An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains - Wang, Zhiyuan; Chew, Jia Jia; Wei, Xiangang; Hu, Ke; Yi, Shun; Yi, Shun (Journal of Theory and Practice in Economics and Management, 2025)
In the context of increasingly complex and globalized supply chains, financial risk control faces unprecedented challenges, particularly in multinational enterprises where data heterogeneity, regulatory discrepancies, and operational opacity hinder effective risk detection. This study investigates how artificial intelligence (AI) technologies can be integrated into financial risk control systems to enhance their accuracy, scalability, and adaptability in multinational supply chain environments. To address this problem, we propose a novel AI-powered intelligent risk control system that integrates structured and unstructured financial data from cross-border operations. The system architecture consists of a multi-layer design incorporating data preprocessing, dynamic risk modeling, and real-time decision-making modules. Core AI techniques employed include LSTM-based time-series forecasting, XGBoost and LightGBM for tabular risk scoring, and BERT-based natural language processing for contract and invoice analysis. Empirical validation is conducted using a real-world dataset collected from multinational supply chain partners across different regions. Experimental results demonstrate that the proposed system outperforms traditional rule-based approaches in identifying abnormal financial behavior, reducing response time, and improving prediction accuracy. The findings highlight the practical value of integrating AI into global financial governance systems and provide a scalable framework for intelligent accounting and risk mitigation in cross-border supply chain contexts. This study contributes to the literature by bridging AI and financial risk control in global supply chains, offering both a theoretical model and an applied solution with demonstrated effectiveness.