Application of Internet of Things and Machine Learning in Improving Supply Chain Financial Risk Management System

This study looks into the significant operational risk, information asymmetry, and modal lag problems that are currently present in the market for supply chain financing between banks and enterprises. Policy suggestions will be made based on the investigation’s results. This study set out to define supply chain finance and illustrate its core goal. The first and most crucial step was this. From a theoretical perspective, this study looked into the conceptual foundations of financial risk management in supply networks. In an effort to ascertain whether or not they could assist in the process of avoiding potential risks during the actual production of the product, the self-compensated trade finance theory, the information asymmetry theory, and the use of principle agents were all put to the test. After that, an investigation of the deficiencies of the operational strategies and associated business practices of the three primary supply chain finance models was carried out. On the basis of this, operational risks were found, and supply chain operations’ financial elements as well as risk sources were investigated. Creating a strategy for risk mitigation is the first step for a business. The next phase is to implement this plan as soon as it is practical, and you should continuously assess its effectiveness. In the context of managing pandemics and natural disasters, a significant amount of input from the government should be incorporated into the process of giving aid in the form of both financial and nonfinancial resources. This is the case since actively participating in one’s local community is one of the best methods to create a strong society.