a very large extent. The study did not establish any
significant correlation between transaction rewards and credit card default (r=.027,
p>.05).
4.3.3 The Effect of Behavioral Scoring Process on Credit Card Default
Among the variables examined in this section were: minimum and maximum balance,
payment trends, overdraft frequency, credit and debit turnover, frequency of defaults and
number of cash advances.
42
Table 4.3.3 The Effect of Behavioral Scoring Process on Credit Card Default
Behavioral Scoring
Factor
Responses
Distribution
Frequency
Minimum and
Maximum Balances
Not at all
Very small extent
Small extent
Large extent
Very large extent
Payment Trends
Very Small extent
Small extent
large extent
Very large extent
Overdraft Frequency
Very small extent
Small extent
Large extent
very large extent
Credit and debit
Very small extent
turnover
Small extent
Large extent
Very large extent
Cash advances
Not at all
Small extent
Large extent
Very large extent
Source: Author Computation 2011
4
5
10
9
4
1
7
12
12
2
8
14
8
3
12
8
9
1
9
12
10
Percent Cumulative
Percent
12.5
12.5
15.6
28.1
31.3
59.4
28.1
87.5
12.5
100.0
3.1
3.1
21.9
25.0
37.5
62.5
37.5
100.0
6.3
6.3
25.0
31.3
43.8
75.0
25.0
100.0
9.4
9.4
37.5
46.9
25.0
71.9
28.1
100.0
3.1
3.1
28.1
31.3
37.5
68.8
31.3
100.0
Table 4.3.3 shows that 31.3% of the respondents said minimum and maximum balance
levels determine credit card default trends to a small extent, 15.6% said it did to a very
small extent while 12.5% said not at all. However, 28.1% said it did to a large extent and
12.5% said it did to a very large extent. The study did not find any significant relationship
between the minimum and maximum level of balances and credit card default trends
(r=.152, p>.05).
According to table 4.3.3 up to 75% of the respondents said payment trends determined
credit card default to a large extent (37.5% said large extent and 35.7% said very large
extent). The table shows that 21.9% said it did to a small extent and 3.1% said it did to a
43
very small extent. However, the study found out that there was no significant relationship
between payment trends and credit card default (r=.135, p>.05).
Table 4.3.3 shows that 43.8% were of the opinion that it did determine defaults to a large
extent and 25% said it did to a very large extent. Twenty five percent (25%) of the
respondents however said it did to a small extent whereas 6.3% said it did to a very small
extent. The correlation results showed that overdraft frequency was negatively but
insignificantly related to credit card default (r=-.213, p>.05).
Table 4.3.3 further shows that 37.5% of the respondents were of the opinion that credit
and debit turnover determined credit card default trends to a small extent and 9.4% said it
did to a very small extent. Twenty five percent (25%) of the respondents however said it
did to a large extent and a further 28.1% said it did to a very large extent.
According to table 4.3.3.shows that it did to large extent (37.5%) and very large extent
(31.3%). The able also shows that 28.1% said it did to a small extent while 3.1 said it did
not determine credit card default trends at all. The study found out that no significant
correlation existed between cash advances and credit default trends (r=.121, p>.05).
4.3.4 What can be done to Reduce Credit Card Default
This section sought respondent’s opinion on what can be done to reduce credit card
default at the bank. The strategies examined include consumer education, counseling,
credit standing reports, financial awareness campaigns, frequent appraisals, the role of
inspirational groups and stiffer regulation.
44
Table 4.3.4 What can be done to Reduce Credit Card Default
What can be done to
reduce credit card
default
Consumer education
Counseling Programs
Monitoring credit
Standing
Due diligence
Financial awareness
Frequent card
appraisals
Influence of
inspirational groups
Regulation by Central
Bank
Responses
Distribution Percent Cumulative
Frequency
Percent
Small extent
Large extent
Very large extent
Not at all
Very small extent
Small extent
Large extent
Very large extent
Very small extent
Small extent
Large extent
Very large extent
Small extent
Large extent
Large extent
Very small extent
Small extent
Large extent
Very large extent
Not at all
Very small extent
Small extent
Large extent
Very large extent
Not at all
Very small extent
Small extent
Large extent
Not at all
Very small extent
Small extent
Large extent
2
14
16
1
3
10
14
4
3
2
18
9
1
19
12
6
3
13
10
2
1
8
19
2
6
8
14
4
6
8
14
4
6.3
43.8
50.0
3.1
9.4
31.3
43.8
12.5
9.4
6.3
56.3
28.1
3.1
59.4
37.5
18.8
9.4
40.6
31.3
6.3
3.1
25.0
59.4
6.3
18.8
25.0
43.8
12.5
18.8
25.0
43.8
12.5
6.3
50.0
100.0
3.1
12.5
43.8
87.5
100.0
9.4
15.6
71.9
100.0
3.1
62.5
100.0
18.8
28.1
68.8
100.0
6.3
9.4
34.4
93.8
100.0
18.8
43.8
87.5
100
18.8
43.8
87.5
100.0
Source: Author computation 2011
According to table 4.3.4 Fifty percent (50%) of the respondents felt that consumer
education can reduce credit card default to a very large extent and 43.8% said it can to a
large extent. Only 6.3% of the respondents said it can to a small extent.
