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The retail banking services are one of the pillars of the modern economicgrowth. However, the evolution of the client's habits in modern societies andthe recent European regulations promoting more competition mean the retailbanks will encounter serious challenges for the next few years, endangeringtheir activities. They now face an impossible compromise: maximizing thesatisfaction of their hyper-connected clients while avoiding any risk ofdefault and being regulatory compliant. Therefore, advanced and novel researchconcepts are a serious game-changer to gain a competitive advantage. In thiscontext, we investigate in this thesis different concepts bridging the gapbetween persistent homology, neural networks, recommender engines andreinforcement learning with the aim of improving the quality of the retailbanking services. Our contribution is threefold. First, we highlight how toovercome insufficient financial data by generating artificial data usinggenerative models and persistent homology. Then, we present how to performaccurate financial recommendations in multi-dimensions. Finally, we underline areinforcement learning model-free approach to determine the optimal policy ofmoney management based on the aggregated financial transactions of the clients.Our experimental data sets, extracted from well-known institutions where theprivacy and the confidentiality of the clients were not put at risk, supportour contributions. In this work, we provide the motivations of our retailbanking research project, describe the theory employed to improve the financialservices quality and evaluate quantitatively and qualitatively ourmethodologies for each of the proposed research scenarios.

From Persistent Homology to Reinforcement Learning with Applications for Retail Banking