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

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Financial risk early warning systems face a structural problem: information silos and monitoring delays limit precision of early signals. This conference paper proposes fusing heterogeneous data sources — transaction behavior and social networks — through a deep neural network with attention mechanisms, creating an integrated early identification system for corporate and individual credit risks.\n\nThe system architecture extracts multidimensional features from diverse data types to provide earlier and more accurate risk warnings than traditional rule-based engines. The paper claims significantly enhanced accuracy and timeliness, but provides no specific quantitative benchmarks — making independent verification of claims difficult.\n\nPublished in MIDA 2026 proceedings (ACM), a conference focused on machine intelligence and digital applications. This is a 6-page short paper with limited evaluation methodology detail.\n\nFor enterprise risk managers, the conceptual approach of heterogeneous data fusion for credit risk is well-established in industry. The paper adds an attention mechanism variant but does not strongly differentiate from existing literature.