The Anatomy of Mortgage Default Using Shape-Constrained Explainable Machine Learning Model

Abstract

This study leverages novel machine learning techniques to quantify the complex empirical relationship between mortgage default and its drivers. The primary model employed is the authors’ newly developed shape-constrained GAMI-Net, which introduces lattice function-based main effects and pairwise interactions that take user-defined shape constraints. Their approach of adding shape constraints to a lattice module enhances the interpretability and applicability of the model in real-world scenarios. The authors compare the performance of shape-constrained GAMI-Net with alternative machine learning and traditional statistical methods using Freddie Mac’s publicly available mortgage dataset. The results demonstrate competitive predictive performance and high interpretability for the shape-constrained GAMI-Net model.