45
Table 4.3.4 further shows that 43.8% of the respondents said that counseling programs
would to a large extent and 12.5% said it would to a very large extent. On the other hand,
31.3% of the respondents said it would to a small extent, 9.4% said it would to a very
small extent while 3.15 said not at all.
Table 4.3.4 shows that 56.3% of the respondents said monitoring credit standing can
reduce credit card default to a large extent and 28.1% said it can to a very large extent.
On the other hand, 6.3% of the respondents said it can to a small extent and 9.4% said it
can to a very small extent.
Table 4.3.4 shows that 59.4% of the respondents said due diligence can help reduce credit
card default to a large extent and 37.5% said it can to a very large extent. Only 3.1% of
the respondents said it can to a small extent.
According to table 4.3.4, 40.6% of the respondents said financial awareness can to a large
extent and 31.3% said it can to a very large extent. However, some 9.4% said it can to a
small extent and a further 18.8% said it can to a very small extent.
The table shows that 59.4% of the respondents said frequent card appraisals can to a large
extent and 6.3% said it can to a very large extent. However, 25 %said it can to a very
small extent and 3.1% said it can to a small extent whereas 6.35% said not at all.
Table 4.3.4 above shows that majority of the respondents (43.8%) felt that Inspirational
groups can reduce credit card default to a small extent and 25% said to a very small
extent while 18.8% said not at all. However, some 12.5% of the respondents said this
strategy can help reduce credit card default to a large extent.
Table 4.3.4 shows respondents’ opinion on the extent to which stiffer regulation by
Central Bank of Kenya (CBK) can reduce credit card default. The table shows that 34.4%
of the respondents said it can to a large extent and 3.1% said it can to a very large extent.
46
However, 31.35 said it can to a very small extent and 21.9% said it can to a small extent
whereas 9.4% said not at all.
The appendix I, II and III show the individual variables captured in the linear regression;
for card characteristics, cardholders’ characteristics and behavioral scoring process.
4.4 Discussion
4.4.1 Summary of the Card-Holder Characteristics on Credit Card Default
Table 4.4.1 Card-Holder Characteristics on Credit Card Default
Card holder
Extent of Influence
Credit card default with increase
Gender
Insignificant
-
Age
Significant
Decrease
Income
Significant
Decrease
Lifestyle
Significant
-
characteristic
Source: Author computation 2011
The findings showed that majority of the respondents were of the opinion that gender of
the respondents affected credit card default to a small extent, suggesting that gender is
not significant in determining potential card default. This is consistent with previous
research which established that mean credit card scores were essentially the same for men
and women. However, it goes contrary other researches which found out that the
tendency to revolve was significantly higher among males. The finding confirms the
researcher’s hypothesis that research on gender differences was inconclusive.
The study found out that age contributed to credit card default to a large extent. The
correlation results showed an inverse relationship existed between card holder’s age and
rate of default, suggesting that age could possibly influence credit card default, with the
rate going down as the card holder advances in years. Age has also been found previously
47
to be one of the significant demographic and socio-economic characteristic in describing
consumer credit card practices. The inverse correlation confirms earlier conclusion that
credit cards are particularly problematic for the youth. For instance, it was posited in the
literature that the average college student will graduate with more than $2,800 in credit
card debt and up to one- fifth carry a credit card debt of $10,000 or more. As the inverse
correlation established in this study suggests, the older a person gets, the less likely he is
to default on payment.
The study established a general consensus from the credit card managers that income did
affect credit card default. An inverse relationship existed between card holder’s level of
income and credit card default, suggesting that the rate of default reduced as card
holder’s income rose. This is naturally expected and is consistent with earlier arguments
which held that when consumers’ incomes are high, they are likely to pay their credit
card bills in full, and therefore their debt burden is low and they pay little or no interest.
The card centre managers were of the opinion that the lifestyles of card holders affected
credit card default to a large extent. This result is shared by other scholars who observe
that credit cards allow many people the ability to reach what, in their minds, equates to
living in the next higher class level. The study established that self control of the card
holder also influenced credit card default. While previous studies found out that increase
in the number of cards on which a consumer has reached the borrowing limit increase
default, this study found out that number of cards held with other banks affected credit
card default to a small extent, suggesting that although some previous findings hold, its
relevance as a determinant needs to be considered in perspective.
48
4.4.2 Summary of Credit Card Characteristics that Affect Credit Card Default
Table 4.4.2 Credit Card Characteristics that Affect Credit Card Default
Credit card
Extent of Influence
Credit card default with increase
Rate of Interest
Significant
Increase
Penalty Fees
Significant
Increase
Hidden costs
Significant
Increase
Convenience
Significant
Increase
Transaction Rewards
Significant
Inconclusive
characteristic
The study found out that according to the card centre managers, the rate of interest
charged on the card affected credit card default. The study established a direct, though
insignificant correlation between interest rate and credit card default. This suggests that
high interest rates encouraged defaulters. The practice by banks to offer introductory low
or zero interest rates as observed earlier is, in the face of these findings, counter
productive, since once credit card debt is established, a combination of high interest rates,
fees, and insufficient income usually keeps people from paying off their debt. That
majority of the cardholders carried forward their debt obligations could be contributed by
interest rates charged on credit card defaults. A direct relationship was also established
between penalty fees and credit card defaults, suggesting that the rate of default actually
increased with higher penalty fees. This has been previously observed as implied in the
literature which argued that the credit card companies often compound the problem of
default by charging penalties and fees and increasing debtors’ credit limits.
