Introduction to consumer credit and credit scoring

Thomas, Lyn C.

doi:10.1093/acprof:oso/9780199232130.003.0001

Consumer Credit Models: Pricing, Profit and Portfolios

Lyn C. Thomas

https://doi.org/10.1093/acprof:oso/9780199232130.001.1

Published online:

01 May 2009

Published in print:

29 January 2009

Online ISBN:

9780191715914

Print ISBN:

9780199232130

- Copyright Page
  - Notes
  - Notes
- Dedication
  - Notes
  - Notes
- Preface
  - Notes
  - Notes
- Acknowledgements
  - Notes
  - Notes
- 1.1 Introduction: importance and impact of consumer credit 1.1 Introduction: importance and impact of consumer credit
- 1.2 Historical background of default-based credit scoring 1.2 Historical background of default-based credit scoring
- 1.3 Objectives of lenders 1.3 Objectives of lenders
  - Objectives of lender Objectives of lender
  - Lending process Lending process
- 1.4 Tools for modelling lending decisions: influence diagrams, decision trees, and strategy trees 1.4 Tools for modelling lending decisions: influence diagrams, decision trees, and strategy trees
  - Influence diagrams Influence diagrams
  - Application decisions in consumer credit Application decisions in consumer credit
  - Decision trees Decision trees
  - Decision trees for consumer credit decisions Decision trees for consumer credit decisions
  - Example 1.4.1 Example 1.4.1
  - Strategy trees Strategy trees
- 1.5 Probabilities, odds, information, and scores 1.5 Probabilities, odds, information, and scores
  - Probabilities and odds Probabilities and odds
  - Example 1.5.1 Probabilities and likelihoods from Bank of Southampton (Appendix B) data Example 1.5.1 Probabilities and likelihoods from Bank of Southampton (Appendix B) data
  - Information odds and population odds Information odds and population odds
  - Example 1.5.1 (continued) Example 1.5.1 (continued)
  - Forecasting using information odds Forecasting using information odds
  - Score as a sufficient statistic Score as a sufficient statistic
  - Log odds score Log odds score
  - Log odds score separates weights of evidence and population odds Log odds score separates weights of evidence and population odds
  - Example 1.5.2 Log odds score using Bank of Southampton data (Appendix B) Example 1.5.2 Log odds score using Bank of Southampton data (Appendix B)
  - Naïve Bayes' scorecard building Naïve Bayes' scorecard building
  - Example 1.5.2 (continued) Naïve Bayes' applied to Southampton Bank data Example 1.5.2 (continued) Naïve Bayes' applied to Southampton Bank data
  - Score distributions Score distributions
- 1.6 Modifying scores: scaling, multiple levels, and time dependency 1.6 Modifying scores: scaling, multiple levels, and time dependency
  - Scaling of natural scores Scaling of natural scores
  - Example 1.6.1 Scaling of scores from run book in Appendix A Example 1.6.1 Scaling of scores from run book in Appendix A
  - Scaling normally distributed scores to make them log odds scores Scaling normally distributed scores to make them log odds scores
  - Multiple-level scorecards using information from different sources Multiple-level scorecards using information from different sources
  - Hazard rates and time-dependent scores Hazard rates and time-dependent scores
  - Example 1.6.2 Exponential default rate: constant hazard rate Example 1.6.2 Exponential default rate: constant hazard rate
  - Example 1.6.3 Weibull distribution hazard rate Example 1.6.3 Weibull distribution hazard rate
  - Discrete time hazard probabilities Discrete time hazard probabilities
- 1.7 Lending returns and costs 1.7 Lending returns and costs
  - Rate of return model for one period loan Rate of return model for one period loan
  - Calculating risk rates using one period rate of return model on corporate bonds Calculating risk rates using one period rate of return model on corporate bonds
  - Two ways of measuring consumer lending – profitability and rate of return Two ways of measuring consumer lending – profitability and rate of return
  - Two period rate of return model with spot and short rates Two period rate of return model with spot and short rates
  - Multi-period loans Multi-period loans
- 1.8 Fundamentals of scorecard building 1.8 Fundamentals of scorecard building
  - Basic approach to scorecard development Basic approach to scorecard development
