Trustworthy-constraint Deep Graph Learning for Enterprise Financial Risk Prediction
Financial risk prediction for enterprises is a hot topic in the field of financial technology. Deep learning-Based methods achieve encouraging the financial risk prediction performance due to the power ability of the feature learning. However, there exists two issues in deep learning-Based methods....
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Tamkang University Press
2025-06-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202601-29-01-0011 |
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| Summary: | Financial risk prediction for enterprises is a hot topic in the field of financial technology. Deep learning-Based methods achieve encouraging the financial risk prediction performance due to the power ability of the feature learning. However, there exists two issues in deep learning-Based methods. (1) Current methods fail to accurately model the complex relationships among listed companies in the real financial market. (2) Current methods lack credible estimates in the decision-making process, which leads to the questionable reliability of the decision-making results. To this end, a trustworthy-constraint deep graph learning network (TDGL-net) is proposed to achieve the above goal, which includes the multi-view feature encoding, the heterogeneous graph information aggregation, the trustworthy decision-making mechanism. Specifically, TDGL-net integrates a multi-dimensional bilinear neural tensor and Transformer into a unified multi-view feature encoding to learn comprehensive representation. Then, TDGL-net models heterogeneous graph information aggregation via the cross-category association and the intra-category association, to capture complex inter-enterprise relationships with momentum spillover effects for enhancing the representation discrimination. Additionally,
TDGL-net incorporates a trustworthy decision-making mechanism to adaptively integrate information from deep embedding representations and graph embedding representations according to enterprise-specific contexts,
improving decision reliability and accuracy. Ultimately, extensive experimental evaluations on the real-world dataset reveal that TDGL-net delivers state-of-the-art performance in predicting enterprise financial risk. |
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| ISSN: | 2708-9967 2708-9975 |