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Evaluating models for classifying customers in retail banking collections

Author

Listed:
  • D J Hand

    (Imperial College
    Institute for Mathematical Sciences, Imperial College)

  • F Zhou

    (Institute for Mathematical Sciences, Imperial College)

Abstract

When seeking to establish a repayment strategy with delinquent borrowers, it is useful to determine how they are likely to behave, so that an optimal use of resources can be made. We examine two behavioural classifications (‘settle immediately’ versus ‘not settle immediately’, and ‘make some repayment’ versus ‘make no repayment’) and apply a variety of rules for predicting into which class each customer is likely to belong. Since no such rule will yield perfect predictions, the way in which performance is evaluated is crucial in choosing a good rule, and hence subsequently in obtaining accurate predictions of likely future behaviour. We examine some popular standard performance evaluation criteria, showing that they have major weaknesses. We describe and illustrate the use of an alternative measure that overcomes these weaknesses.

Suggested Citation

  • D J Hand & F Zhou, 2010. "Evaluating models for classifying customers in retail banking collections," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1540-1547, October.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:10:d:10.1057_jors.2009.129
    DOI: 10.1057/jors.2009.129
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    References listed on IDEAS

    as
    1. Adrien Jamain & David Hand, 2008. "Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 87-112, June.
    2. Hand David J, 2008. "Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-23, December.
    Full references (including those not matched with items on IDEAS)

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