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

ii

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

iii

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.

iv

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.

v

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

vi

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

vii

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

viii

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

ix

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.

1

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).

2

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)

3

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)

4

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).

5

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:

6

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.

7

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.

8

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.

9

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.

10

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.

11

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).

12

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

13

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

14

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 risk-

taking 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

15

(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

16

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.

17

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.

18

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 (Abdul-

Muhmin 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.

19

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

20

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.

21

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

22

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.

23

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.

24

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.

25

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.

26

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

27

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

28

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

29

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 semi-

structured (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

30

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.

31

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

DistributionLength of service

Frequency PercentCumulative

PercentLess than one Year

9 28.1 28.1

1-3 Years 11 34.4 62.54-5 Years 6 18.8 81.3More than 5 Years

6 18.8 100.0

Total 32 100.0

Source: Author Computation 2011

32

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

DistributionResponses

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.

33

Table 4.2.3 Years of Experience Managing Credit Cards

Distribution

Experience in Years Frequency Percent

Cumulative

Percent

Less than 3

years12 37.5 37.5

3-5 years 11 34.4 71.9

5-10 years 5 15.6 87.5

more than 10

years4 12.5 100.0

Total 32 100.0

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.

34

Table 4.2.4 Percentage of shopping expenses Charged to Credit Cards

DistributionPercentage of shopping

expenses

Frequency Percent

Cumulative

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

Total 32 100.0

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

Distribution Percentage of credit card

obligations

Frequency Percent

Cumulative

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

35

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

DistributionLevel of income

Frequency PercentCumulative

Percent0-25,000 Kshs 4 12.5 12.525,001-50,000 Kshs 22 68.8 81.350,001-100,000 Kshs

6 18.8 100.0

Total 32 100.0Source: 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.

36

Table 4.3.1 The Effect of Card-Holder Characteristics on Credit Card Default

Card holder Characteristic

Responses DistributionFrequency

Percent Cumulative Percent

Not all 1 3.1 3.1Very small extent 7 21.9 25.0Small extent 11 34.4 59.4Large extent 9 28.1 87.5

Gender

Very large extent 4 12.5 100.0Very small extent 4 12.5 12.5Small extent 13 40.6 53.1Large extent 13 40.6 93.8

Age

Very Large extent 2 6.3 100.0Small extent 1 3.1 3.1Large extent 12 37.5 40.6

Income

Very large extent 19 59.4 100.0Small extent 12 37.5 37.5Large extent 10 31.3 68.8

Education

Very large extent 10 31.3 100.0Small extent 4 12.5 12.5Large extent 16 50.0 62.5

Lifestyle

Very large extent 12 37.5 100.0Self control Very small extent 4 12.5 12.5

Small extent 6 18.8 31.3Large extent 9 28.1 59.4Very large extent 13 40.6 100.0

Cards held Not at all 1 3.1 3.1Very small extent 8 25.0 28.1Small extent 8 25.0 53.1Large extent 10 31.3 84.4Very large extent 5 15.6 100.0

Assets held Not at all 6 18.8 18.8Very small extent 14 43 62.5Small extent 8 25.0 87.5Large extent 4 12.5 100.0

Loans held Not at all 5 15.6 15.6Very small extent 8 25.0 40.6Small extent 5 15.6 56.3Large extent 8 25.0 81.3Very large extent 6 18.8 100.0

Source: Author Computation 2011

37

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

38

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).

39

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.

40

Table 4.3.2 Credit Card Characteristics that Affect Credit Card Default

Credit card Characteristic

Responses DistributionFrequency

Percent Cumulative Percent

Very small extent 6 18.8 18.8Small extent 7 21.9 40.6Large extent 9 28.1 68.8

Interest Rate

Very large extent 10 31.3 100.0Very small extent 4 12.5 12.5Small extent 8 25.0 37.5Large extent 8 25.0 62.5

Penalty fees

Very Large extent 12 37.5 100.0Small extent 1 3.1 3.1Large extent 12 37.5 40.6

Income

Very large extent 19 59.4 100.0Not at all 8 25.0 37.5Very small extent 10 31.3 56.3Small extent 6 18.8 75.0Large extent 3 9.4 84.4

