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Credit rating of sustainable agricultural supply chain finance by integrating heterogeneous evaluation information and misclassification risk

Author

Listed:
  • Decui Liang

    (University of Electronic Science and Technology of China)

  • Wen Cao

    (University of Electronic Science and Technology of China)

  • Mingwei Wang

    (University of Electronic Science and Technology of China)

Abstract

Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk.

Suggested Citation

  • Decui Liang & Wen Cao & Mingwei Wang, 2023. "Credit rating of sustainable agricultural supply chain finance by integrating heterogeneous evaluation information and misclassification risk," Annals of Operations Research, Springer, vol. 331(1), pages 189-219, December.
  • Handle: RePEc:spr:annopr:v:331:y:2023:i:1:d:10.1007_s10479-021-04453-x
    DOI: 10.1007/s10479-021-04453-x
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