Explainable Machine Learning for Fallout Prediction in the Mortgage Pipeline

This study examines mortgage loan fallout using data provided by a leading financial institution. By accurately predicting mortgage loan fallout, lenders can protect their bottom line, maintain financial stability, and contribute to a healthier economy. The paper employs various machine learning models to predict mortgage fallout based on loan, market, property, and borrower characteristics. A large dataset of locked mortgage applications from a major U.S. lender was analyzed. The random forest model demonstrated superior predictive efficiency and stability. To understand the factors influencing mortgage fallout, the SHAP method, along with empirical analysis with logistic regression, was utilized to identify key determinants. The paper discusses the implications of these findings for mortgage lenders and future research.