Readings in Credit Scoring Foundations, Developments, and Aims

, by Thomas, Lyn C.; Edelman, David B.; Crook, Jonathan N.

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ISBN: 9780198527978 | 0198527977
Cover: Hardcover
Copyright: 9/9/2004

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Credit scoring is one of the most successful applications of statistical and management science techniques in finance in the last forty years. This unique collection of recent papers, with comments by experts in the field, provides excellent coverage of recent developments, advances and sims in credit scoring. Aimed at statisticians, economists, operational researchers and mathematicians working in both industry and academia, and to all working on credit scoring and data mining, it is an invaluable source of reference.

List of Contributors

xiii

Part I. Historical Development of Credit and Behavioural Scoring

1. Legal, social, and economic issues in implementing scoring in the United States

(12)

R.W. Johnson

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 19-32. OUP, Oxford 2002.

2. Problems in applying discriminant analysis in credit-scoring models

(16)

Robert A. Eisenbeis

Journal of Banking and Finance, 2, 205-19, 1978.

3. Behaviour scoring and adaptive control systems

(18)

M.A. Hopper and E.M. Lewis

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 257-76. OUP, Oxford 2002.

Part II. Objectives and Measures in Credit Scoring

4. Measures for comparing scoring systems

(12)

A.D. Wilkie

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 123-38. OUP, Oxford 2002.

5. The use of affordability data-does it add real value?

(10)

George Wilkinson and Jon Tingay

Proceedings of Credit Scoring and Credit Control VII, Credit Research Centre, University of Edinburgh, 2001.

6. Improving lender offers using consumer preferences

(20)

Ralph L. Keeney and Robert M. Oliver

Proceedings of Credit Scoring and Credit Control VIII, Credit Research Centre, University of Edinburgh, 2003.

Part III. Practical Implementation of Scoring Systems

7. Updating scorecards: removing the mystique

(18)

Alan Lucas

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 179-96. OUP, Oxford 1992.

8. Efficient frontier cut-off policies in credit portfolios

111

(22)

Robert M. Oliver and E. Wells

Journal of Operational Research Society, 52, 1025-33, 2001.

Part IV. Features of Scoring

9. Can reject inference ever work?

133

(14)

David J. Hand and William E. Henley

IMA Journal of Mathematics Applied in Business and Industry, 5, 45-55, 1993/4.

10. The flat-maximum effect and generic linear scoring models: a test

147

(14)

George A. Overstreet, Jr, Edwin L. Bradley, Jr:, and Robert S. Kemp, Jr.

IMA Journal of Mathematics Applied in Business and Industry, 4, 97-109, 1992.

11. The degradation of the scorecard over the business cycle

161

(16)

Jonathan N. Crook, Lyn C. Thomas, and Robert Hamilton

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 111-23. OUP, Oxford 2002.

12. Inferring the inferred

177

(12)

Gaynor Bennett, Graham Plata, and Jane Crossley

IMA Journal of Mathematics Applied in Business and Industry, 7, 327-38, 1996.

Part V. Other Applications of Scoring in Credit Risk

13. Detecting credit card fraud using expert systems

189

(6)

Kevin J. Leonard

Computers and Industrial Engineering, 25 (1-4), 103-6, 1993.

14. A single European scorecard? Does data predict differently across Europe: An Experian Scorex investigation

195

(10)

Graham Platts and Ian Howe

Proceedings of Credit Scoring and Credit Control V, Credit Research Centre, University of Edinburgh, 1997.

15. Small sample scoring

205

(30)

Alan Lucas and Joanna Powell

Proceedings of Credit Scoring and Credit Control V, Credit Research Centre, University of Edinburgh, 1997.

Part VI. Alternative Approaches to Scoring Systems

16. Survival analysis and the credit-granting decision

235

(12)

B. Narain

In Credit Scoring and Credit Control, (eds. Thomas, Crook, and Edelman), pp. 109-22. OUP, Oxford 2002.

17. Graphical models in credit scoring

247

(30)

Pete Sewart and Joe Whittaker

IMA Journal of Mathematics Applied in Business and Industry, 9, 241-66, 1998.

18. Credit scoring using neural and evolutionary techniques

277

(18)

Mumine B. Yobas, Jonathan N. Crook, and Peter Ross

IMA Journal of Mathematics Applied in Business and Industry, 11, 111-25, 2000.

19. Segmentation in Markov chain consumer credit behavioural models

295

(14)

Joseph Ho, Lyn C. Thomas, T.A. Pomroy, and William T. Scherer

Proceedings of Credit Scoring and Credit Control VII, Credit Research Centre, University of Edinburgh, 2001.

Index

309

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Readings in Credit Scoring Foundations, Developments, and Aims

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