A novel approach for Credit Scoring using Deep Neural Networks with bank transaction data

With the PSD2 open banking revolution FinTechs obtained a key role in the financial industry. This role implies the inquiry and development of new techniques, products and solutions to compete with other players in this area. The aim of this thesis is to investigate the applicability of the state-of-the-art Deep Learning techniques for Credit Risk Modeling. In order to accomplish it, a PSD2-related synthetic and anonymized dataset has been used to simulate an application process with only one account per user. Firstly, a machine-readable representation of the bank accounts has been created, starting from the raw transactions’ data and scaling the variables using the quantile function. Afterwards, a Deep Neural Network has been created in order to capture the complex relations between the input variables and to extract information from the accounts’ representations. The proposed architecture accomplished the assigned tasks with a Gini index of 0.55, exploiting a Convolutional encoder to extract features from the inputs and a Recurrent decoder to analyze them.