Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
Published 15 Sept 2026arXiv:2609.15744
Updated 29 h ago · first seen 15 Sept 2026
paper_01M2JK0CXS208FYNSADQ645C1Y
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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- Property changedPaperDesign of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv - New paperPaperDesign of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
New paper: Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
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