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

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.
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https://halshs.archives-ouvertes.fr/halshs-01592168
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  • HAL Id : halshs-01592168, version 2

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Dominique Guegan, Bertrand Hassani. Regulatory Learning: how to supervise machine learning models? An application to credit scoring. 2017. ⟨halshs-01592168v2⟩

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