Transition numérique de la gestion des risques : enjeux, outils et perspectives

Digital, environmental and social transitions upset financial institutions financial risks in a Schumpeterian process. These upheavals involve exogenous factors, stemming from institutions? counterparts, from systemic sources, responses from national and supranational public authorities to the effects of transitions, and endogenous, linked to competitors less hampered by risk inheritance and the systemic importance of their decisions. These transformations call into question current risk management methods fundamental principles, and their reliance on stationarity of economic cycles. They advocate for the creation of new tools, approaches, and algorithms, including the introduction of new methodologies to collect, structure, analyze and predict from large amounts of data. The results of generalist?s tests applied to natural language understanding algorithms, computer vision and deep reasoning, are reaching sufficiently satisfactory levels to test their employability in the financial sector. However, this transition must follow a progressive approach factoring the economic stakes of the large financial institutions and their risk inheritance. This publication identifies the key issues for financial risks arising from transitions. It explores three application areas of machine learning and deep learning technologies, to meet the new challenges of financial risks. Finally, it concludes on the research axes to adapt these new technological tools to the specificities of financial institutions to make the transitions successful and inclusive. Classification JEL : G11, C80.