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

被引:3
|
作者
Xie, Xiaofeng [1 ]
Zhang, Fengying [2 ]
Liu, Li [1 ]
Yang, Yang [3 ]
Hu, Xiuying [1 ]
机构
[1] Sichuan Univ, West China Sch Nursing, West China Hosp, Innovat Ctr Nursing Res,Nursing Key Lab Sichuan P, Chengdu, Sichuan, Peoples R China
[2] Sichuan Univ, West China Sch Nursing, West China Hosp, Chengdu, Sichuan, Peoples R China
[3] Southwestern Univ Finance & Econ, Sch Econ Math, Chengdu, Sichuan, Peoples R China
来源
PLOS ONE | 2023年 / 18卷 / 02期
基金
中国国家自然科学基金;
关键词
DECISION;
D O I
10.1371/journal.pone.0281616
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:20
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