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Abstract
This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.
Highlights
With the financing difficulties of small and mediumsized enterprises (SMEs) in traditional financial institutions such as banks, supply chain finance is becoming more and more urgent
Based on the above analysis, this article proposes the credit risk assessment under the supply chain finance mode based on the Internet of Things on the basis of the credit risk assessment of the traditional financial mode and uses the support vector machine (SVM) algorithm and Logistic regression method to establish the credit risk assessment model, which overcomes the subjective shortcomings of expert assessment and improves the objectivity of the assessment.[18,19]
The optimization method used in this article is to introduce the SVM algorithm based on the logistic regression model.[23]
Summary
With the financing difficulties of small and mediumsized enterprises (SMEs) in traditional financial institutions such as banks, supply chain finance is becoming more and more urgent. The support vector machine (SVM) algorithm and Logistic regression method are used to establish a credit risk assessment model, which reduces the current limitations of most of the supply chain financial business metrics relying on expert evaluation.
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