The study established that, according to majority of the card centre managers, the effect
of hidden cost such as annual fees on credit card default was small. The findings did not
show any significant correlation between hidden costs charged and the rate of default.
This implies that the hidden costs, if any, were so small that they could not possibly be a
reason to cause credit card default. Similarly, the study found out that credit limits set for
cardholders did not have a significant influence on credit card default. This result is
surprising; given that majority of the cardholders at the bank were revolvers, which imply
49
that they exhaust all the credit available on the credit card every month. However, there
are two possible reasons to explain the foregoing finding. Firstly, revolvers are not
necessarily defaulters and, secondly, card holders at the bank may have heterogeneous
demographic profiles. Despite the foregoing results, ease of access to credit affected
credit card default to a large extent. This has been noted by earlier researchers who
argued that there is a large portion of credit card debtors who over spend using credit
cards simply because they are given credit too easily without consideration to whether
they can really handle the amount of credit issued. This particular finding is consistent
the cultural theory of consumption, and by extension, credit limit is therefore still
relevant as a check to credit risks.
Majority of the respondents were of the opinion that convenience affected credit default
to a large extent. A positive, although insignificant relationship was established between
convenience and credit card default, suggesting that chances of default rose with easier
credit access. The convenience of credit cards sometimes carries with it the fleeting
impression of liquidity, which some cardholders with limited discretionary income may
fall victims of. As noted in the literature review, consumers can sometimes use credit
unwisely, carry high balances, and frequently pay only the minimum amount on each
card they hold.
The study established that card centre managers did not relate transaction rewards to
credit card default at all. This suggests that such reward practices - among them, cashback and air miles – as argued out by previous researchers, were non-existent at the bank,
or they did not provide sufficient incentive for cardholders to overlook any impending
costs. Nonetheless, caution is necessary when practicing such motivators in order to
ensure due diligence necessary to mitigate credit default risks.
50
4.4.3 Summary of the Effect of Behavioral Scoring Process on Credit Card Default
Table 4.4.3 Behavioral Scoring Process on Credit Card Default
Behavioral Scoring Process
Extent of Influence
Credit card default
with increase
Minimum and Maximum Payments
Insignificant
-
Payment Trends
Significant
-
Overdraft Frequency
Significant
Increase
The findings revealed that minimum and maximum balance levels determined credit card
default trends to a small extent. The study did not find any significant relationship
between the minimum and maximum level of balances and credit card default trends.
This result implies that while the assumption that credit card banks cannot observe a
direct measure of risk types holds, taking balance size to be the major indictor of default
risk is questionable, and at a minimum, cannot be taken in isolation in any behavioral
scoring model adopted.
The study showed that card centre managers held that payment trends determined credit
card default to a large extent. This makes sense not only due to its measurability as an
indicator, but also due to its importance in differentiating between rational consumers and
hyperbolic discounters. This is because hyperbolic discounters has been simulated to
borrow more on their credit cards than rational consumers and therefore, are also more
likely to pay high interest rates and penalty fees. Thus, hyperbolic discounters are more
likely than rational consumers to accumulate steadily increasing credit card debt.
The study found out that card centre managers held the opinion that overdraft frequency
did determine defaults to a large extent. This suggests that overdraft frequency is a useful
factor during account monitoring. It is indeed important for card issuers to identify
consumer risk types as early as possible to prevent risky consumers from borrowing too
much before default occurs and to customize their marketing strategies to different
customer groups. Other criteria of less significance include analyzing credit and debit
turnover and the cash advances made against the account.
51
4.4.4 Summary of what can be done to Reduce Credit Card Default
Table 4.4.4 Credit Card Default Mitigation Approaches
Extent of Influence
Rate of default with
Increase
Consumer Education
Significant
Decrease
Counseling Programs
Significant
Decrease
Monitoring credit standing
Significant
Decrease
Inspirational groups
Significant
Inconclusive
Stiffer CBK Regulation
Significant
Inconclusive
Due diligence
Significant
Decrease
Financial awareness
Significant
Decrease
The findings revealed that most of the card center managers felt that consumer education
can reduce credit card default to a large or very large extent. By implication, the findings
emphasize consumer centric approach to marketing practice argued to benefit card issuers
when credit card misuse is reduced. While the ultimate responsibility for this task falls on
the card holder, this study agrees with previous scholars that card holders clearly require
help in understanding the nature of the problem, its consequences, and ways to overcome
it, if not avoid it altogether. The linkages already established between certain
demographic factors and credit card use or misuse have clear applications for
communication programs designed to serve card holders. All participants - merchants,
consumer goods companies, and banks stand to gain, and their involvement is necessary.
Counseling programs would also influence credit card default to a large extent as
perceived by card centre managers. Targeted counseling programs would help
compulsive card holders who are at greater risk for building higher levels of credit card
debt, especially for those who are also high in concern with immediate consequences of
their actions. The study findings revealed that monitoring credit standing can reduce
credit card default to a large extent according to majority of the card centre managers.
