Graph-Based Financing Stress Identification for Enterprise Clusters in Industrial Parks

Sarah Mitchell; Thomas Walker

Abstract

Industrial parks contain dense enterprise clusters with shared suppliers, common financing channels, related-party transactions, land-lease relationships, and local guarantee networks. Financial stress in one enterprise may spread to nearby firms through payment delays, joint borrowing, equipment leasing, and cross-guarantee arrangements. This study proposes a graph-based financing stress identification model for enterprise clusters in industrial parks. The model constructs a heterogeneous enterprise graph by integrating loan records, tax payments, trade invoices, guarantee contracts, park tenancy data, electricity consumption, and supplier-customer links. A graph neural encoder is used to learn enterprise risk representations, while a knowledge reasoning module identifies hidden financing stress paths caused by shared creditors, delayed receivables, and affiliated ownership structures. Experiments are conducted on a dataset covering 34 industrial parks, 46,800 enterprises, 720,000 invoice relationships, 118,000 loan records, 39,600 guarantee links, and 15,400 financing stress events over 42 months. The proposed model reduces median early-warning time from 68 days to 26 days compared with a financial-indicator-only baseline. The system identifies 7,920 park-level risk paths, including clustered overdue payments, shared-creditor pressure, and related-party debt exposure. Risk-path aggregation compresses 12,600 enterprise alerts into 3,180 investigation groups for park-level financial supervision. Monthly assessment of all enterprises is completed in 11.8 minutes, with a median inference latency of 46 ms per enterprise node. The results show that graph-based risk inference can improve financing stress identification in industrial parks by capturing both individual enterprise weakness and cluster-level financial dependency.

Keywords

Industrial park financefinancing stress identificationenterprise graphknowledge reasoninggraph neural networkcluster riskindustrial financial supervision

References

Liang, R., Feifan, F. N. U., Liang, Y., & Ye, Z. (2025). Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition. Frontiers in Artificial Intelligence Research, 2(1), 51-57.
Seretidou, D., Billios, D., & Stavropoulos, A. (2025). Integrative analysis of traditional and cash flow financial ratios: Insights from a systematic comparative review. Risks, 13(4), 62.
Wu, C., & Chen, H. (2025). Research on system service convergence architecture for AR/VR system.
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.
Qi, C., & Qiao, X. (2026). Building Reliable AI Systems: A Framework That Bridges Architecture and Operations. Available at SSRN 6795298.
Jombe, M. K., & Maota, T. (2026). Applying Signalling Theory to Internal Auditing: A Novel Approach to Early Distress Detection and Strategic Organisational Resilience. In Internal Auditors as Strategic Partners in Business Resilience (pp. 51-80). IGI Global Scientific Publishing.
Xu, T., Zhu, W., & Zhang, J. (2026). Stability and Consistency of Explainable Deep Learning Methods in Credit Risk Assessment. Available at SSRN 6893938.
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.
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.
Sekar, R. (2025). The Role of Graph Databases in the Finance Sector: Building Social Networks for Enhanced Financial Insights and Fraud Prevention. Journal Of Engineering And Computer Sciences, 4(8), 836-844.
Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). Retrieval-Guided Structured Reasoning and Interpretable Representation Learning for Large-Scale Video-Language Models.
Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
Lin, N., Xu, Y., Yang, H., Zhang, G., Zhang, M., Wang, S., ... & Li, X. (2020). Dissociating the neural correlates of the sociality and plausibility effects in simple conceptual combination. Brain Structure and Function, 225(3), 995-1008.
Miller, S. M., Wang, P. L., & Yang, T. (2026). Does Enterprise Risk Management Deliver Strategic Value? Evidence from Mergers and Acquisiti
Bao, Y., Qiu, Y., & Wang, H. (2026). FLARE: A Real-Time Framework for Detecting Malicious Account Activities at Internet Scale. Available at SSRN 7185659.
Hong, Z., Xu, T., & Chen, H. (2026). A Study on Test Coverage Mapping and Release Risk Prediction in Continuous Delivery of Cloud Services.
Huang, R. (2026). Edge-Centric Generative AI: A Survey on Efficient Inference for Large Language Models in Resource-Constrained Environments. Journal of Computer and Communications, 14(4), 238-253.
Fridkin, S., & Bendersky, M. (2026). Interpretable machine learning: a comprehensive review of foundations, methods, and the path forward. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 16(1), e70075.
Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
Huang, J., Xu, T., Yang, J., & Yin, J. (2026). Blockage Early Warning and Financial Impact Quantification in the Monthly Closing Process of Fintech Companies. Available at SSRN 7179198.
Granato, A., & Drall, P. (2026). Private Credit's State Backstop: How Private Equity Socializes Risk Through Insurers. Available at SSRN 7152239.
Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.
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.
Al-Sari, A. (2025). The collapse of silicon valley bank: a critical analysis of regulatory shortcomings and risk management under Basel III: A. Al Sari. Journal of Banking Regulation, 26(4), 661-677.
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.
You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.
Buda, G. (2026). Industrial symbiosis potential in Uganda's Kampala Industrial and Business Park. Sustainability challenges and opportunities. Sustainable Futures, 11, 101720.
Wu, Y., & Su, D. (2026). An Evaluation of Parental Question Characteristics and the Quality of AI Recommendations in Family Counseling for Children with Autism.
Mostafavi, A., & Chaibakhsh, A. (2026). Dynamic physics-based digital twin for supervisory, condition monitoring and fault diagnosis of industrial turboshaft engines. Digital Twin, 3(2), 2598085.
Bao, Y., Qiu, Y., & Wang, H. (2026, May). Unified Identity Layer for Hyperscale Ecosystems: A Framework for Cross-Platform User Attribution and Experimentation. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 540-543). IEEE.

