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

Author

Listed:
  • LiYang Wang

    (Washington University in St. Louis)

  • Zhen Zhong

    (Georgetown University)

  • Zhen Tian

    (University of Glasgow)

  • Keyu Chen

    (Wuyi University)

  • Keyu Chen

    (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.

Suggested Citation

  • LiYang Wang & Zhen Zhong & Zhen Tian & Keyu Chen & Keyu Chen, 2026. "Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data," Papers 2609.15744, arXiv.org.
    • Handle: RePEc:arx:papers:2609.15744
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    File URL: https://arxiv.org/pdf/2609.15744
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