Assessing and Evaluating the Effectiveness of Different Credit Scoring Models using ROC Curve Analysis

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ACKNOWLEDGEMENT

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University (Bardhan, 2001, 467).

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ABSTRACT

The aim of the study is to Assessing and Evaluating the Effectiveness of Different Credit Scoring Models using ROC Curve Analysis for one of the small size bank of Thailand. The aim of this dissertation is to explore the performance of credit scoring models by assessing and evaluating the effectiveness of different credit scoring models of continuous outcomes using the Receiver Operating Characteristic (ROC) curve analysis to compare and assess their discriminatory accuracy in the small sized bank of Thailand.

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION1

1.1Introduction1

1.2Problem Statement2

1.3Research Objective3

CHAPTER 2: LITERATURE REVIEW5

2.1 Case Study Examples12

CHAPTER 3: DATA AND METHODOLOGY15

3.1 Research Design15

3.2 Qualitative and Quantitative Data17

3.3 Sample Selection18

3.4 Methods of Model Evaluation20

CHAPTER 4 & 5: RESULTS AND ANALYSIS26

4.1 Discussion31

CHAPTER 6: CONCLUSION32

REFERENCES34

CHAPTER 1: INTRODUCTION

Introduction

The proper classification of applicants is of vital importance for determining the granting of credit facilities. Historically, statistical classification models have been used by financial institutions as a major tool to help on granting credit to clients.

The consolidation of the use of classification models occurred in the 90s, when changes in the world scene, such as deregulation of interest rates and exchange rates, increase in liquidity and in bank competition, made financial institutions more and more worried about credit risk, i.e., the risk they were running when accepting someone as their client. The granting of credit started to be more important in the profitability of companies in the financial sector, becoming one of the main sources of revenue for banks and financial institutions in general. Due to this fact, this sector of the economy realized that it was highly recommended to increase the amount of allocated resources without losing the agility and quality of credits, at which point the contribution of statistical modelling is essential.

Classification models for credit scoring are based on databases of relevant client information, with the financial performance of clients evaluated from the time when the client-company relationship began as a dichotomic classification. The goal of credit scoring models is to classify loan clients to either good credit or bad credit (Lee, Chiu, Lu, & Chen, 2002), predicting the bad payers (Lim & Sohn, 2007).

In this context, discriminant analysis, regression trees, logistic regression, logistic regression with state-dependent sample selection and neural networks are among the most widely used classification models. In fact, logistic regression is still very used in building and developing credit scoring models ([Caouette et al., 1998], [Desai et al., 1996], [Hand and Henley, 1997] and [Sarlija et al., 2004]). Generally, the best technique for all data sets ...