  - Reject inference Reject inference
  - Behaviour scoring Behaviour scoring
  - Data sample Data sample
  - Data validation and cleaning Data validation and cleaning
  - Segmentation Segmentation
  - Development and validation samples Development and validation samples
  - Cutting down the number of characteristics considered Cutting down the number of characteristics considered
  - Coarse classifying characteristics Coarse classifying characteristics
  - Example 1.8.1 Coarse classifying using the Bank of Southampton data Example 1.8.1 Coarse classifying using the Bank of Southampton data
  - Chi-square and information value statistics Chi-square and information value statistics
  - Example 1.8.2 Chi-square calculations on age from Bank of Southampton data (Appendix B) Example 1.8.2 Chi-square calculations on age from Bank of Southampton data (Appendix B)
  - Example 1.8.3 Information value using age from the Bank of Southampton data Example 1.8.3 Information value using age from the Bank of Southampton data
  - Transforming coarse classified characteristics into new variables Transforming coarse classified characteristics into new variables
  - Example 1.8.4 Constructing weights of evidence variables on Bank of Southampton data Example 1.8.4 Constructing weights of evidence variables on Bank of Southampton data
  - Building final scorecard and validating it Building final scorecard and validating it
- 1.9 Using logistic regression to build scorecards 1.9 Using logistic regression to build scorecards
  - Example 1.9.1 Building a logistic regression scorecard using the Bank of Southampton Bank data of Appendix B Example 1.9.1 Building a logistic regression scorecard using the Bank of Southampton Bank data of Appendix B
- 1.10 Other scorecard-building approaches 1.10 Other scorecard-building approaches
  - Linear regression Linear regression
  - Example 1.10.1 Using linear regression to build a scorecard using Appendix B data Example 1.10.1 Using linear regression to build a scorecard using Appendix B data
  - Maximizing the divergence Maximizing the divergence
  - Example 1.10.2 Maximizing divergence using Bank of Southampton data (Appendix B) Example 1.10.2 Maximizing divergence using Bank of Southampton data (Appendix B)
  - Linear programming Linear programming
  - Example 1.10.3 Building a scorecard by linear programming using the Bank of Southampton data in Appendix B Example 1.10.3 Building a scorecard by linear programming using the Bank of Southampton data in Appendix B
  - Classification trees (recursive partitioning algorithms) Classification trees (recursive partitioning algorithms)
- Notes
- Notes
2 Measurement of scoring systems
- Notes
- Notes
3 Risk-based pricing
- Notes
- Notes
4 Profit scoring and dynamic models
- Notes
- Notes
5 Portfolio credit risk and the Basel Accord
- Notes
- Notes
- Appendices
  - Notes
  - Notes
- References
  - Notes
  - Notes
- Index
  - Notes
  - Notes

Chapter

1 Introduction to consumer credit and credit scoring

Get access

https://doi.org/10.1093/acprof:oso/9780199232130.003.0001

Pages

1–99

Published:

January 2009

Abstract

This chapter outlines what is meant by a credit score, why it is an integral part of the decision process in lending to consumers, and how credit scoring systems are built. After describing the historical development of consumer credit and credit scoring, decision trees are used to model the credit granting process. In particular, return on capital based models and their connection with the tradition expected profit model are introduced. The chapter defines what is meant by a credit score, why log odds scores have such useful properties, and how one can extend the definition of a credit score to time dependent scores. It then goes through the development process of building a scorecard, discussing sample construction, reject inference, coarse classification, and variable selection. It concludes by looking at the different methodologies for building a scorecard such as logistic regression, linear regression, classification tress, and linear programming.

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Contents

1 Introduction to consumer credit and credit scoring

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