Modelling consumer credit risk

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Abstract
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.

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