Publication

Abstract : This review paper explores the application of machine learning and big data in improving supply chain financial risk management systems. The importance of these technologies in supply chain management is discussed, highlighting their potential to improve risk assessment, real-time monitoring, predictive, cost optimization, and fraud detection.The use of machine learning algorithms to analyze largevolumes of financial data from suppliers, assess their financial health, and make accurate predictionsabout future risks is examined. The application of big data to provide a comprehensive view of the supplychain, optimize costs, and enhance risk management through fraud detection is also explored.The benefitsand challenges of using machine learning and big data in supply chain financial risk management are analyzed. While these technologies have the potential to significantly improve supply chain performance, challenges such as obtaining accurate and complete data and integrating data from different sources must also be considered. Overreliance on technology and the exclusion of qualitative factors such as relationships and reputation can also be a challenge. The potential benefits and challenges of using machine learning and big data in supply chain financial risk management. Organizations can benefit greatly from developing appropriate strategies to leverage these technologies and taking a holistic approach to risk management that incorporates both quantitative and qualitative factors