Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance


  • Yue Liu Wenzhou Polytechnic image/svg+xml
  • Wangke Lin Zhejiang College of Security Technology



blockchain technology, supply chain finance credit risk prediction, jellyfish search optimisation algorithm, deep echo state network


INTRODUCTION: From the perspective of blockchain, it establishes a credit risk evaluation index system for supply chain finance applicable to blockchain, constructs an accurate credit risk prediction model, and provides a reliable guarantee for the research of credit risk in supply chain finance.

OBJECTIVES: To address the inefficiency of the current credit risk prediction and evaluation model for supply chain finance.

METHODS: This paper proposes a combined blockchain supply chain financial credit risk prediction and evaluation method based on kernel principal component analysis and intelligent optimisation algorithm to improve Deep Echo State Network. Firstly, the evaluation system is constructed by describing the supply chain financial credit risk prediction and evaluation problem based on blockchain technology, analysing the evaluation indexes, and constructing the evaluation system; then, the parameters of DeepESN network are optimized by combining the kernel principal component analysis method with the JSO algorithm to construct the credit risk prediction and evaluation model of supply chain finance; finally, the effectiveness, robustness, and real-time performance of the proposed method are verified by simulation experiment analysis.

RESULTS: The results show that the proposed method improves the prediction efficiency of the prediction model.

CONCLUSION: The problems of insufficient scientific construction of index system and poor efficiency of risk prediction model of B2B E-commerce transaction size prediction method are effectively solved.


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How to Cite

Liu Y, Lin W. Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance. EAI Endorsed Scal Inf Syst [Internet]. 2024 Mar. 19 [cited 2024 Apr. 18];. Available from:



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