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Title: Regulatory learning: How to supervise machine learning models with an application to credit scoring Authors:  Bertrand Hassani - Université Paris 1 Pantheon Sorbonne - Labex Refi (France) [presenting]
Dominique Guegan - Universite Paris 1 - Pantheon-Sorbonne (France)
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. 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 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.