aiqbench analysis
This research presents a deep learning framework that fuses heterogeneous data sources, including transaction behaviors and social networks, to improve the accuracy and timeliness of credit risk warnings. By integrating attention mechanisms with neural networks, the system effectively overcomes information silos inherent in traditional rule-based engines. The findings offer practical value for financial institutions seeking more robust early intervention mechanisms against corporate and individual credit risks.
Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
arXiv:2609.15744v1 Announce Type: new Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability.
Authors:LiYang Wang (1), Zhen Zhong (2), Zhen Tian (3), Keyu Chen (4)Keyu Chen ((1) Washington University in St. Louis, (2) Georgetown University, (3) University of Glasgow, (4) Wuyi University)
Abstract:Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability.
6 pages, 6 figures. Published in Proceedings of the 3rd International Conference on Machine Intelligence and Digital Applications (MIDA 2026), Xi'an, China, April 24-26, 2026. ACM. this https URL
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Databases (cs.DB); Machine Learning (cs.LG); Risk Management (q-fin.RM)
I.2.6; H.2.8; J.1; K.6.5
arXiv:2609.15744 [cs.AI]
(or arXiv:2609.15744v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.15744 arXiv-issued DOI via DataCite (pending registration)
Proc. 3rd International Conference on Machine Intelligence and Digital Applications (MIDA 2026), Xi'an, China, pp. 1367-1372, ACM (2026)
https://doi.org/10.1145/3801438.3804860 DOI(s) linking to related resources
Full text under the publisher's open licence · 243 words
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