Loan Default Prediction with Deep Learning and Muddling Label Regularization

Loan default prediction has been a significant problem in the financial domain because overdue loans may incur significant losses. Machine learning methods have been introduced to solve this problem, but there are still many challenges including feature multicollinearity, imbalanced labels, and smal...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E105.D; no. 7; pp. 1340 - 1342
Main Author JIANG, Weiwei
Format Journal Article
LanguageEnglish
Published Tokyo The Institute of Electronics, Information and Communication Engineers 01.07.2022
Japan Science and Technology Agency
Subjects
Deep learning
loan default prediction
Loans
Machine learning
muddling label regularization
Neural networks
Regularization
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Summary:Loan default prediction has been a significant problem in the financial domain because overdue loans may incur significant losses. Machine learning methods have been introduced to solve this problem, but there are still many challenges including feature multicollinearity, imbalanced labels, and small data sample problems. To replicate the success of deep learning in many areas, an effective regularization technique named muddling label regularization is introduced in this letter, and an ensemble of feed-forward neural networks is proposed, which outperforms machine learning and deep learning baselines in a real-world dataset.
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ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2022EDL8003