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

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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 identificatio

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