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

Author

Listed:
  • Dominique Guegan

    (University of Ca’ Foscari [Venice, Italy], CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, IPAG Business School)

  • Bertrand Hassani

    (Capgemini Consulting [Paris], UCL-CS - Department of Computer science [University College of London] - UCL - University College of London [London])

Abstract

The arrival of Big Data strategies is threatening the latest 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, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Post-Print halshs-01835213, HAL.
    • Handle: RePEc:hal:journl:halshs-01835213
    DOI: 10.1016/j.jfds.2018.04.001
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    Citations

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    Cited by:

    1. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
    2. Charlene H. Chu & Simon Donato-Woodger & Shehroz S. Khan & Rune Nyrup & Kathleen Leslie & Alexandra Lyn & Tianyu Shi & Andria Bianchi & Samira Abbasgholizadeh Rahimi & Amanda Grenier, 2023. "Age-related bias and artificial intelligence: a scoping review," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    3. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    4. Guner Altan & Server Demirci, 2022. "Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 397-424, July.
    5. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.

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