This reinforces previous study findings which established that credit counseling improved
consumers’ financial behavior. It agrees with financial literature validated by a series of
52
studies which found that consumers who are financially knowledgeable are more likely to
behave in financially responsible ways.
The card centre managers also observed that due diligence can help reduce credit card
default to a large extent. As such, due diligence is especially necessary as the study
established that majority of the card holders charged up to half of their shopping expenses
to credit card while they also carried forward a greater part of their credit card obligations
every month. Due diligence would help card centre managers to identify high-risk
consumers at the earliest stage possible. This echoes the important in the need for the
card issuer to use the spending and repayment data from the first month when a consumer
opens a credit card account with the company. In addition, effective due diligence
systems such as knowing customers and being alert to unusual transactions are also
fundamental to help ensure compliance with activity reporting regulations.
As the study found out, general financial awareness campaign targeted on the card holder
can also go along way in reducing credit card default. Card holders at the bank with
lower internal locus of control can especially benefit from targeted programs along with
frequent card appraisals as suggested in this study. While the influence of inspirational
groups was not established as significant in reducing credit card default, providing
information to such groups would be part of a holistic awareness campaign that would
benefit card holders with external locus of control. This is suggested on the strength of
previous research which has shown that externals believe that their success is controlled
by external forces, and rely more on reference groups or authorities than internals.
Further, card centre managers in this study felt that stiffer regulation by central bank can
help reduce credit card defaults to a small extent if at all. This is unsurprising as previous
studies have established that according to the perspective of bankers, further regulations
will seriously harm the profitability of the credit card business. However, this argument
by itself should not warrant wholesome dismissal of the role of regulation in mitigating
credit card default risks.
53
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter covers the summary of the study, conclusion and recommendations for
further studies. Section 5.2 covers the summary of the study. Section 5.3 covers the
conclusion. Section 5.4 entails the recommendations for further studies.
5.2 Summary of the Study
The general objective of the study was to establish the relationship between credit card
risk of default and Cardholders characteristics, Credit Card characteristics and Behavioral
Scoring Process in Kenya. The following research questions guided the study: What is
the effect of card-holder characteristics on credit card default? Do the credit card
characteristics affect credit card default? Does the behavioral scoring process influence
credit card default? What can be done to reduce default in credit card usage?
A descriptive design was used for the study. The population comprised of card centre
credit risk officers at the 17 Commercial Bank issuing credit cards, totaling to 34
managers. A sample of 34 respondents, equivalent to the population was targeted for the
study. Data was collected using semi structured questionnaire and 32 questionnaires were
successfully filled and returned. Data was analyzed and summarized in frequencies and
percentages. Spearman’s Rank Correlation Coefficient was used to determine the
relationship between the study variables. The findings have been presented in tables and
charts for easier interpretation.
The study found out that majority of card holders charged up to about half (49%) of their
shopping expenses to credit card. The study also found out that 34.4% of card holders
carried forward 75%-100% of their credit card obligations every month while 25% of the
card holders carried forward 50 – 74% of the obligations. Majority of card holders were
in the income bracket of between Kshs. 25,000 – 50,000. The study found out that
54
gender, assets held and other cards held affected credit card default to a small extent,
whereas age, education, card holder’s self control and lifestyle did affect credit card
default to a large extent. An inverse relationship was found between card holder’s age,
income and loans held on credit card default.
The study also found out majority of the respondents felt that credit card characteristics
such as interest rate, penalty fees, ease of credit access and the convenience of credit
cards affected credit card default to a large or very large extent. On the other hand,
hidden cost, credit limits, merchant fees and transaction rewards either affected credit
card default or did not affect at all. The study further found out that, aspects of behavioral
scoring such as payment trends, overdraft frequency, credit and debit turnover and cash
advances determined credit card default trends to a large extent whereas maximum and
minimum balances did to a small extent. No significant correlation was however
established between the behavioral scoring factors and credit card default.
According to majority of the respondents, consumer education, monitoring credit
standing, due diligence, financial awareness campaigns and frequent card holder
appraisals can help reduce credit card default to a large or very large extent. On the other
hand, counseling programs, providing information on inspirational groups and stiffer
regulation by central bank can help to a small extent if at all.
5.3 Conclusion
5.3.1 The Effect of Card-Holder Characteristics on Credit Card Default
A number of card holder characteristics affected credit card default. Among them, level
of income was the greatest. Age also did have significant implications on credit card
default as well as cardholder’s lifestyle and locus of control. However, while gender also
affected credit card default, its effects was considered small among the Card Centre risk
officers. Each characteristic, however, have strategic significance to card issuers and
would be useful in mitigating credit card defaults.
55
5.3.2
Credit Card Characteristics that Affect Credit Card Default
Credit card characteristics did affect credit card default. This included rate of interest
charged by the card issuer, penalty fees charged upon default, easy access to credit and
convenience that is attendant to card usage. In addition, hidden costs also influenced
credit card default, albeit to a lesser degree. Transaction rewards, on the other hand did
not affect credit card default, either due to insufficient incentive available in the reward
that can distort card holder’s reasoned decision choices or due to absence of such rewards
to begin with.
5.3.3
The Effect of Behavioral Scoring Process on Credit Card Default
Most aspects of behavioral scoring determined credit card default trends. Top in the list
was payment trends and overdraft frequency. Other criteria of less significance included
analyzing credit and debit turnover and the cash advances made against the account.