Publication details

Journal: Markets, Organizations & Public Value
Volume: 1 (2026)
Article number: mv20260083
License: CC BY 4.0

[1] Liang, R., Feifan, F. N. U., Liang, Y., & Ye, Z. (2025). Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition. Frontiers in Artificial Intelligence Research, 2(1), 51-57.

[2] Seretidou, D., Billios, D., & Stavropoulos, A. (2025). Integrative analysis of traditional and cash flow financial ratios: Insights from a systematic comparative review. Risks, 13(4), 62.

[3] Wu, C., & Chen, H. (2025). Research on system service convergence architecture for AR/VR system.

[4] 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.

[5] Qi, C., & Qiao, X. (2026). Building Reliable AI Systems: A Framework That Bridges Architecture and Operations. Available at SSRN 6795298.

[6] Jombe, M. K., & Maota, T. (2026). Applying Signalling Theory to Internal Auditing: A Novel Approach to Early Distress Detection and Strategic Organisational Resilience. In Internal Auditors as Strategic Partners in Business Resilience (pp. 51-80). IGI Global Scientific Publishing.

[7] Xu, T., Zhu, W., & Zhang, J. (2026). Stability and Consistency of Explainable Deep Learning Methods in Credit Risk Assessment. Available at SSRN 6893938.

[8] 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.

[9] 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.

[10] Sekar, R. (2025). The Role of Graph Databases in the Finance Sector: Building Social Networks for Enhanced Financial Insights and Fraud Prevention. Journal Of Engineering And Computer Sciences, 4(8), 836-844.

[11] Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). Retrieval-Guided Structured Reasoning and Interpretable Representation Learning for Large-Scale Video-Language Models.

[12] Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.

[13] Lin, N., Xu, Y., Yang, H., Zhang, G., Zhang, M., Wang, S., ... & Li, X. (2020). Dissociating the neural correlates of the sociality and plausibility effects in simple conceptual combination. Brain Structure and Function, 225(3), 995-1008.

[14] Miller, S. M., Wang, P. L., & Yang, T. (2026). Does Enterprise Risk Management Deliver Strategic Value? Evidence from Mergers and Acquisiti

[15] Bao, Y., Qiu, Y., & Wang, H. (2026). FLARE: A Real-Time Framework for Detecting Malicious Account Activities at Internet Scale. Available at SSRN 7185659.

[16] Hong, Z., Xu, T., & Chen, H. (2026). A Study on Test Coverage Mapping and Release Risk Prediction in Continuous Delivery of Cloud Services.

[17] Huang, R. (2026). Edge-Centric Generative AI: A Survey on Efficient Inference for Large Language Models in Resource-Constrained Environments. Journal of Computer and Communications, 14(4), 238-253.

[18] Fridkin, S., & Bendersky, M. (2026). Interpretable machine learning: a comprehensive review of foundations, methods, and the path forward. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 16(1), e70075.

[19] Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.

[20] Huang, J., Xu, T., Yang, J., & Yin, J. (2026). Blockage Early Warning and Financial Impact Quantification in the Monthly Closing Process of Fintech Companies. Available at SSRN 7179198.

[21] Granato, A., & Drall, P. (2026). Private Credit's State Backstop: How Private Equity Socializes Risk Through Insurers. Available at SSRN 7152239.

[22] Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.

[23] 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.

[24] Al-Sari, A. (2025). The collapse of silicon valley bank: a critical analysis of regulatory shortcomings and risk management under Basel III: A. Al Sari. Journal of Banking Regulation, 26(4), 661-677.

[25] 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.

[26] You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.

[27] Buda, G. (2026). Industrial symbiosis potential in Uganda's Kampala Industrial and Business Park. Sustainability challenges and opportunities. Sustainable Futures, 11, 101720.

[28] Wu, Y., & Su, D. (2026). An Evaluation of Parental Question Characteristics and the Quality of AI Recommendations in Family Counseling for Children with Autism.

[29] Mostafavi, A., & Chaibakhsh, A. (2026). Dynamic physics-based digital twin for supervisory, condition monitoring and fault diagnosis of industrial turboshaft engines. Digital Twin, 3(2), 2598085.

[30] Bao, Y., Qiu, Y., & Wang, H. (2026, May). Unified Identity Layer for Hyperscale Ecosystems: A Framework for Cross-Platform User Attribution and Experimentation. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 540-543). IEEE.