An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains

Authors

  • Zhiyuan Wang Logistics and Supply Chain Management, Cranfield University, United Kingdom
  • Jia Jia Chew Accounting, Universiti Sains Malaysia, Malaysia
  • Xiangang Wei Management Science and Engineering, Xi'an University of Architecture and Technology, Shaanxi, China
  • Ke Hu Mechanical Design, Manufacturing and Automation, Heilongjiang Institute of Technology, Heilongjiang, China
  • Shun Yi Software Engineering, Shaanxi University of Science and Technology, Shaanxi, China
  • Shun Yi Software Engineering, Shaanxi University of Science and Technology, Shaanxi, China

Keywords:

Artificial Intelligence, Financial Risk Control, Multinational Supply Chain, Intelligent Accounting Systems, Machine Learning, Risk Prediction Models

Abstract

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.

References

Choi, T. M., & He, X. (2020). A survey of machine learning applications in supply chain management. Computers & Industrial Engineering, 148, 106635. https://doi.org/10.1016/j.cie.2020.106635

He, H., & Xu, L. (2021). AI-based predictive models for financial risk management in supply chains. Journal of Financial Risk Management, 14(2), 124-135. https://doi.org/10.1016/j.jfrm.2020.12.008

Sharma, A., & Soni, G. (2019). Machine learning applications for managing financial risks in cross-border supply chains. International Journal of Production Economics, 221, 107478. https://doi.org/10.1016/j.ijpe.2019.07.003

Zha, H., & Zhang, L. (2020). Predicting financial risks in international trade: A machine learning approach. Computers, Materials & Continua, 63(1), 197-212. https://doi.org/10.32604/cmc.2020.010569

Van Der Meer, R., & Knol, K. (2020). Financial risk assessment and forecasting in the supply chain: A machine learning-based framework. International Journal of Production Research, 58(4), 1095-1107. https://doi.org/10.1080/00207543.2019.1644015

Jain, S., & Kaur, G. (2020). Risk management in supply chains using artificial intelligence. Journal of Risk and Financial Management, 13(3), 43. https://doi.org/10.3390/jrfm13030043

Allen, F., & Carletti, E. (2021). Financial networks and systemic risk. Journal of Financial Stability, 12(4), 364-382. https://doi.org/10.1016/j.jfs.2021.07.004

Li, X., & Li, S. (2020). Machine learning for financial risk prediction in global supply chains. International Journal of Data Science and Analytics, 9(4), 325-338. https://doi.org/10.1007/s41060-019-00153-6

McMillan, S., & Wang, J. (2019). Credit scoring in multinational supply chains using machine learning models. Computational Economics, 53(2), 547-572. https://doi.org/10.1007/s10614-018-9844-y

Li, Y., & Xu, Y. (2020). A deep learning approach to financial risk prediction in global supply chains. Financial Innovation, 6(1), 22. https://doi.org/10.1186/s40854-020-00203-x

Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005

Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13-39. https://doi.org/10.1080/13675560902736537

Wamba, S. F., Akter, S., Coltman, T., & Ngai, E. W. T. (2015). Guest editorial: Information technology-enabled supply chain transformation. International Journal of Operations & Production Management, 35(4), 509-513. https://doi.org/10.1108/IJOPM-03-2015-0130

Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34. https://doi.org/10.1016/j.compind.2017.04.002

Ivanov, D., Dolgui, A., Sokolov, B., Ivanova, M., & Werner, F. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. European Journal of Operational Research, 294(3), 933-948. https://doi.org/10.1016/j.ejor.2019.03.035

Zhao, K., Kumar, A., Harrison, T. P., & Yen, J. (2011). Analyzing the resilience of complex supply network topologies against random and targeted disruptions. IEEE Systems Journal, 5(1), 28-39. https://doi.org/10.1109/JSYST.2010.2090353

Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive analytics and artificial intelligence in supply chain management: Review and implications for the future. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024

Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488. https://doi.org/10.1016/j.ijpe.2005.12.006

Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International Journal of Production Research, 57(15-16), 4719-4742. https://doi.org/10.1080/00207543.2017.1402140

Brintrup, A., Wang, Y., Tiwari, A., & McFarlane, D. (2015). Risk propagation analysis in supply networks using graph theory and Markov models. IEEE Transactions on Engineering Management, 62(2), 329-339. https://doi.org/10.1109/TEM.2015.2415395

Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108-1118. https://doi.org/10.1016/j.jclepro.2016.03.059

Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Blome, C. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341-361. https://doi.org/10.1111/1467-8551.12355

Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455-470. https://doi.org/10.1016/j.ijpe.2015.06.032

Govindan, K., Azevedo, S. G., Carvalho, H., & Cruz-Machado, V. (2015). Lean, green and resilient practices influence on supply chain performance: Interpretive structural modeling approach. International Journal of Environmental Science and Technology, 12(1), 15-34. https://doi.org/10.1007/s13762-013-0409-7

Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133. https://doi.org/10.1016/j.ijpe.2015.10.023

Ivanov, D. (2020). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 1-21. https://doi.org/10.1007/s10479-020-03640-6

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Published

2025-04-23

How to Cite

Wang, Z., Chew, J. J., Wei, X., Hu, K., Yi, S., & Yi, S. (2025). An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains. Journal of Theory and Practice in Economics and Management, 2(2), 49–62. Retrieved from https://woodyinternational.com/index.php/jtpem/article/view/208

Issue

Vol. 2 No. 2 (2025)

Section

Articles

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Copyright (c) 2025 Zhiyuan Wang, Jia Jia Chew, Xiangang Wei, Ke Hu, Shun Yi, Shun Yi

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.