Platform Transaction Graph Learning for Credit Risk Prediction in Industrial Internet Ecosystems

William Harris; Charlotte Bennett

Abstract

Industrial Internet platforms connect manufacturers, equipment suppliers, maintenance providers, logistics firms, software vendors, and financial institutions. These platforms record transaction orders, equipment usage, maintenance requests, digital contracts, settlement behavior, and service-performance data, which can provide early signals of enterprise credit deterioration. However, conventional credit models do not fully use platform-level relational data and may overlook risks hidden in transaction dependency and service-chain instability. This study develops a platform transaction graph learning method for credit risk prediction in industrial Internet ecosystems. The method constructs a multi-relational graph using enterprise profiles, platform orders, equipment service records, digital invoices, settlement delays, supplier-customer links, maintenance disputes, and financing applications. A graph neural network learns enterprise risk embeddings, while a knowledge reasoning module infers warning paths from shrinking orders, abnormal service interruption, delayed settlement, supplier loss, and maintenance-cost escalation. The dataset contains 58,700 platform enterprises, 2.48 million platform orders, 6.2 million equipment-service records, 1.36 million digital invoices, 410,000 settlement-delay events, and 10,800 confirmed credit-risk cases over 45 months. The proposed model reduces median credit-risk warning time from 88 days to 34 days compared with a financial-statement baseline. It identifies 6,430 enterprises with persistent transaction shrinkage and 3,760 firms affected by upstream service-chain disruption before formal risk confirmation. Graph-based explanation generates 11,900 interpretable warning paths. Monthly platform-wide risk assessment is completed in 16.8 minutes. The results show that platform transaction graph learning can strengthen industrial financial risk prediction by combining enterprise relations, operational behavior, and knowledge-based risk inference.

Keywords

Industrial Internet financeplatform transaction graphcredit risk predictiongraph neural networkknowledge reasoningenterprise credit deteriorationdigital industrial ecosystem

References

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Panchal, S. S., Ansari, I., Azim, K. S., Bhujel, K., & Ahirrao, Y. S. (2025). Cyber risk and business resilience: A financial perspective on IT security investment decisions. The USA Journals TAJET, 7(09), 23-48.
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Bao, Y., & Wang, H. (2026). Edge-to-Cloud Infrastructure Continuum: A Unified Framework for Optimizing Industrial IoT Systems. Available at SSRN 7185578.
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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.
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.
This issue is particularly relevant to Industrial Internet credit assessment, where risk signals are distributed across transaction, equipment, service, payment, and relationship data rather than stored in a single financial database
Bao, Y., & Wang, H. (2026). Large-scale Metrics Migration: A Systematic Approach to Rebuilding Production Measurement Infrastructure. Available at SSRN 7185660.
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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.
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Bao, Y., Qiu, Y., & Wang, H. (2026). FLARE: A Real-Time Framework for Detecting Malicious Account Activities at Internet Scale. Available at SSRN 7185659.
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Publication details

Journal: Interdisciplinary Intelligence and Emerging Technologies
Volume: 1 (2026)
Article number: ajg20260012
License: CC BY 4.0

[1] 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.

[2] Panchal, S. S., Ansari, I., Azim, K. S., Bhujel, K., & Ahirrao, Y. S. (2025). Cyber risk and business resilience: A financial perspective on IT security investment decisions. The USA Journals TAJET, 7(09), 23-48.

[3] Hong, Z., Liang, S., Xu, T., & Chen, H. (2026). A Study on Failover Verification and Recovery Objective Prediction for Cross-Region Cloud Services.

[4] Bao, Y., & Wang, H. (2026). Edge-to-Cloud Infrastructure Continuum: A Unified Framework for Optimizing Industrial IoT Systems. Available at SSRN 7185578.

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

[6] Sherif, Z., & Salonitis, K. (2025). A systematic review of decision tools for process selection and performance improvement in manufacturing. The International Journal of Advanced Manufacturing Technology, 141(3), 1113-1141.

[7] 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.

[8] 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.

[9] This issue is particularly relevant to Industrial Internet credit assessment, where risk signals are distributed across transaction, equipment, service, payment, and relationship data rather than stored in a single financial database

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

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

[12] 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.

[13] Mollah, M. A. R. (2026). AI-Driven Business Intelligence for Real-Time Fraud Risk Monitoring in Digital Payment Platforms: A Framework-Based Review. International Journal of Scientific Interdisciplinary Research, 7(1), 527-580.

[14] 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.

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

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

[17] Sudasinghe Pathirannehelage, V. (2026). Understanding Customer Perceptions of Reliability in Rock-Breaking Equipment Industry: A Case Study on Normet’s Xrock Product Line.

[18] Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). The Impact of VR Sketching Simulations on the Assessment of Scale and Site Suitability in Public Art Spaces.

[19] Liang, S., Du, Y., Chen, W., & Liu, Z. (2026). Order Allocation and Emergency Transportation Decisions Amid Multi-Source Procurement Disruptions.

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

[21] Sahu, R. (2025). Enterprise knowledge graphs: Revolutionizing system dependencies. Journal Of Engineering And Computer Sciences, 4(8), 940-948.

[22] 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.

[23] Yang, Q., & Du, Y. (2026). Predicting Rollback Risks and Controlling Gradual Rollout Traffic for Online Ranking Feature Deployments.

[24] Paraskevas, K., & Christos, T. (2025). Data preprocessing and feature engineering for data mining: Techniques, tools, and best practices. Ai, 6(10), 257.

[25] Zhang, Z. (2026). A Study on the Impact of Cost Presentation on Decision-making Bias and Cancellation Behavior in Online Subscriptions. Available at SSRN 7349839.

[26] Liang, S., Hsu, K. H., & Liu, Z. (2026). Rolling Scheduling and Capacity Balancing for Server Assembly Under Engineering Change Disturbances.

[27] Fardous, M. (2025). Predictive analytics for working capital management: Machine learning applications in cash flow and liquidity forecasting. American Journal of Scholarly Research and Innovation, 4(01), 662-694.

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

[29] Du, Y., Chen, Z., Liu, Q., & Dong, Y. (2026). Co-identification of Vibration, Structural Looseness, and Surface Damage in Industrial Equipment Operation Videos.

[30] Senić, A. (2026). Risk Heterogeneity and Directed Primary–Secondary Interactions in Completed Road Infrastructure Projects: A Data-Driven Analysis. Sustainability, 18(16), 8588.