Contrary to expectations, minimum and maximum balance levels had minimal
determining significance on credit card default trends. This has implications for the
emphasis placed on each factor when scoring the behavior of card holders.
5.3.4 Recommendations for further Research
The study covered credit card risk of default among commercial banks in Kenya. I
recommend future studies to examine other credit card risks such as credit card fraud
among commercial banks in Kenya.
The study has examined credit card defaults in relation to cardholder characteristics,
credit card characteristics and behavioral scoring process. Further studies should be
conducted to array the contribution of technological innovations, liberization,
deregulation, prevailing economic conditions among others in explaining credit card
default among commercial banks in Kenya.
56
REFERENCES
Abdul-Muhmin, A. G. & Umar, Y. A. (2007). Credit card ownership and usage behaviour
in Saudi Arabia: The impact of demographics and attitudes toward debt, Journal
of Financial Services Marketing (2007) 12, 219 – 234.
Akin, G. G., Aysan, F. A., Kara, G. I. & Yildiran, L. (2010). The Failure of Price
Competition in the Turkish Credit Card Market, Emerging Markets Finance &
Trade, 2010, Vol. 46, Supplement 1, pp. 23–35.
Alex, J. N. & Raveendran, P. T. (2008). Does compulsive buying affect credit card
defaults? The Journal of Business Perspective, l Vol. 12 l No. 4.
Balnaves, M. & Caputi, P. (2001). Introduction to Quantitative Research Methods.
London: Sage Publications Ltd.
Baumann, C., Burton, S. & Elliott, G. (2007). Predicting Consumer Behavior in Retail
Banking, Journal of Business and Management – Vol. 13, No. 1.
Bellotti, T. (2009). A simulation study of Basel II expected loss distributions for a
portfolio of credit cards, Journal of Financial Services Marketing, Vol. 14, 4,
268–277.
Bolt, W. & Chakravorti, S. (2010). Economics of Payment Cards: A Status Report.
Chikago: Federal Reserve Bank of Chicago.
Bradford, W. (2003) The savings and credit management of low-income, low-wealth
black and white families. Economic Development Quarterly 17, 53–74.
Chen, Y., & Devaney, S. A. (2001), The effects of credit attitude and socioeconomic
factors on credit card and installment debt, Journal of Consumer Affairs, Vol. 35,
pp. 162-179.
57
Cohen, M. J. (2005). Consumer credit, household financial management, and sustainable
consumption, International Journal of Consumer Studies, 1470-6431
Cooper, D. R. & Schindler, P.S. (2005). Business Research Methods. NY: McGraw Hill
Irwin.
Denscombe, M. (2003). The Good Research Guide. 2nd Edition. Maidenhead,
Philadelphia: Open University Press.
Desear, E. M. (2009). Credit Card Structures: Surviving the “Worst Case” Scenario. The
Journal of Structured Finance.
Durkin, T. A. (2000). Credit cards: Use and consumer attitudes, 1970-2000. Federal
Reserve Bulletin, September, 623-634.
Elliehausen, G. E,. Christopher, L., & Michael, E. S. (2007). The Impact of Credit
Counseling on Subsequent Borrower Behavior. Journal of Consumer Affairs, 41
(Summer): 1–28.
Elliehausen, G., Lundquist, E. C. & Staten, M. (2003). The Impact of Credit Counseling
on Subsequent Borrower Credit Usage and Payment Behavior, Washington, DC:
Georgetown University Credit Research Center.
Erdem, C. (2008). Factors Affecting the Probability of Credit Card Default and the
Intention of Card Use in Turkey, International Research Journal of Finance and
Economics, Issue 18.
Frederic S. M. (2007): The Economics of Money, Banking and Financial Markets, (8th
ed.). Pearson International.
58
Finke, M.S. & Huston, S. J. (2003) Factors affecting the probability of choosing a risky
diet. Journal of Family and Economic Issues, 24, 291–303.
Gorton, G. B. & He, P. (2008). Bank Credit Cycles. The Review of Economic Studies. 75,
1181–1214.
Goyal, A. (2006). Consumer Perception towards the Purchase of Credit Cards, Journal of
Services Research, Volume 6, Special Issue
Hamilton, R. & Khan, S. (2001). Revolving Credit Card Holders: Who Are They and
How Can They Be Identified? The Service Industries Journal, Vol.21, No.3 (July
2001), pp.37-48
Healey, J. F. (2005). Statistics: A Tool for Social Research. Belmont: Thomson
Wadsworth.
Henn, M., Weinstein, M. & Foard, N. (2006). A Short Introduction to Social Research,
New Delhi: Vistaar Publications.
Hogarth, J.M., & Hilgert, M.A. (2002). Financial Knowledge, Experience and Learning
Preferences: Preliminary Results from a New Survey on Financial Literacy,
Consumer Interest Annual (Online) available at
http://www.consumerinterests.org/files/public/Financialliteracy-02.pdf.
Hogarth, J.M., Hilgert, M.A. & Beverly, S. (2003). Patterns of Financial Behaviors:
Implications for Community Educators and Policy. Presented at the Federal
Reserve System’s Community Development Research Conference, Washington,
DC.