Hidden costs

Very large extent 5 15.6 100.0Not at all 2 6.3 6.3Very small extent 9 28.1 34.4Small extent 7 21.9 56.3large extent 8 25.0 81.3

Credit limits

Very large extent 6 18.8 100.0Very small extent 4 12.5 12.5Small extent 6 18.8 31.3Large extent 13 40.6 71.9

Credit Access

Very large extent 9 28.1 100.0Very small extent 6 18.8 18.8Small extent 8 25.0 43.8Large extent 12 37.5 81.3

Convenience

Very large extent 6 18.8 100.0Not at all 12 37.5 37.5Very small extent 9 28.1 65.6Small extent 6 18.8 84.4Large extent 2 6.3 90.6

Merchant fees

Very large extent 3 9.4 100.0Not at all 13 40.6 40.6Very small extent 9 28.1 68.8Small extent 5 15.6 84.4Large extent 3 9.4 93.8

Transaction Rewards

Very large extent 2 6.3 100.0Source: Author Computation 2011

41

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

42

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.

43

Table 4.3.3 The Effect of Behavioral Scoring Process on Credit Card Default

Behavioral Scoring Factor

Responses Distribution

Frequency Percent Cumulative Percent

Not at all 4 12.5 12.5Very small extent 5 15.6 28.1Small extent 10 31.3 59.4Large extent 9 28.1 87.5

Minimum and Maximum Balances

Very large extent 4 12.5 100.0Very Small extent 1 3.1 3.1Small extent 7 21.9 25.0large extent 12 37.5 62.5

Payment Trends

Very large extent 12 37.5 100.0Very small extent 2 6.3 6.3Small extent 8 25.0 31.3Large extent 14 43.8 75.0

Overdraft Frequency

very large extent 8 25.0 100.0Very small extent 3 9.4 9.4Small extent 12 37.5 46.9Large extent 8 25.0 71.9

Credit and debit turnover

Very large extent 9 28.1 100.0Not at all 1 3.1 3.1Small extent 9 28.1 31.3Large extent 12 37.5 68.8

Cash advances

Very large extent 10 31.3 100.0

Source: Author Computation 2011

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

44

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.

45

Table 4.3.4 What can be done to Reduce Credit Card Default

What can be done to reduce credit card default

Responses DistributionFrequency

Percent Cumulative Percent

Small extent 2 6.3 6.3Large extent 14 43.8 50.0

Consumer education

Very large extent 16 50.0 100.0Not at all 1 3.1 3.1Very small extent 3 9.4 12.5Small extent 10 31.3 43.8Large extent 14 43.8 87.5

Counseling Programs

Very large extent 4 12.5 100.0Very small extent 3 9.4 9.4Small extent 2 6.3 15.6Large extent 18 56.3 71.9

Monitoring credit Standing

Very large extent 9 28.1 100.0Small extent 1 3.1 3.1Large extent 19 59.4 62.5

Due diligence

Large extent 12 37.5 100.0Very small extent 6 18.8 18.8Small extent 3 9.4 28.1Large extent 13 40.6 68.8

Financial awareness

Very large extent 10 31.3 100.0Not at all 2 6.3 6.3Very small extent 1 3.1 9.4Small extent 8 25.0 34.4Large extent 19 59.4 93.8

Frequent card appraisals

Very large extent 2 6.3 100.0Not at all 6 18.8 18.8Very small extent 8 25.0 43.8Small extent 14 43.8 87.5

Influence of inspirational groups

Large extent 4 12.5 100Not at all 6 18.8 18.8Very small extent 8 25.0 43.8Small extent 14 43.8 87.5

Regulation by Central Bank

Large extent 4 12.5 100.0

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.

46

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.

47

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

characteristic

Extent of Influence Credit card default with increase

Gender Insignificant -

Age Significant Decrease

Income Significant Decrease

Lifestyle Significant -

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

48

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.

49

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

characteristic

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

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

50

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, cash-

back 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.

51

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.

52

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

53

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.

54

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

55

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.

56

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.

