发表机构
Washington University in St. Louis; Georgetown University; University of Glasgow; Wuyi University（圣路易斯华盛顿大学; 乔治城大学; 格拉斯哥大学; 武夷学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种融合深度神经网络与注意力机制的信用风险预警系统，整合交易行为与社交网络等多源异构数据，提升风险预警的准确性和及时性，优于传统规则引擎。
AI 中文摘要
数据融合与实时分析技术的进步为解决复杂领域挑战开辟了新途径。金融风险预警系统常因信息孤岛和监测延迟而效率低下。本文提出了一种基于异构信息融合的信用风险预警系统。该系统采用集成深度神经网络与注意力机制的模型架构，从交易行为、社交网络等多源数据中提取多维特征，从而建立针对企业和个人信用风险的早期识别机制。系统测试表明，该方法显著提高了风险预警的准确性和及时性，优于传统的基于规则的引擎解决方案。研究结果为金融风险的早期干预提供了创新思路，对维护金融稳定具有实际意义。
英文摘要
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.
Comments6 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 refProc. 3rd International Conference on Machine Intelligence and Digital Applications (MIDA 2026), Xi'an, China, pp. 1367-1372, ACM (2026)
