Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908456650276864 |
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| author | Zhang, Zizhou Shen, Qinyan Hu, Zhuohuan Liu, Qianying Shen, Huijie |
| author_facet | Zhang, Zizhou Shen, Qinyan Hu, Zhuohuan Liu, Qianying Shen, Huijie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain Zhang, Zizhou Shen, Qinyan Hu, Zhuohuan Liu, Qianying Shen, Huijie Machine Learning 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. |
| title | Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.07854 |