  This paper introduces a credit risk rating model for credit risk assessment
in quantitative finance, aiming to categorize borrowers based on their
behavioral data. The model is trained on data from Experian, a widely
recognized credit bureau, to effectively identify instances of loan defaults
among bank customers. Employing state-of-the-art statistical and machine
learning techniques ensures the model's predictive accuracy. Furthermore, we
assess the model's transferability by testing it on behavioral data from the
Bank of Italy, demonstrating its potential applicability across diverse
datasets during prediction. This study highlights the benefits of incorporating
external behavioral data to improve credit risk assessment in financial
institutions.
