用图神经网络分析供应链数据，精准预测中小企业贷款违约风险。
Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain
- 构建供应链图谱，融合交易与社交数据捕捉企业关联
- 在2340万节点数据上实现0.995的供应链分析AUC，0.701的违约预测AUC
- 适合金融监管、银行风控及央行压力测试使用
中小企业是现代经济的重要支柱，但其信用风险评估常因数据稀缺而困难，尤其对缺乏直接信贷记录的在线贷款机构。本文提出一种基于图神经网络（GNN）的框架，利用企业交易与社交数据中的互动关系，刻画空间依赖性并预测贷款违约风险。在来自Discover和蚂蚁征信的真实数据集上测试，供应链分析涉及2340万节点，违约预测涵盖860万节点。结果表明，该GNN模型显著优于传统方法及其他GNN基线，在供应链挖掘任务中达到0.995的AUC，违约预测任务中达0.701。该方法还能帮助监管机构模拟供应链中断对银行的影响，准确预测由原材料短缺引发的贷款违约，为美联储压力测试提供关键数据以设定CCAR风险缓冲。该方法为中小企业信用风险评估提供了可扩展、高效的解决方案。
原文摘要 · Abstract (English)
Small and Medium-sized Enterprises (SMEs) are vital to the modern economy, yet their credit risk analysis often struggles with scarce data, especially for online lenders lacking direct credit records. This paper introduces a Graph Neural Network (GNN)-based framework, leveraging SME interactions from transaction and social data to map spatial dependencies and predict loan default risks. Tests on real-world datasets from Discover and Ant Credit (23.4M nodes for supply chain analysis, 8.6M for default prediction) show the GNN surpasses traditional and other GNN baselines, with AUCs of 0.995 and 0.701 for supply chain mining and default prediction, respectively. It also helps regulators model supply chain disruption impacts on banks, accurately forecasting loan defaults from material shortages, and offers Federal Reserve stress testers key data for CCAR risk buffers. This approach provides a scalable, effective tool for assessing SME credit risk.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。