A credit rating model in a fuzzy inference system environment
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Publication date
2019
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01A - Journal article
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Algorithms
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12
Issue / Number
7
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MDPI
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Basel
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Abstract
One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting.
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1999-4893
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Open access category
Gold
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Citation
Yazdi, A. K., Hanne, T., Wang, Y. J., & Wee, H.-M. (2019). A credit rating model in a fuzzy inference system environment. Algorithms, 12(7). https://doi.org/10.3390/A12070139