Good practice in retail credit scorecard assessment

Author: Hand, D J

Source: Journal of the Operational Research Society, Volume 56, Number 9, September 2005 , pp. 1109-1117(9)

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Abstract:

In retail banking, predictive statistical models called ‘scorecards’ are used to assign customers to classes, and hence to appropriate actions or interventions. Such assignments are made on the basis of whether a customer's predicted score is above or below a given threshold. The predictive power of such scorecards gradually deteriorates over time, so that performance needs to be monitored. Common performance measures used in the retail banking sector include the Gini coefficient, the Kolmogorov–Smirnov statistic, the mean difference, and the information value. However, all of these measures use irrelevant information about the magnitude of scores, and fail to use crucial information relating to numbers misclassified. The result is that such measures can sometimes be seriously misleading, resulting in poor quality decisions being made, and mistaken actions being taken. The weaknesses of these measures are illustrated. Performance measures not subject to these risks are defined, and simple numerical illustrations are given.Journal of the Operational Research Society (2005) 56, 1109–1117. doi:10.1057/palgrave.jors.2601932 Published online 2 February 2005

Document Type: Research article

DOI: http://dx.doi.org/10.1057/palgrave.jors.2601932

Affiliations: 1: 1Imperial College, London, UK

Publication date: 2005-09-01