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Selection bias in credit scorecard evaluation

Author

Listed:
  • David J Hand

    (Department of Mathematics, Imperial College London, London, UK)

  • Niall M Adams

    (1] Department of Mathematics, Imperial College London, London, UK[2] Heilbronn Institute for Mathematical Research, University of Bristol, Bristol, UK)

Abstract

Selection bias is a perennial problem when constructing and evaluating scorecards. It is familiar in the context of reject inference, but crops up in many other situations as well. In this paper, we examine the impact of how accepting or rejecting customers using one scorecard leads to biased comparisons of performance between that scorecard and others. This has important implications for organisations seeking to improve or replace scorecards.

Suggested Citation

  • David J Hand & Niall M Adams, 2014. "Selection bias in credit scorecard evaluation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 408-415, March.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:3:p:408-415
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    Cited by:

    1. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
    2. Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, March.
    3. Jonathan Crook & David Edelman, 2014. "Special issue credit risk modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 321-322, March.
    4. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.

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