Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910006582968320 |
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| author | Hu, Xianghong Xu, Tianning Chen, Ying Wang, Shuai |
| author_facet | Hu, Xianghong Xu, Tianning Chen, Ying Wang, Shuai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00120 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control Hu, Xianghong Xu, Tianning Chen, Ying Wang, Shuai Machine Learning 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. |
| title | Predicting Mortgage Default with Machine Learning: AutoML, Class Imbalance, and Leakage Control |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.00120 |