57

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64

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%

65

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

extent

Large

extent

Small

extent

Very small

extent

Not at

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) ----------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------

66

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

extent

Large

extent

Small

extent

Very small

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)-------------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------

67

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

extent

Large

extent

Small

extent

Very small

extent

Not at all

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) -----------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------

68

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

extent

Large

extent

Small

extent

Very

small

extent

Not at all

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) -----------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------

69

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

70

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)

71

APPENDIX III: Correlation Data

Correlation between Card Holder Characteristics and Default

Credit CardDefault 1 2 3 4 5 6 7 8 9

Spearman's rho

Credit CardDefault

Correlation Coefficient 1.000

Sig. (2-tailed)

.

N 32 1 gender Correlation

Coefficient-.410(*) 1.000

Sig. (2-tailed)

.020 .

N 32 32 2 Age Correlation

Coefficient-.093 .311 1.000

Sig. (2-tailed)

.614 .084 .

N 32 32 32 3 Income Correlation

Coefficient-.763** .106 -.008 1.000

Sig. (2-tailed)

.000 .565 .965 .

N 32 32 32 32 4 Education Correlation

Coefficient.006 .181 .423(*) .191 1.000

Sig. (2-tailed)

.975 .323 .016 .295 .

N 32 32 32 32 32 5 lifestyle Correlation

Coefficient.138 -.081 -.070 -.016 -.157 1.000

Sig. (2-tailed)

.451 .660 .701 .931 .390 .

N 32 32 32 32 32 32 6 Self-

controlCorrelation Coefficient

-.181 .346 .259 .167 -.037 -.0961.00

0Sig. (2-tailed)

.321 .052 .153 .361 .842 .600 .

N 32 32 32 32 32 32 32 7 Cards held Correlation

Coefficient.297 -.198 -.021 -.029 -.177 -.178 .029

1.000

Sig. (2-tailed)

.099 .277 .909 .875 .331 .329 .877 .

N 32 32 32 32 32 32 32 32 8 Assets

heldCorrelation Coefficient

.108 .000 -.036 .100 -.041 .131 .115 .178 1.000

Sig. (2-tailed)

.556 1.000 .844 .587 .825 .475 .530 .330 .

N 32 32 32 32 32 32 32 32 32 9 Loans held Correlation

Coefficient-.093 .240 .051 -.056 -.334 -.031 .308 .326 .264 1.000

Sig. (2-tailed)

.614 .187 .783 .761 .062 .868 .086 .069 .144 .

N 32 32 32 32 32 32 32 32 32 32

Correlation is significant at the 0.05 level (2-tailed

72

Correlation between Credit Card Characteristics and Default

Credit card

default 1 2 3 4 5 6 7 8Spearman's rho

Credit card default

Correlation Coefficient 1.000

Sig. (2-tailed)

.

N 32 1 Interest

rateCorrelation Coefficient

.123 1.000

Sig. (2-tailed)

.502 .

N 32 32 2 Penalty

feesCorrelation Coefficient

.109.645(*

*)1.00

0Sig. (2-tailed)

.554 .000 .

N 32 32 32 3 Hidden

costsCorrelation Coefficient

.236 .210 .0061.00

0Sig. (2-tailed)

.194 .248 .973 .

N 32 32 32 32 4 Credit

limitCorrelation Coefficient

.181 -.009 -.073 -.0951.00

0Sig. (2-tailed)

.321 .962 .690 .607 .

N 32 32 32 32 32 5 Credit

accessCorrelation Coefficient

-.131 .078 -.150 .229 .049 1.000

Sig. (2-tailed)

.475 .672 .411 .208 .788 .

N 32 32 32 32 32 32 6 Convenie

nceCorrelation Coefficient

.042 .232 .229 .166 .140.567(*

*)1.000

Sig. (2-tailed)

.818 .201 .207 .364 .445 .001 .

N 32 32 32 32 32 32 32 7 Merchant

feesCorrelation Coefficient

.038 .209 -.020 .164 .301 .104 .1281.00

0Sig. (2-tailed) .835 .251 .912 .370 .094 .572 .484 .

N 32 32 32 32 32 32 32 32 8 Transacti

on rewards

Correlation Coefficient .027 -.229 -.152 .233 .235 .296 .393(*) .288 1.000

Sig. (2-tailed)

.883 .207 .406 .200 .195 .099 .026 .110 .

N 32 32 32 32 32 32 32 32 32

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).