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Loughborough UniversityInstitutional Repository

[Credit] scoring : predicting,understanding andexplaining consumer

behaviour

This item was submitted to Loughborough University's Institutional Repositoryby the/an author.

Additional Information:

A Doctoral Thesis. Submitted in partial fulfilment of the requirementsfor the award of Doctor of Philosophy of Loughborough University

Metadata Record: https://dspace.lboro.ac.uk/2134/13053

Publisher: c Robert Hamilton

Please cite the published version.

https://dspace.lboro.ac.uk/2134/13053

This item was submitted to Loughborough University as a PhD thesis by the author and is made available in the Institutional Repository

(https://dspace.lboro.ac.uk/) under the following Creative Commons Licence conditions.

For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/

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[CREDIT] SCORING: PREDICTING, UNDERSTANDING AND EXPLAINING CONSUMER

BEHAVIOUR

by

ROBERT HAMILTON

A Doctoral Thes1s

Submitted in partial fulfilment of the requ1rements

for the award of

Doctor of Philosophy

of Loughborough University

Loughborough University

Busmess School

August 2005

by Robert Ham1lton 2005

" Loughborough Univcr~ity

Pilkongton Ltbrary

Date Sfl 2

[CREDIT] SCORING: PREDICTING, UNDERSTANDING AND EXPLAINING CONSUMER

BEHAVIOUR

By

ROBERT HAMILTON

ABSTRACT

Th1s thesis stems from my research mto the broad area of (credit) sconng and the

pred1ctmg, understandmg and explainmg of consumer behaviour. This research statted at

the Univers1ty of Edmburgh on an ESRC funded project m 1988.

This work, wh1ch is being subm1tted as the pat11al fulfilment of the requirements for the

award of Doctor of Philosophy of Loughborough Umvers1ty, cons1sts of an introductory

chapter and a selection of papers publtshed 1991 - 2001 (mclusive). The papers address

some of the key 1ssues and areas of interest and concern ansmg from the rap1dly evolving

and expandmg cred1t (card) market and the h1ghly compet1t1ve nature of the credit mdustry.

These features were patticularly ev1dent during the late 1980's and throughout the 90's

Chapter One prov1des a general background to the research and outlines some of the key

(practical) issues mvolved m butldmg a (credit) scorecard Additionally, 1t provides a bnef

summary of each of the research papers appearing in full m Chapters 2- 9 (inclusive) and

ends w1th some generall1m1tattOns and conclusiOns. The research papers appeanng m

Chapters 2-9 (mclus1ve) are all concerned w1th predictmg, understandmg and explammg

different types of consumer behaviour m relat1on to the use of cred1t cards. For example

d1scnminating between 'GOOD' and 'BAD' repayers of cred1t card debt on the bas1s of

different defintt1ons of good and bad, the ident1ficat1on of 'slow payers' usmg different

stat1st1cal methods; examining the charactenst1cs of cred1t card users and non-users, and

1dent1fying the characteristics of credit card holders most l1kely to return thetr cred1t card.

Keywords: Credit scoring; Behavioural scoring; Discriminant analysis, Cred1t cards;

Scorecard

11

ACKNO~EDGEMENTS

This research has taken place over a number of years and to the many people that have

helped, contnbuted and supported my research I say a very grateful and heartfelt thank

you.

In no particular order I would especially hke to mention. Professor Jonathan Crook;

Professor Lyn Thomas, Mr. Dav1d Edelman; Professor Barry Howcroft; Professor I an

Monson; Dr. David Coates; the ESRC, the vanous financial institutions who prov1ded the

data and in some cases funding for the research; the various journal ed1tors and the

anonymous referees; the secretaries who helped prepare the vanous articles; colleagues at

the Umvers1ty of Edinburgh and Loughborough Umversity Business School; the many

practitioners I have met over the years and Kay Harns for carefully, pat1ently and diligently

putt1ng all the matenal together for this thesis

I would also hke to thank those present at my Oral Exammat1on: Professor I an Davidson,

D1rector, Loughborough University Business School for h1s continued support, Professor

Chnstine Ennew and Professor Gary Akehurst for their encouragement and constructive

comments

li1

DEDICATION

To my parents and especially my mother, Elizabeth McKean Ham1lton (nee Thaw), who

always believed and trusted in me and was always there to support me.

To Ruth Elizabeth, my daughter, for making each and every day rewarding

To lrene for her support, encouragement and belief

iv

GLOSSARY OF TERMS

Attribute: A set or range of values that a charactenstic (vanable) can attain.

Behavioural scoring: A scoring system for assessmg the performance of an exist1ng

account (cardholder).

Bespoke cred1t scorecard: A scorecard whose development IS based on the credit grantor's

own expenence of the product for which their use is Intended Normally this involves using

the cred1t grantor's own data collected from the cred1t grantor's own accounts.

Categorical variable (characteristic): A vanable that has a discrete set of possible answers

Charactenst1c: Any variable that could appear m a scorecard. Characteristics are made up

of Attnbutes.

Continuous variable (charactenst1c): A vanable whose range of possible values is numenc

and very large (infimte)

Credit scoring: The term for us1ng a linear predictive model for assessing and ranking

customers or applicants for credit. Typically used more generally to include all types of

predictive cred1t models used for decision making 1n the accepUreject Situation.

Generic scorecard: A scorecard that has been generated when there is insufficient data to

build a bespoke scorecard. These scorecards can be based upon the expenence of other

cred1t grantors and/or of another cred1t product.

L1near Discnminant Analysis: A statistical technique that Involves deriving the linear

combination of two or more independent vanables (characteristics) that will discriminate

best between the a prion defined groups (e.g. goods and bads).

VI

Logistic Regression A logistiC form of regression analys1s in which the dependent vanable

takes one of two values, typ1cally 0 or 1.

Revolvers. Typ1cally cred1t card users that pay less than the prev1ous months outstanding

balance by the due date

Robust scorecard: A scorecard that Will perform as expected for a reasonable length of

time

Scorecard A table listing the characteristics that prov1de predict1ve Information in the

sconng system, the attnbutes of each characteristic and the score pomts (weights)

associated With each attribute.

SOURCE: Various

vii

CONTENTS

ABSTRACT

ACKNOWLEDGEMENTS

DEDICATION

CERTIFICATE OF ORIGINALITY

GLOSSARY OF TERMS

CONTENTS

LIST OF TABLES

LIST OF FIGURES

CHAPTER

1 Introduction, Structure, Methodology and Conclusions

2

Introduction

Structure of the Thes1s

General Methodology of (Cred1t) Sconng

Summary of the Research Papers

Conclusions

References

A Comparison of Discriminators under Alternative Definitions of

Credit Default

Introduction

Data and Vanables

Results

Conclusions

References

Vlll

PAGE

ii

i 11

IV

V

Vi-VIi

VIII-XI

XII-XV

xvi

1

1

3

4

17

31

35

39

40

47

53

65

69

3

4

5

Methods for Credit Scoring Applied to Slow Payers

Introduction

Methodologies for Cred1t Sconng

Results

Conclusions

References

The Degradation of the Scorecard over the Business Cycle

Introduction

Methodology

Changes 1n Discriminatmg Functions

Effects of Changes in Cut-off Scores

DISCUSSIOn

Conclusion

References

A Comparison of a Credit Scoring Model with a Credit

Performance Model

Introduction

Vanables and Methodology

Results

Conclus1on

References

ix

77

78

80

87

92

93

96

98

99

102

105

113

114

118

121

122

124

128

141

147

6

7

8

9

Credit Card Holders: Characteristics of Users and Non-Users

Introduction

Data, Variables a

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