Joireman, J., Kees, J. & Sprott, D. (2010). Concern with Immediate Consequences
Magnifies the Impact of Compulsive Buying Tendencies on College Students’
Credit Card Debt. The Journal of Consumer Affairs. Vol. 44, No. 1, 2010
59
Jim McMenamin, (1999) Financial Management: an Introduction. Taylor and Francis
(Routledge).
Kidane, A. & Mukherji, S. (2004). Characteristics of consumers targeted and neglected
by credit card companies, Financial Services Review, 13, 185-198.
King, A. S. (2004). Untangling the Effects of Credit Cards on Money Demand:
Convenience Usage vs. Borrowing, Lincoln: University of Nebraska.
Lie, C., Hunt, M., Peters, H. L., Veliu, B. & Harper, D. (2010). The “negative” credit
card effect: credit cards as spending-limiting stimuli in New Zealand, the
Psychological Record, 60, 399–412.
Littwin, A. (2008) Beyond Usury: a study of credit-card use and preference among lowincome consumers. Texas Law Review, 86, 451– 506.
Lopes, P. (2008). Credit Card Debt and Default over the Life Cycle, Journal of Money,
Credit and Banking, Vol. 40, No. 4
Mae, N. (2005). Undergraduate Students and Credit Cards 2004: An Analysis of Usage
Rates and Trends. [Online] Available: www.nelliemae.com/library/research
12.html.
Mavri, M., Angelis, V., Ioannou, G., Gaki, E. & Koufondotis, I. (2008). A two-stage
dynamic credit scoring model, based on customers’ profile and time horizon,
Journal of Financial Services Marketing, Vol. 13, 1 17–27.
Norum, P. S. (2008). The role of time preference and credit card usage in compulsive
buying behaviour, International Journal of Consumer Studies, 1470-6423.
60
Perry, V. G. (2008). Giving Credit Where Credit is Due: The Psychology of Credit
Ratings, The Journal of Behavioral Finance, 9: 15–21.
Peterson, J. (2001). The Policy Relevance of Institutional Economics.” Journal of
Economic Issues 35, no. 1, 173-184.
Pirog, S. F. & Robert, J. A. (2007). Personality and Credit Card Misuse among College
Students: The Mediating Role of Impulsiveness, Journal of Marketing Theory and
Practice, vol. 15, no. 1.
Rotich, B. (2006). Analysis of factors influencing credit card default in Kenya: A case
Study of Post Bank. School of Business and Economics – Research and
Publications.
Saunders, M., Lewis, P. & Thornhill, A. (2009). Research Methods for Business
Students. (5th ed.) Harlow: FT/Prentice Hall.
Scott, R. H. (2007). Credit Card Use and Abuse: A Veblenian Analysis, Journal of
Economic Issues, Vol. XLI, No. 2.
Stauffer, R. F. (2003). Credit cards and interest rates: theory and institutional factors,
Journal of Post Keynesian Economics / Winter 2003–4, Vol. 26, No. 2 289
Telyukova, I. A. & Wright, W. (2008). A Model of Money and Credit, with Application
to the Credit Card Debt Puzzle, Review of Economic Studies (2008) 75, 629–647.
Thomas, L. C., Ho, J. & Scherer, W. T. (2000). Time will tell: Behavioural scoring and
the dynamics of consumer credit assessment, (Online): Available:
http://ideas.repec.org/p/fth/sotoam/01-174.html
61
Tunal, H. & Tatoglu, F. Y. (2010). Factors Affecting Credit Card Uses: Evidence from
Turkey Using Tobit Model, European Journal of Economics, Finance and
Administrative Sciences, Issue 23.
Veblen, T. (1898). “Why is Economics Not an Evolutionary Science?” Cambridge
Journal of Economics 22, no. 4 ([1898] July 1998): 403-414.
Wickramasinghe, V. & Gurugamage, A. (2009).Consumer credit card ownership and
usage practices: empirical evidence from Sri Lanka, International Journal of
Consumer Studies, Blackwell Publishing Ltd.
Yang, B., James, S. & Lester, D. (2005). Reliability and validity of a short credit card
attitude scale in British and American subjects, International Journal of
Consumer Studies, 29, pp 41–46
Zhao, Y. & Song, I. (2009). Predicting New Customers’ Risk Type in the Credit Card
Market, Journal of Marketing Research, Vol. XLVI, 506–517.
Kim, T., Dunn, L. F. & Mumy, G. E. (2005). Bank Competition and Consumer Search
Over Credit Card Interest Rates, Economic Inquiry, Vol. 43, No. 2, 344-353
White, M. J. (2007). Bankruptcy Reform and Credit Cards, Journal of Economic
Perspectives-Volume 21, Number 4, Pages 175–199
Johnson, K. W. (2005). “Recent Developments in the Credit Card Market and the
Financial Obligations Ratio.” Federal Reserve Bulletin, Autumn, pp. 473–86.
Laibson, D., Andrea, R. & Jeremy, T. (2003). “A Debt Puzzle.” In Knowledge,
Information, and Expectations in Modern Macroeconomics: In Honor of Edmund
S. Phelps, ed. Philippe Aghion et al, 228–66. Princeton University Press.
62
Roszbach, K. (2004). Bank lending policy, credit scoring, and the survival of loans, The
Review of Economics and Statistics, 86(4): 946-958
Mbijiwe J. M . (2005). Analysis of factors influencing credit card default in Kenya: A
case Study of Barclays Bank of Kenya. School of Business and Economics –
Research and Publications
Mugenda, O., and Mugenda, G. (2003). Research Methods: Quantitative and Qualitative
Approaches. ACTS, Nairobi Kenya.
