Artificial Intelligence: the Application of Machine Learning and Predictive Analytics in Credit Risk

Abstract

In the last years Machine Learning (and the Artificial Intelligence), is experiencing a new rush thanks to the growth of volume and kind of data, the presence of tools / software with higher computational power and cheaper data storage size (e.g. cloud). In Credit Risk Management, the PD (Probability of Default) estimation has attracted lots of research interests in the past literature and recent studies have shown that advanced Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods tied to simplified Machine Learning techniques. The study empirically investigates the results of applying different advanced machine learning techniques in estimation and calibration of Probability of Default. The study has been done on big data sample with more than 800,000 Retail customers of a panel European Banks under ECB Supervision, with 10 years of historical information (2006 - 2016) and 300 variables to be analyzed for each customer. The study shows that neural network produces a higher population riskiness ranking accuracy, with 71% of Accuracy Ratio. However, the authors’ idea is that classification tree is more interpretable from an economic and credit point of view. In terms of model calibration, cluster analysis produces rating classes more stable and with a predicted risk probability aligned with the observed default rate.