A Credit Rating Model in a Fuzzy Inference System Environment
AbstractOne 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. View Full-Text
Share & Cite This Article
Karbassi Yazdi, A.; Hanne, T.; Wang, Y.J.; Wee, H.-M. A Credit Rating Model in a Fuzzy Inference System Environment. Algorithms 2019, 12, 139.
Karbassi Yazdi A, Hanne T, Wang YJ, Wee H-M. A Credit Rating Model in a Fuzzy Inference System Environment. Algorithms. 2019; 12(7):139.Chicago/Turabian Style
Karbassi Yazdi, Amir; Hanne, Thomas; Wang, Yong J.; Wee, Hui-Ming. 2019. "A Credit Rating Model in a Fuzzy Inference System Environment." Algorithms 12, no. 7: 139.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.