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Performance of credit risk prediction models via proper loss functions

Author

Listed:
  • Silvia Figini

    (Department of Political and Social Sciences, University of Pavia)

  • Mario Maggi

    (Department of Economics and Management, University of Pavia)

Abstract
The performance of predictions models can be assessed using a variety of methods and metrics. Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the AUC (Area Under the ROC curve), such as the H index. It is widely recognized that AUC suffers from lack of coherency especially when ROC curves cross. On the other hand, the H index requires subjective choices. In our opinion the problem of model comparison should be more adequately handled using a different approach. The main contribution of this paper is to evaluate the performance of prediction models using proper loss function. In order to compare how our approach works with respect to classical measures employed in model comparison, we propose a simulation studies, as well as a real application on credit risk data.

Suggested Citation

  • Silvia Figini & Mario Maggi, 2014. "Performance of credit risk prediction models via proper loss functions," DEM Working Papers Series 064, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0064
    as

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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0064.pdf
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    References listed on IDEAS

    as
    1. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
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    More about this item

    Keywords

    Model Comparison; AUC; H index; Loss Function; Proper Scoring Rules; Credit Risk;
    All these keywords.

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