Guarantee Circle Risk Mining for Industrial Enterprises Based on Graph Neural Reasoning

Guarantee relationships are common in industrial financing, especially among affiliated firms, regional enterprise groups, upstream suppliers, downstream distributors, and companies sharing the same controlling shareholders. Although guarantees can improve short-term financing capacity, dense guarantee circles may amplify credit risk when one enterprise experiences repayment pressure. This study proposes a graph neural reasoning method for guarantee circle risk mining in industrial enterprises. The method constructs a guarantee-centered enterprise graph that integrates guarantee contracts, loan records, ownership structures, executive affiliations, supplier-customer links, litigation records, and overdue repayment events. A relation-aware graph neural network is used to learn enterprise risk representations, while a knowledge inference module identifies hidden guarantee circles, repeated guarantor exposure, and multi-hop risk transmission paths. Experiments are conducted on an industrial financing dataset containing 94,000 enterprises, 286,000 guarantee contracts, 142,000 bank loan records, 68,000 ownership links, 520,000 supplier-customer edges, and 13,400 confirmed guarantee-related risk events over 46 months. The proposed model shortens median risk-warning time from 79 days to 31 days compared with a loan-level scoring baseline. Graph reasoning identifies 5,870 high-risk guarantee circles and 2,430 enterprises exposed to more than three layers of indirect guarantee risk. The system consolidates 19,600 enterprise-level alerts into 4,280 guarantee-chain investigation cases. Monthly full-graph assessment is completed in 17.2 minutes, with a median inference latency of 52 ms per enterprise node. These results show that graph neural reasoning can reveal hidden guarantee-circle risks that are difficult to detect through single-enterprise financial indicators.

1. Zhao, J., Fan, J., & Li, L. (2026). A Study on an Explainable Causal-Enhanced LLM Agent for Predicting the Forming Quality of Automotive Component Materials.
2. Karami, A., & Igbokwe, C. (2025). The impact of big data characteristics on credit risk assessment. International Journal of Data Science and Analytics, 20(5), 4239-4259.
3. Wang, S., Feng, Y., & Fang, X. (2026, May). A Large Language Model-Enabled Multi-Agent Collaboration Method for Complex Task Solving. In 2026 6th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 253-256). IEEE.
4. You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.
5. Wu, Y., & Su, D. (2026). An Evaluation of Parental Question Characteristics and the Quality of AI Recommendations in Family Counseling for Children with Autism.
6. Freeman, K. M. (2025). Overlapping ownership along the supply chain. Journal of Financial and Quantitative Analysis, 60(1), 105-134.
7. Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). Retrieval-Guided Structured Reasoning and Interpretable Representation Learning for Large-Scale Video-Language Models.
8. Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
9. Zhang, G., Xu, Y., Zhang, M., Wang, S., & Lin, N. (2021). The brain network in support of social semantic accumulation. Social cognitive and affective neuroscience, 16(4), 393-405.
10. Chakraborty, S., & Sharov, S. (2025). Graph based approach on financial fraudulent detection and prediction. In Applied Graph Data Science (pp. 25-37). Morgan Kaufmann.
11. Bao, Y., & Wang, H. (2026). Large-scale Metrics Migration: A Systematic Approach to Rebuilding Production Measurement Infrastructure. Available at SSRN 7185660.
12. Xu, D., Gui, H., & Chen, H. (2026, March). Research on Layered Control and Fault Recovery Mechanisms for Fast Charging Safety Diagnosis of High-Voltage Battery Systems Under Charging Network Interoperability Conditions. In 2026 3rd International Conference on Electrical Technology and Automation Engineering (ETAE) (pp. 669-672). IEEE.
13. Qi, C., & Qiao, X. (2026). Efficient Data Sampling and Feature Selection Algorithms for Scalable Machine Learning Pipelines.
14. Theodorakopoulos, L., & Theodoropoulou, A. (2026). Big data and graph deep learning for financial decision support from social networks: A critical review. Electronics, 15(7), 1405.
15. Gong, S., Gamm, D. M., Edwards, K. L., Pattnaik, B. R., Liu, D. R., Wang, Y., ... & Spillane, A. Nonviral base editing ofKCNJ13mutation preserves vision in an inherited retinal channelopathy.
16. Du, Y., Chen, Z., Liu, Q., & Dong, Y. (2026). Co-identification of Vibration, Structural Looseness, and Surface Damage in Industrial Equipment Operation Videos.
17. Xiong, W., Zeng, Y., & Guo, Y. (2026). A Study on the Characterization of Building MEP System States and the Learning of Behavioral Patterns Driven by Large-Scale Operational Data. Available at SSRN 7121883.
18. Uesugi, I. (2025). Is it Possible to Finance SMEs without an Excessive Reliance on Personal Guarantees and Collateral?. In The Economics of SME Finance (pp. 235-269). Singapore: Springer Nature Singapore.
19. Hong, Z., Xu, T., & Chen, H. (2026). Automated Detection and Reclamation of Orphaned Compute and Storage Resources in Cloud Infrastructure. Available at SSRN 7116258.
20. Li, J., Ma, R., & Gu, Y. (2026). From Audit Requirements to Computable Software Architecture: A Rule-Engine and Evidence-Indexing Method for Trusted Digital Infrastructure.
21. Wu, J., Wu, D., Zhang, J., & Peng, Y. (2026). Generative AI Feedback in Junior High School Artistic Creation Evaluation. Available at SSRN 7244838.
22. Nowak, O., Davies, A., & Richter, A. (2026). Developing a Multi-Layer Credit and Market Risk Assessment Model for SMEs Participating in Capital Markets through Brokerage Intermediaries.
23. Huang, J., Xu, T., Yang, J., & Yin, J. (2026). A Graph Neural Network Approach for Financial Data Validation and Risk Identification in Large-Scale Intercompany Transactions. Available at SSRN 7175058.
24. Bao, Y., & Wang, H. (2026). Edge-to-Cloud Infrastructure Continuum: A Unified Framework for Optimizing Industrial IoT Systems. Available at SSRN 7185578.
25. Wilson, N., & Kacer, M. (2026). Default, Fraud, and Fraud Classification in UK COVID-19 Loan Schemes: Evidence from One Million Guaranteed Loans.
26. Du, Y. (2025). Research on Digital Quality Traceability System for Temperature-Controlled Supply Chain of Foreign Trade Wine Driven by Blockchain and IoT. Business and Social Sciences Proceedings, 4, 57-65.
27. Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.
28. Vanhoeven, A., & Chen, X. (2025). Default risk prediction for small and micro enterprises using a wide and deep learning framework. Transactions on Computational and Scientific Methods, 5(4).
29. Gu, X. (2026). Identifying Causal Effects and Analyzing Heterogeneity of User Growth Interventions on Digital Platforms: Evidence from Large-Scale Behavioral Data. Available at SSRN 6809181.
30. Vanhoeven, A., & Chen, X. (2025). Default risk prediction for small and micro enterprises using a wide and deep learning framework. Transactions on Computational and Scientific Methods, 5(4).