Enterprise Credit Risk Assessment Based on Hybrid Fuzzy Synthetic Evaluation Model
DOI:
https://doi.org/10.11113/mjfas.v20n4.3510Keywords:
Hybrid fuzzy synthetic evaluation, average score technology, ranking technology, corporate credit risk covid-19 post-vaccination, fuzzy cognitive maps, neutrosophic cognitive maps, neutrosophic set.Abstract
This article uses a hybrid fuzzy synthetic evaluation model to evaluate the credit risk of Shouguang vegetable enterprises, and designs four risk levels: impact degree, occurrence probability, risk manageability and government support level. The model uses average score technology to calculate the scores of secondary indicators, primary indicators and evaluation targets at each level, uses sorting technology to sort secondary indicators and primary indicators at each level, and uses fuzzy synthetic evaluation technology to construct Shouguang enterprise credit risk evaluation. The model uses the geometric mean method to calculate the comprehensive score, and summarizes the comprehensive score results to make decisions. Among them, the data in this article comes from a questionnaire, which was completed by a total of 41 professionals who have a better understanding of the credit risks of Shouguang vegetable companies Through calculation, the comprehensive credit risk score of Shouguang Vegetable Enterprise is 2.8585. The risk is medium risk. Banks can lend based on the operation of specific enterprises. The calculation results also show that the risk assessment results at different levels are not completely consistent. The main first-level risk indicators at the first three levels are "enterprise technological innovation" and "enterprise financial status", and the main first-level risk indicators at the last level are "Enterprise management level" and "enterprise development plan". Indicators related to corporate technological innovation and financial status are the main influencing indicators of the credit risk of Shouguang vegetable companies.
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Copyright (c) 2024 Tao Li, Majid Khan Majahar Ali, Lili Wu, Ying Tian
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