Regulatory Learning: Credit Scoring Application of Machine Learning

Dominique Guegan () and Bertrand Hassani ()
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Dominique Guegan: UP1 - Université Paris 1 Panthéon-Sorbonne, 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, University of Ca’ Foscari [Venice, Italy]
Bertrand Hassani: UP1 - Université Paris 1 Panthéon-Sorbonne, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Keywords: Regulation; Credit Risk; Big Data; Machine Learning (search for similar items in EconPapers)
Date: 2017-07-27
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Published in DMBD 2017, Jul 2017, Fukuoka, Japan

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-01905489

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