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Direct versus indirect credit scoring classifications

Author

Listed:
  • H G Li

    (Alcatel, Greenwich)

  • D J Hand

    (Imperial College of Science, Technology and Medicine)

Abstract

We introduce a new approach to assigning bank account holders to ‘good’ or ‘bad’ classes based on their future behaviour. Traditional methods simply treat the classes as qualitatively distinct, and seek to predict them directly, using statistical techniques such as logistic regression or discriminant analysis based on application data or observations of previous behaviour. We note, however, that the ‘good’ and ‘bad’ classes are defined in terms of variables such as the amount overdrawn at the time at which the classification is required. This permits an alternative, ‘indirect’, form of classification model in which, first, the variables defining the classes are predicted, for example using regression, and then the class membership is derived deterministically from these predicted values. We compare traditional direct methods with these new indirect methods using both real bank data and simulated data. The new methods appear to perform very similarly to the traditional methods, and we discuss why this might be. Finally, we note that the indirect methods also have certain other advantages over the traditional direct methods.

Suggested Citation

  • H G Li & D J Hand, 2002. "Direct versus indirect credit scoring classifications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(6), pages 647-654, June.
  • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:6:d:10.1057_palgrave.jors.2601346
    DOI: 10.1057/palgrave.jors.2601346
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    Citations

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    Cited by:

    1. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    2. Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2022. "A data-driven explainable case-based reasoning approach for financial risk detection," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2257-2274, December.
    3. S M Finlay, 2008. "Towards profitability: a utility approach to the credit scoring problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 921-931, July.
    4. J Whittaker & C Whitehead & M Somers, 2007. "A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 911-921, July.
    5. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    6. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.

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