Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

arXiv:2609.15744 · cs.AI, cs.CE, cs.DB, cs.LG, q-fin.RM · Submitted 2026-09-14 · Read on arXiv

cs.AI, cs.CE, cs.DB, cs.LG, q-fin.RM

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 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. https://doi.org/10.1145/3801438.3804860

Journal ref: Proc. 3rd International Conference on Machine Intelligence and Digital Applications (MIDA 2026), Xi'an, China, pp. 1367-1372, ACM (2026)

DOI: 10.1145/3801438.3804860

License: http://creativecommons.org/licenses/by/4.0/

The gist: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges.

Terminology

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.

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