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.
Weiwei JIANG
Beijing University of Posts and Telecommunications
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Weiwei JIANG, "Loan Default Prediction with Deep Learning and Muddling Label Regularization" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 7, pp. 1340-1342, July 2022, doi: 10.1587/transinf.2022EDL8003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8003/_p
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@ARTICLE{e105-d_7_1340,
author={Weiwei JIANG, },
journal={IEICE TRANSACTIONS on Information},
title={Loan Default Prediction with Deep Learning and Muddling Label Regularization},
year={2022},
volume={E105-D},
number={7},
pages={1340-1342},
abstract={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.},
keywords={},
doi={10.1587/transinf.2022EDL8003},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Loan Default Prediction with Deep Learning and Muddling Label Regularization
T2 - IEICE TRANSACTIONS on Information
SP - 1340
EP - 1342
AU - Weiwei JIANG
PY - 2022
DO - 10.1587/transinf.2022EDL8003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2022
AB - 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.
ER -