  The retail banking services are one of the pillars of the modern economic
growth. However, the evolution of the client's habits in modern societies and
the recent European regulations promoting more competition mean the retail
banks will encounter serious challenges for the next few years, endangering
their activities. They now face an impossible compromise: maximizing the
satisfaction of their hyper-connected clients while avoiding any risk of
default and being regulatory compliant. Therefore, advanced and novel research
concepts are a serious game-changer to gain a competitive advantage. In this
context, we investigate in this thesis different concepts bridging the gap
between persistent homology, neural networks, recommender engines and
reinforcement learning with the aim of improving the quality of the retail
banking services. Our contribution is threefold. First, we highlight how to
overcome insufficient financial data by generating artificial data using
generative models and persistent homology. Then, we present how to perform
accurate financial recommendations in multi-dimensions. Finally, we underline a
reinforcement learning model-free approach to determine the optimal policy of
money management based on the aggregated financial transactions of the clients.
Our experimental data sets, extracted from well-known institutions where the
privacy and the confidentiality of the clients were not put at risk, support
our contributions. In this work, we provide the motivations of our retail
banking research project, describe the theory employed to improve the financial
services quality and evaluate quantitatively and qualitatively our
methodologies for each of the proposed research scenarios.
