Construction of Credit Assessment Model for International Import and Export Trade Based on Fuzzy Hierarchical Analysis

Yan Liu and Gengxin Sun

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: With the continuous expansion and development of import and export trade, the problem of credit risk faced by foreign trade enterprises has received increasing attention. This thesis focuses on the credit risk problem in import and export trade based on fuzzy hierarchical analysis; international import and export trade credit risk management risk is measured and evaluated to derive the ranking of key risk factors; the risk factors in the guideline layer are ranked from largest to smallest in order of legal risk, market risk, financial risk, and policy risk. The main purpose of this study is to build a fuzzy AHP international import and export trade credit evaluation model, which can provide reliable credit evaluation when enterprises conduct import and export trade and can better help enterprises to complete the trade. The analysis algorithm implemented in this study is a common and effective algorithm used for credit modeling, and more exploration and research on foreign trade credit modeling using existing modeling techniques are needed. The modeling techniques for credit evaluation of international import and export trade should be based on the credit information available to the enterprise to select the most appropriate modeling techniques and propose the most practical evaluation model. This study uses qualitative and quantitative, theoretical, and empirical comprehensive analysis methods based on fuzzy hierarchical analysis, draws on theories and models of credit risk management of 100 well-known enterprises around the world, and combines the actual situation of import and export trade. The credit evaluation model of international import and export trade based on fuzzy hierarchical analysis is constructed with certain operability and can make the credit evaluation of international import and export trade more accurate and reliable.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2705068

DOI: 10.1155/2022/2705068

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