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Adaptive consumer credit classification

Author

Listed:
  • N G Pavlidis

    (Lancaster University, Lancaster, UK)

  • D K Tasoulis

    (Winton Capital Management, London, UK)

  • N M Adams

    (Imperial College of Science, Technology and Medicine, London, UK)

  • D J Hand

    (1] Winton Capital Management, London, UK[2] Imperial College of Science, Technology and Medicine, London, UK)

Abstract

Credit scoring methods for predicting creditworthiness have proven very effective in consumer finance. In light of the present financial crisis, such methods will become even more important. One of the outstanding issues in credit risk classification is population drift. This term refers to changes occurring in the population due to unexpected changes in economic conditions and other factors. In this paper, we propose a novel methodology for the classification of credit applications that has the potential to adapt to population drift as it occurs. This provides the opportunity to update the credit risk classifier as new labelled data arrives. Assorted experimental results suggest that the proposed method has the potential to yield significant performance improvement over standard approaches, without sacrificing the classifier's descriptive capabilities.

Suggested Citation

  • N G Pavlidis & D K Tasoulis & N M Adams & D J Hand, 2012. "Adaptive consumer credit classification," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(12), pages 1645-1654, December.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:12:p:1645-1654
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    Citations

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    Cited by:

    1. Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2021. "A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection," Papers 2107.08808, arXiv.org.
    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. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.

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