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Assessment of associated credit risk in the supply chain based on trade credit risk contagion

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  • Xiaofeng Xie
  • Fengying Zhang
  • Li Liu
  • Yang Yang
  • Xiuying Hu

Abstract

Assessment of associated credit risk in the supply chain is a challenge in current credit risk management practices. This paper proposes a new approach for assessing associated credit risk in the supply chain based on graph theory and fuzzy preference theory. First, we classified the credit risk of firms in the supply chain into two types, namely firms’ “own credit risk” and “credit risk contagion”; second, we designed a system of indicators for assessing the credit risks of firms in the supply chain and used fuzzy preference relations to obtain the fuzzy comparison judgment matrix of credit risk assessment indicators, on which basis we constructed the basic model for assessing the own credit risk of firms in the supply chain; third, we established a derivative model for assessing credit risk contagion. On this basis, we carried out a comprehensive assessment of the credit risk of firms in the supply chain by combining the two assessment results, revealing the contagion effect of associated credit risk in the supply chain based on trade credit risk contagion (TCRC). The case study shows that the credit risk assessment method proposed in this paper enables banks to accurately identify the credit risk status of firms in the supply chain, which helps curb the accumulation and outbreak of systemic financial risks.

Suggested Citation

  • Xiaofeng Xie & Fengying Zhang & Li Liu & Yang Yang & Xiuying Hu, 2023. "Assessment of associated credit risk in the supply chain based on trade credit risk contagion," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-20, February.
    • Handle: RePEc:plo:pone00:0281616
    DOI: 10.1371/journal.pone.0281616
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    References listed on IDEAS

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    1. Saba Moradi & Farimah Mokhatab Rafiei, 2019. "A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-27, December.
    2. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    3. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    4. Sripad K. Devalkar & Harish Krishnan, 2019. "The Impact of Working Capital Financing Costs on the Efficiency of Trade Credit," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 878-889, April.
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    Cited by:

    1. Tao Yu & Wei Huang & Xin Tang & Duosi Zheng, 2025. "A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-25, January.

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