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Approaches for credit scorecard calibration: An empirical analysis

Author

Listed:
  • Artem Bequé
  • Kristof Coussement

    (IESEG - School of Management (LEM), LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Ross Gayler
  • Stefan Lessmann

Abstract

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Suggested Citation

  • Artem Bequé & Kristof Coussement & Ross Gayler & Stefan Lessmann, 2017. "Approaches for credit scorecard calibration: An empirical analysis," Post-Print hal-01745262, HAL.
    • Handle: RePEc:hal:journl:hal-01745262
    DOI: 10.1016/j.knosys.2017.07.034
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    Citations

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    Cited by:

    1. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
    2. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    3. Matthieu Garcin & Samuel St'ephan, 2021. "Credit scoring using neural networks and SURE posterior probability calibration," Papers 2107.07206, arXiv.org.
    4. Matthieu Garcin & Samuel Stéphan, 2023. "Credit scoring using neural networks and SURE posterior probability calibration," Working Papers hal-03286760, HAL.
    5. Manta Eduard Mihai & Bogoevici Flavia, 2023. "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, vol. 12(2), pages 88-108, December.
    6. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102, arXiv.org.

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