{"id":1646,"date":"2026-03-29T19:16:46","date_gmt":"2026-03-29T11:16:46","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=1646"},"modified":"2026-05-23T19:33:21","modified_gmt":"2026-05-23T11:33:21","slug":"trustworthy-constraint-deep-graph-learning-for-enterprise-financial-risk-prediction","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=trustworthy-constraint-deep-graph-learning-for-enterprise-financial-risk-prediction","title":{"rendered":"Trustworthy-constraint Deep Graph Learning for Enterprise Financial Risk Prediction"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 29, Issue 1<\/a><\/p>\n<\/div>\n\n\n\n

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Wenting Ma<\/i><\/a><\/p>\n\n\n\n

School of Finance and Economic, Zhengzhou University of Science and Technology, Zhengzhou, Henan, China 450064<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0March 14, 2025
Accepted:\u00a0April 5, 2025
Publication Date:\u00a0March 29, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Comparison of TDGL-net to top-performing methods on SMP in terms of four metrics.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202601_29(1).0011<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords:\u00a0Financial risk prediction; Heterogeneous graph information aggregation; Trustworthy decision-making mechanism<\/em><\/p>\n\n\n\n

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