Mukherjee D. D., (2005): Credit Appraisal, Risk Analysis & Decision Making- An
Integrated Approach to On & Off balance sheet lending. Ketan Thakkar,
Snow White Publications Pvt. Ltd.
Ngare, E.M. (2008). A survey of credit risk management practices by commercial banks
in Kenya, Unpublished MBA Project, University of Nairobi.
Njiru G.M. (2003). Credit risk management by coffee co-operatives in Embu district,
Unpublished MBA Project, University of Nairobi.
Oldfield, G.S. and Santomero, A.M. (1997). Risk management in financial institutions,.
Sloan Management Review, Vol. 39 No. 1, pp. 33-46
63
APPENDICES
Appendix I: Questionnaire
Dear Respondent,
This is an academic research on “The Relationship between Credit Card Default Risk
And Cardholders Characteristics, Credit Card Characteristics, Behavioral Scoring
Process Among Commercial Banks in Kenya.” Credit Card Default refers to the failure
by a card holder to pay up credit card obligations by the due date thus becoming bad
debts which the bank ends up writing off. Your responses will be treated with utmost
confidentiality and findings will be used for academic purposes only. This questionnaire
is made up of five short sections that should take only a moment of your time. Kindly fill
in your responses by ticking in the appropriate box or writing your answers on the spaces
provided.
Thank you.
SECTION A: GENERAL INFORMATION
1. Gender:
Male 
Female 
2. Job Title: ____________________
3. How long have you worked at the Card Centre?
Less than 1 year 
1 – 3 years  4 – 5 years  More than 5 years 
4. What is your management level?
Junior Management Middle Management Senior Management 
5. How many years of experience do you have managing credit cards?
Less than 3 years 
3 – 5 years  5 – 10 years 
More than 10 years 
6. How many credit card defaults have you registered in the last one year?
Less than 5 
5 – 10 
11 – 20 
More than 20
7. Approximately what percentage of shopping expenses do cardholders charge to credit
card monthly?
Less than 25% 
25%-49%  50% – 74% 75% –
100%
64
8. Approximately what percentage cardholder’s credit obligation is carried forward
every month?
Less than
0- 25%
25%-49%
50% – 74%
75% – 100%
9. Which of the following describes majority of cardholder’s level of income?
0 -25,000 Kshs

100,001 - 200,000 Kshs

25,001-50,000Kshs

200,001 Kshs and over

50, 0001-100,000 Kshs

SECTION B: THE EFFECT OF CARD-HOLDER CHARACTERISTICS ON
CREDIT CARD DEFAULT
Please indicate the extent to which the following factors affect credit card default:
Very Large
Large
Small
Very small
Not at
extent
extent
extent
extent
all
1) Gender
2) Age
3) Income
4) Education
5) Lifestyle
6) Locus of control
7) Compulsiveness
8) Other cards held
9) Assets held
10) Loans held
11. Other (please specify) ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
65
SECTION C: CREDIT CARD CHARACTERISTICS THAT AFFECT
CREDIT CARD DEFAULT
Please indicate the extent to which the following factors affect credit card default
Very Large
Large
Small
Very small
extent
extent
extent
extent
Not at all
1) Interest rates
2) Penalty fees
3) Hidden costs
4) Credit limits
5) Easy access to credit
6) Convenience
7) Merchant fees
8) Transaction rewards
9)Other (please specify)------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
66
D: THE EFFECT OF BEHAVIORAL SCORING PROCESS ON CREDIT CARD
DEFAULT
Please indicate the extent to which the following factors in the behavioral scoring process
determine credit card default trends
Very
Large
Small
Very small
Large
extent
extent
extent
Not at all
extent
1) Minimum and maximum
levels of balance
2) Trend of payment by
cardholder/No. of missed
payments
3) Overdraft Frequency
4) Credit and debit turn over
5) Frequency of Defaults
6) Number of cash advances
7) Cardholder characteristics
9) Other (Please specify) ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
67
E: WHAT CAN BE DONE TO REDUCE CREDIT CARD DEFAULT
To what extent can the following factors help to reduce credit card defaults?
