Knowledge-Guided Graph Reasoning for Early Warning of Credit Deterioration in Manufacturing Enterprises

James Carter; Emily Thompson; Oliver Bennett

Abstract

Manufacturing enterprises face credit deterioration risks caused by declining cash flow, production disruption, supplier instability, debt pressure, market demand changes, and legal disputes. These risks are difficult to detect early when analysis is limited to static balance-sheet variables. This study develops a knowledge-guided graph reasoning model for early warning of credit deterioration in manufacturing enterprises. The proposed method builds a manufacturing enterprise knowledge graph that integrates financial statements, tax records, patent activity, supplier relationships, downstream customer dependency, judicial records, and executive changes. A graph attention network is used to learn enterprise-level risk embeddings, while rule-based knowledge reasoning is applied to infer risk factors from overdue payments, shrinking orders, abnormal financing behavior, and ownership-related exposure. The empirical dataset contains 52,400 manufacturing firms, 640,000 supplier-customer links, 126,000 financing records, 83,000 litigation records, 412,000 tax-declaration entries, and 18,900 credit-risk events across four fiscal years. Compared with logistic regression and gradient boosting baselines, the proposed model shortens median credit-risk warning time from 91 days to 37 days before a confirmed credit downgrade. The enterprise risk ranking shows a Kendall correlation of 0.781 with subsequent bank-review priority lists. Knowledge reasoning contributes 6,840 interpretable warning chains, including cash-flow stress, supplier disruption, litigation escalation, and related-party guarantee risk. The model completes monthly portfolio assessment for 52,400 firms in 14.6 minutes. These findings indicate that knowledge-guided graph reasoning can provide earlier and more interpretable credit deterioration warnings for industrial financial risk management.

Keywords

Manufacturing enterprise financecredit deterioration warningknowledge graph reasoninggraph attention networkindustrial risk predictionsupplier-customer networkinterpretable risk analysis

References

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Publication details

Journal: Advances in Adaptive Intelligence
Volume: 1 (2026)
Article number: aai20260042
License: CC BY 4.0

[1] 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.

[2] Kamal, M. (2025). Financial Vulnerability Mapping in Global Supply Chains: Implications for US Trade Stability and Investment Risk. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), 1636-1667.

[3] Shao, W. (2026). Design and Implementation of an Open-Source Security Framework for Cloud Infrastructure. arXiv preprint arXiv:2604.03331.

[4] 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.

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

[6] Bose, A., & Kim, B. (2025). A novel similarity score for link prediction approach using financial transaction networks and firms’ attribute. IEEE Access, 13, 52051-52068.

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

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

[9] Park, Y. W., & Kikuchi, H. (2026). IT Infrastructure IV: Supply Chain Management. In Analog-Digital Transformation Strategy (pp. 197-225). Springer, Singapore.

[10] 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.

[11] Bao, Y., & Wang, H. (2026). Large-scale Metrics Migration: A Systematic Approach to Rebuilding Production Measurement Infrastructure. Available at SSRN 7185660.

[12] Yang, J. (2026). Stage‐Coupled Computational Framework for Stratiﬁed Accessibility and Equity Analysis in Community‐Based Elderly Care Services.

[13] Hellberg, R. (2026). Reconfiguring defence supply chains: procurement dynamics, inter-organizational dependence, and resilience under geopolitical uncertainty. Defence and Peace Economics, 1-40.

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

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

[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] Peeris, M. P., Baryannis, G., & Papadakis, E. (2026). Advancing Sustainable Supply Chains Through Knowledge Graph Completion and Graph-Based Artificial Intelligence. Sustainability, 18(6), 2825.

[18] 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.

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

[20] Nand, A., Gupta, G. K., & Bhagat, D. (2026). An inventory model for deteriorating items with linear deterioration and time-dependent demand under compound interest and backordering. OPSEARCH, 1-25.

[21] 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.

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

[23] 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.

[24] Parizad, B., Jamali, A., & Khayyam, H. (2025). Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation. Energy and AI, 100584.

[25] 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.

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

[27] Kehinde, O. (2025). Leveraging data-driven decision-making for enhanced risk management and resource allocation in projects. International Journal of Computer Applications Technology and Research, 14(2), 1-17.

[28] Hong, Z., Xu, T., & Chen, H. (2026). Automated Detection and Reclamation of Orphaned Compute and Storage Resources in Cloud Infrastructure. Available at SSRN 7116258.

[29] Wu, J., Wu, D., Zhang, J., & Peng, Y. (2026). Generative AI Feedback in Junior High School Artistic Creation Evaluation. Available at SSRN 7244838.

[30] Leuna-Obioha, J. (2026). Financing South Africa’s Water Infrastructure-the Critical Macro-Finance Perspective (Doctoral dissertation, Stellenbosch: Stellenbosch University).