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

Dominique Guegan () and Bertrand Hassani ()
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Dominique Guegan: Centre d'Economie de la Sorbonne and LabEx ReFi, https://cv.archives-ouvertes.fr/dominique-guegan
Bertrand Hassani: Group Capgemini and Centre d'Economie de la Sorbonne and LabEx ReFi

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la 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

Keywords: Big Data; Credit scoring; machine learning; AUC; regulation (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2017-07, Revised 2017-09
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-pay and nep-rmg
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Published in Journal of Finance and Data Science

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ftp://mse.univ-paris1.fr/pub/mse/CES2017/17034R.pdf (application/pdf)
https://www.sciencedirect.com/science/article/pii/S2405918817300648

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:17034r

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