Model interpretability in credit insurance

The use of complex machine learning (ML) models has become increasingly popular in the credit insurance field due to their ability to accurately predict the probability of default. However, the lack of interpretability of these models has become a critical issue for businesses and regulatory bodies. This study focuses on the use of Explainable Boosting Machines (EBM) to develop an interpretable model for predicting the probability of default for buyers in credit insurance policies. The empirical analysis uses a dataset of credit insurance policies and compares the performance of the EBM model with state-of-the-art models in terms of accuracy and interpretability. The results show that the EBM model achieves high accuracy levels, comparable to the best-performing models, while maintaining a high degree of interpretability. The findings suggest that EBM can be a valuable tool for credit insurance companies to balance the need for accurate predictions with the need for transparency and accountability, in line with new policy restrictions.