Wenting MaThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Finance and Economic, Zhengzhou University of Science and Technology, Zhengzhou, Henan, China 450064
Received: March 14, 2025 Accepted: April 5, 2025 Publication Date: May 1, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
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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