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Profit-Oriented Feature Selection in Credit Scoring Applications

In: Operations Research Proceedings 2018

Author

Listed:
  • Nikita Kozodoi

    (Humboldt University of Berlin
    Kreditech)

  • Stefan Lessmann

    (Humboldt University of Berlin)

  • Bart Baesens

    (Catholic University of Leuven)

  • Konstantinos Papakonstantinou

    (Kreditech)

Abstract

In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard feature selection techniques are based on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators for model evaluation may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a wrapper-based framework that uses the Expected Maximum Profit measure (EMP) as a fitness function. Experiments on multiple credit scoring data sets provide evidence that EMP-maximizing feature selection helps to develop scorecards that yield a higher expected profit compared to conventional feature selection strategies.

Suggested Citation

  • Nikita Kozodoi & Stefan Lessmann & Bart Baesens & Konstantinos Papakonstantinou, 2019. "Profit-Oriented Feature Selection in Credit Scoring Applications," Operations Research Proceedings, in: Bernard Fortz & Martine LabbĂ© (ed.), Operations Research Proceedings 2018, pages 59-65, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-18500-8_9
    DOI: 10.1007/978-3-030-18500-8_9
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