DefaultGNN：基于买卖双方交易网络预测企业违约的双视角图神经网络框架
DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks
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中文总结 AI 辅助
针对企业违约预测中财务报表数据稀疏的问题，提出双视角图神经网络框架DefaultGNN，整合买卖双方交易网络建模风险传播，显著提升预测性能，并提高信用审批率7-11个百分点。
中文摘要 AI 辅助
企业违约预测是金融风险管理中的核心问题，然而传统信用模型严重依赖财务报表，而许多企业的财务报表往往稀疏或不可得。企业交易网络提供了对真实经济活动的补充视角，但风险如何通过买卖双方关系传播仍未得到充分探索。我们利用覆盖六年的真实电子税务发票数据开展了一项大规模实证研究，将交易历史与违约事件相关联，揭示出交易驱动的风险既具有角色依赖性（买方或卖方），又具有规模依赖性。基于这些发现，我们构建了多层的买方视角和卖方视角交易网络，并提出了DefaultGNN——一种基于双视角图神经网络的企业违约预测框架。DefaultGNN整合了两种视角，以建模风险如何通过交易关系流动，在基于属性和基于图的基线方法上均取得了显著改进，尤其是对于内在风险信号有限的企业。我们进一步通过可视化陷入困境的交易伙伴如何导致违约风险，提供了可解释的基于网络的解释。与一家持牌信用评级机构合作，我们验证了DefaultGNN的预测能够补充现有信用评分模型，在不增加已批准企业违约风险的情况下，将批准率提高了7-11个百分点。源代码可在以下网址获取：此https URL。
英文摘要
Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that are often sparse or unavailable for many firms. Corporate transaction networks offer a complementary view of real economic activity, but how risk propagates through buyer-seller relationships remains underexplored. We conduct a large-scale empirical study using real-world electronic tax-invoice data spanning six years that links transaction histories with default events, revealing that transaction-driven risk is both role-dependent (buyer or seller) and scale-dependent. Based on these findings, we construct multiplex buyer-view and seller-view transaction networks and propose DefaultGNN, a dual-perspective graph neural network-based framework for corporate default prediction. DefaultGNN integrates both views to model how risk flows through transactional relationships, achieving strong improvements over both attribute-based and graph-based baselines, especially for firms with limited intrinsic risk signals. We further provide interpretable network-based explanations by visualizing how distressed trading partners contribute to default risk. In collaboration with a licensed credit rating agency, we validate that DefaultGNN's predictions complement existing credit scoring models, improving approval rates by 7-11%p without increasing default risk among approved firms. The source code can be found at https://github.com/jhkim611/DefaultGNN
发表机构
- KAIST（韩国科学技术院）
- Techfin Ratings
- Douzone
机构由 AI 辅助整理，请以论文原文为准。
