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Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Niall M. Adams

    (Imperial College London, Department of Mathematics)

  • Dimitris K. Tasoulis

    (Imperial College London, Department of Mathematics)

  • Christoforos Anagnostopoulos

    (Imperial College London, The Institute for Mathematical Sciences)

  • David J. Hand

    (Imperial College London, Department of Mathematics
    Imperial College London, The Institute for Mathematical Sciences)

Abstract

Classification methods have proven effective for predicting the creditworthiness of credit applications. However, the tendency of the underlying populations to change over time, population drift, is a fundamental problem for such classifiers. The problem manifests as decreasing performance as the classifier ages and is typically handled by periodic classifier reconstruction. To maintain performance between rebuilds, we propose an adaptive and incremental linear classification rule that is updated on the arrival of new labeled data. We consider adapting this method to suit credit application classification and demonstrate, with real loan data, that the method outperforms static and periodically rebuilt linear classifiers.

Suggested Citation

  • Niall M. Adams & Dimitris K. Tasoulis & Christoforos Anagnostopoulos & David J. Hand, 2010. "Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 167-176, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_15
    DOI: 10.1007/978-3-7908-2604-3_15
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