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IMA Journal of Management Mathematics 2001 12(2):139-155; doi:10.1093/imaman/12.2.139
© 2001 by Institute of Mathematics and its Applications
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Modelling consumer credit risk

David J. Hand1

1 Department of Mathematics, Imperial College, 180 Queen's Gate, London SW7 2BZ, UK

The consumer credit market is experiencing unprecedented change, increased competition, and new challenges. To cope with these developments, increasingly sophisticated mathematical and statistical tools are being used. Such tools are used to identify good and bad risks, to monitor customer performance, to characterize different behaviour patterns, and in a wide variety of other ways, at both individual and portfolio level. Examples of such applications and of the modern statistical tools developed to model them are given, including statistical staples such as logistic regression and naive Bayes, but also including more recent developments such as neural networks and recursive partitioning models. A key aspect is the development of methods for assessing performance of the models, and this is examined in detail.

Keywords: credit scoring; creditworthiness; classification

Received 1 February 2001. Accepted 15 August 2001.


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