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Regulatory Learning: how to supervise machine learning models? An application to credit scoring

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  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Bertrand Hassani

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne)

Abstract

The arrival of big data strategies is threatening the lastest trends in financial regulation related to the simplification of models and the enhancement of the comparability of approaches chosen by financial institutions. Indeed, the intrinsic dynamic philosophy of Big Data strategies is almost incompatible with the current legal and regulatory framework as illustrated in this paper. Besides, as presented in our application to credit scoring, the model selection may also evolve dynamically forcing both practitioners and regulators to develop libraries of models, strategies allowing to switch from one to the other as well as supervising approaches allowing financial institutions to innovate in a risk mitigated environment. The purpose of this paper is therefore to analyse the issues related to the Big Data environment and in particular to machine learning models highlighting the issues present in the current framework confronting the data flows, the model selection process and the necessity to generate appropriate outcomes.

Suggested Citation

  • Dominique Guegan & Bertrand Hassani, 2017. "Regulatory Learning: how to supervise machine learning models? An application to credit scoring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01592168, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01592168
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01592168v2
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    References listed on IDEAS

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    1. Bertrand K. Hassani, 2016. "Scenario Analysis in Risk Management," Springer Books, Springer, number 978-3-319-25056-4, December.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Keywords

    machine learning; AUC; Big data; Credit scoring; regulation;
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