Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control

• URL: http://arxiv.org/abs/2602.00120v1
• Date: Tue, 27 Jan 2026 15:25:04 GMT
• Title: Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
• Authors: Xianghong Hu, Tianning Xu, Ying Chen, Shuai Wang,
• Abstract summary: We compare multiple machine learning approaches for mortgage default prediction using a real-world loan-level dataset.<n>We employ leakage-aware feature selection, a strict temporal split that constrains both origination and reporting periods.<n>Across multiple positive-to-negative ratios, performance remains stable, and an AutoML approach achieves the strongest AUROC among the models evaluated.
• Score: 7.5947844798897535
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mortgage default prediction is a core task in financial risk management, and machine learning models are increasingly used to estimate default probabilities and provide interpretable signals for downstream decisions. In real-world mortgage datasets, however, three factors frequently undermine evaluation validity and deployment reliability: ambiguity in default labeling, severe class imbalance, and information leakage arising from temporal structure and post-event variables. We compare multiple machine learning approaches for mortgage default prediction using a real-world loan-level dataset, with emphasis on leakage control and imbalance handling. We employ leakage-aware feature selection, a strict temporal split that constrains both origination and reporting periods, and controlled downsampling of the majority class. Across multiple positive-to-negative ratios, performance remains stable, and an AutoML approach (AutoGluon) achieves the strongest AUROC among the models evaluated. An extended and pedagogical version of this work will appear as a book chapter.

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