The uncertainty of the global economic environment and the complexity of supply chain networks have brought unprecedented risk management pressure to enterprises. Artificial intelligence leverages its capabilities in large-scale data processing, pattern recognition and prediction to provide new methods and tools for supply chain risk early warning. This paper analyzes its role in the digitalization of supply chain finance, intelligent risk control, and multi-source data integration, and studies the value of artificial intelligence in enhancing supply chain transparency, strengthening risk identification, and optimizing the efficiency of capital flow and logistics. At the same time, it points out that artificial intelligence still has deficiencies in responding to systemic risks, ensuring data security, and the interpretability of models. Research shows that integrating artificial intelligence with technologies such as blockchain and the Internet of Things can promote the development of supply chain risk management towards intelligence and systematization. However, in the future, continuous improvement is needed in data standardization, technical governance and policy coordination to establish an efficient and reliable supply chain risk early warning system, providing theoretical and practical basis for enterprises to achieve scientific, intelligent and sustainable risk management.

Yanchun Wang. Research on the Application of Artificial Intelligence in Supply Chain Risk Early Warning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 51-59. https://doi.doi.org/10.3807/IJSSEM.2026.070106.

[1] Nilaish N, Tiwari B. Artificial intelligence applications in supply chain risk management for reliable operations. Risk, Reliability and Resilience in Operations Management, 2025:183-200.

[2] Tiwari P A. Moderating Role of Supply Chain Re-engineering Capabilities on Artificial Intelligence-Based Risk Management and Supply Chain Agility: A Resource-Based View. Studies in Systems, Decision and Control, 2024:167-179.

[3] Xu, H. (2025). Research on the Implementation Path of Resource Optimization and Sustainable Development of Supply Chain. International Journal of Humanities and Social Science, 1(2), 12-18.

[4] Jahin M A, Naife S A, Saha A K, et al. AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis. 2023.

[5] Wu X, Bao W. Research on the Design of a Blockchain Logistics Information Platform Based on Reputation Proof Consensus Algorithm. Procedia Computer Science, 2025, 262: 973-981.

[6] Huang, J. (2025). Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning. European Journal of Business, Economics & Management, 1(3), 104-110.

[7] Zheng, H. (2025). Research on Lifecycle Configuration and Reclamation Strategies for Edge Nodes Based on Microservice Architectures. Advances in Computer and Communication, 6(5).

[8] Zeng N, Liu Y, Knig M. 4D BIM-Enabled Look-Ahead Scheduling for Early Warning of Off-Site Supply Chain Disruptions. Journal of Construction Engineering and Management, 2023.

[9] Zhu, Z. (2025). Cutting-Edge Challenges and Solutions for the Integration of Vector Database and AI Technology. European Journal of AI, Computing & Informatics, 1(2), 51-57.

[10] Owolabi O S. Network Analysis for Systemic Risk Assessment in Supply Chains: A Cross-Disciplinary Framework Integrating Financial Contagion Models. Journal of Data Analysis and Information Processing, 2025, 13(3):23.

[11] Huang H, Xu Y, Nan X, et al. Research on Credit Risk Assessment and Early Warning of Supply Chain Finance Based on CNN Model. Lecture Notes in Electrical Engineering, 2025:207-214.

[12] Tang X, Wu X, Bao W. Intelligent Prediction-Inventory-Scheduling Closed-Loop Nearshore Supply Chain Decision System. Advances in Management and Intelligent Technologies, 2025, 1(4).

[13] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.

[14] Chang, Chen-Wei. "AI-Driven Privacy Audit Automation and Data Provenance Tracking in Large-Scale Systems. " (2025).

[15] Zhang K. Research on the Application of Homomorphic Encryption-Based Machine Learning Privacy Protection Technology in Precision Marketing[C]//2025 3rd International Conference on Data Science and Network Security (ICDSNS). IEEE, 2025: 1-6.

[16] Yang D, Liu X. Collaborative Algorithm for User Trust and Data Security Based on Blockchain and Machine Learning. Procedia Computer Science, 2025, 262: 757-765.

[17] Xu, H. (2025). Optimization of Packaging Procurement and Supplier Strategy in Global Supply Chain. European Journal of Business, Economics & Management, 1(3), 111-117.

[18] Lu, Z. (2025). AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System. European Journal of AI, Computing & Informatics, 1(4), 43-50.

[19] Dingyuan Liu. The Relationship between Household Consumption Pattern Changes under Disasters and the Recovery of Business Ecosystems. Academic Journal of Business & Management (2025), Vol. 7, Issue 12: 151-156.

[20] Zheng, H. (2025). Research on Delay-aware Scheduling Algorithms for Edge Task Migration in High-concurrency Environments. Engineering Advances, 5(4).