Predicting credit risk in SCF: A novel framework with explainable GraphSAGE based on network integration

Jing Li, Chi Xie, Gang-Jin Wang and Matteo Foglia

International Review of Financial Analysis, 2025, vol. 104, issue PA

Abstract: We integrate three enterprise networks, i.e., the stock return network, risk spillover network, and market transaction network to predict the credit risk in supply chain finance (SCF) by applying the explainable GraphSAGE model. We construct the aforementioned networks to comprehensively illustrate the relationships among enterprises, train the GraphSAGE model to classify the nodes in the graph structure, and use GNNExplainer to analyze the explainability of model's predictions. We find that (i) GraphSAGE significantly outperforms the baseline models and achieves the highest scores in terms of all performance metrics in predicting credit risk; (ii) GNNExplainer is able to identify the financial indicators (reflecting the profitability, liquidity and leverage of enterprises) that have significant impacts on the predictions; and (iii) the influential neighbors of risky enterprises tend to be risky themselves, while those of the non-risky enterprises are often non-risky, thus demonstrating a credit risk alignment among enterprise relationships. Our findings offer market participants valuable insights into enhancing credit risk prediction by utilizing advanced graph-based models, identifying the critical financial indicators, and assessing credit risk based on enterprise networks.

Keywords: Supply chain finance; Credit risk prediction; Network integration; GraphSAGE; GNNExplainer (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:104:y:2025:i:pa:s1057521925003928

DOI: 10.1016/j.irfa.2025.104305

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