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Loan Default Prediction with Deep Learning and Muddling Label Regularization

Weiwei JIANG

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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.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.7 pp.1340-1342
Publication Date
2022/07/01
Publicized
2022/04/04
Online ISSN
1745-1361
DOI
10.1587/transinf.2022EDL8003
Type of Manuscript
LETTER
Category
Artificial Intelligence, Data Mining

Authors

Weiwei JIANG
  Beijing University of Posts and Telecommunications

Keyword