Journal of the Operational Research Society

, Volume 56, Issue 9, pp 1109–1117

Good practice in retail credit scorecard assessment

Special Issue Paper

DOI: 10.1057/palgrave.jors.2601932

Cite this article as:
Hand, D. J Oper Res Soc (2005) 56: 1109. doi:10.1057/palgrave.jors.2601932

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.

Keywords

credit scoring scorecard bad rate banking management methodology decision making 

Copyright information

© Palgrave Macmillan Ltd 2005

Authors and Affiliations

  1. 1.Imperial CollegeLondonUK