Bertrand Hassani
University College London, UK
Title: Regulatory learning; what framework can be proposed to ensure that the financial environment can be controlled?
Biography
Biography: Bertrand Hassani
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 comparability of approaches chosen by the various entities. Indeed, the intrinsic dynamic philosophy of big data strategies is almost incompatible with the current legal and regulatory framework in particular the one related to model risk governance. 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 and 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 and to propose solutions to regulators, analyzing the features of each and every algorithm implemented, for instance a logistic regression, a support vector machine, a neural network, a random forest and gradient boosting.
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