Scoring bank loans that may go wrong: a case study

Jan Cramer

Statistica Neerlandica, 2004, vol. 58, issue 3, 365-380

Abstract: A bank employs logistic regression with state‐dependent sample selection to identify loans that may go wrong. The data consist of some 20 000 loans for which a number of conventional accounting ratios of the debtor firm are known; after two years just over 600 have gone wrong. Inspection shows that the state‐dependent sampling technique does not work because the data do not satisfy the standard logit model. Several variants on this model are considered, and it is found that a bounded logit with a ceiling of (far) less than 1 fits the data better. When it comes to their performance in an independent data‐set, however, the differences between the various methods of analysis are negligible.

Date: 2004
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