Very
Large
Small
Very
Large
extent
extent
small
extent
Not at all
extent
1) Consumer education
2) Credit card counseling
programs
3) Monitor credit standing
through reports
4) Exercising due diligence
5) Financial awareness
campaigns
6) Frequent card holder
appraisals
7) Providing information on
inspirational groups
8) Stiffer regulation by central
bank
9) Other (please specify) ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
68
APPENDIX II: List of Commercial Banks in Kenya
1. African Banking Corporation, Nairobi
2. Bank of Africa Kenya, Nairobi
3. Bank of Baroda, Nairobi
4. Bank of India, Nairobi (foreign owned)
5. Barclays Bank of Kenya, Nairobi (listed on NSE)
6. CFC Stanbic Bank, Nairobi (listed on NSE)
7. Charterhouse Bank Ltd, Nairobi
8. Chase Bank Ltd, Nairobi
9. Citibank, Nairobi (foreign owned)
10. City Finance Bank, Nairobi
11. Commercial Bank of Africa, Nairobi
12. Consolidated Bank of Kenya Ltd, Nairobi (gov)
13. Co-operative Bank of Kenya, Nairobi
14. Credit Bank Ltd, Nairobi
15. Development Bank of Kenya, Nairobi
16. Diamond Trust Bank, Nairobi
17. Dubai Bank Kenya Ltd, Nairobi
18. Equatorial Commercial Bank Ltd, Nairobi
19. Equity Bank, Nairobi
20. Family Bank, Nairobi
21. Fidelity (Commercial) Bank Ltd, Nairobi
22. Fina Bank Ltd, Nairobi
23. First Community Bank Ltd, Nairobi
24. Giro Commercial Bank Ltd, Nairobi
25. Guardian Bank, Nairobi
26. Gulf African Bank Ltd, Nairobi
27. Habib Bank A.G. Zurich, Nairobi (foreign owned)
28. Habib Bank Ltd, Nairobi (foreign owned)
29. Housing Finance Co. Ltd, Nairobi (gov) (listed on NSE)
30. I&M Bank Ltd (former Investment & Mortgages Bank Ltd), Nairobi
69
31. Imperial Bank, Nairobi
32. Kenya Commercial Bank Ltd, Nairobi (gov) (listed on NSE)
33. K-Rep Bank Ltd, Nairobi
34. Middle East Bank, Nairobi
35. National Bank of Kenya, Nairobi (gov)
36. National Industrial Credit Bank Ltd (NIB Bank), Nairobi (listed on NSE)
37. Oriental Commercial Bank Ltd, Nairobi
38. Paramount Universal Bank Ltd, Nairobi
39. Prime Bank Ltd, Nairobi
40. Southern Credit Banking Corp. Ltd, Nairobi
41. Standard Chartered Bank , Nairobi (listed on NSE)
42. Trans-National Bank Ltd, Nairobi
43. UBA Kenya Bank Ltd., Nairobi
44. Victoria Commercial Bank Ltd, Nairobi
Source: (CBK, 2010)
70
APPENDIX III: Correlation Data
Correlation between Card Holder Characteristics and Default
Credit Card
Default
Spearm
an's rho
Credit
Card
Default
Correlation
Coefficient
Sig. (2tailed)
N
1
gender
2
Age
3
Income
4
Education
5
lifestyle
6
Selfcontrol
7
Cards held
8
Assets
held
9
Loans held

Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
1
2
3
4
5
6
7
8
9
1.000
.
32
-.410(*)
1.000
.020
.
32
32
-.093
.311
1.000
.614
.084
.
32
32
32
-.763**
.106
-.008
1.000
.000
.565
.965
.
32
32
32
32
.006
.181
.423(*)
.191
1.000
.975
.323
.016
.295
.
32
32
32
32
32
.138
-.081
-.070
-.016
-.157
1.000
.451
.660
.701
.931
.390
.
32
32
32
32
32
32
-.181
.346
.259
.167
-.037
-.096
1.00
0
.321
.052
.153
.361
.842
.600
.
32
32
32
32
32
32
32
.297
-.198
-.021
-.029
-.177
-.178
.029
1.00
0
.099
.277
.909
.875
.331
.329
.877
.
32
32
32
32
32
32
32
32
.108
.000
-.036
.100
-.041
.131
.115
.178
1.000
.556
1.000
.844
.587
.825
.475
.530
.330
.
32
32
32
32
32
32
32
32
32
-.093
.240
.051
-.056
-.334
-.031
.308
.326
.264
1.000
.614
.187
.783
.761
.062
.868
.086
.069
.144
.
32
32
32
32
32
32
32
32
32
32
Correlation is significant at the 0.05 level (2-tailed
71
Correlation between Credit Card Characteristics and Default
Credit
card
default
Spearma
n's rho
Credit
card
default
Correlation
Coefficient
Sig. (2tailed)
N
1
2
3
4
5
6
7
Interest
rate
Penalty
fees
Hidden
costs
Credit
limit
Credit
access
Convenie
nce
Merchant
fees
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
Correlation
Coefficient
Sig. (2tailed)
N
8
Transacti
on
rewards
Correlation
Coefficient
Sig. (2tailed)
N
1
2
3
4
5
6
7
8
1.000
.
32
.123
1.000
.502
.
32
32
.645(*
*)
1.00
0
.554
.000
.
32
32
32
.236
.210
.006
1.00
0
.194
.248
.973
.
32
32
32
32
.181
-.009
-.073
-.095
1.00
0
.321
.962
.690
.607
.
32
32
32
32
32
-.131
.078
-.150
.229
.049
1.000
.475
.672
.411
.208
.788
.
32
32
32
32
32
.042
.232
.229
.166
.140
32
.567(*
*)
.818
.201
.207
.364
.445
.001
.
32
32
32
32
32
32
32
.038
.209
-.020
.164
.301
.104
.128
1.00
0
.835
.251
.912
.370
.094
.572
.484
.
32
32
32
32
32
32
32
32
.027
-.229
-.152
.233
.235
.296
.393(*)
.288
1.000
.883
.207
.406
.200
.195
.099
.026
.110
.
32
32
32
32
32
32
32
32
32
.109
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
72
1.000