A Credit Rating Model in a Fuzzy Inference System Environment

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.

Bond rating determinants and modeling: evidence from India

“…We use long-term borrowing to total borrowing as a structure ratio, equity to total assets ratio as a measure of solvency, and interest coverage ratio to measure coverage (Horrigan, 1966;Belkaoui, 1980;Ederington, 1985;Kamstra et al, 2001;Touray, 2004;Brabazon and O'Neil, 2006;Bellovary et al, 2007;Doumpos et al, 2015;Feki and Khoufi, 2015). Managerial characteristics represent management quality and are determined using ownership (measured using strategic ownership as an essential measure of control); reputation and experience, measured through the firm's age (See Yazdi et al, 2019;Czarnitzki and Kraft, 2007). Market characteristics are determined using size and valuation.…”

“…, 2015; Feki and Khoufi, 2015). Managerial characteristics represent management quality and are determined using ownership (measured using strategic ownership as an essential measure of control); reputation and experience, measured through the firm's age (See Yazdi et al. , 2019; Czarnitzki and Kraft, 2007).…”

New Data Mining approach for clustering Export Credit Agencies (ECAs) based on performance criteria: a bibliometric citation analysis for the period 2005 to 2020

“…Consequently, the result showed that if the economic factors such as financial capacity, institutional capacity, and government are in a good mood, establishing the ECA is worthy. Yazdi et al(2019) [29] evaluated the performance of ECAs by Data Envelopment Analysis. They considered some inputs and outputs, and then based on those, these ECAs are evaluated.…”

“…They considered some inputs and outputs, and then based on those, these ECAs are evaluated. The result demonstrated that among these ECAs, which one has efficiency and which one is inefficiency and how inefficiency ECAs can be changed to efficiency ECA.Karbassi Yazdi et al(2019) [29] suggested how can did credit risk in an uncertain environment. They did this model by Fuzzy Inference System (FIS).…”

“…As per our knowledge, there is a little evidence that analyzed their performance. The solely paper that can be found about ECAs performance is paper related to Yazdi et al( 2019) [29] which they used DEA and some inputs and outputs for evaluated some, not all ECAs in the specific time (a year). The contribution of this paper is to evaluate the performance of ECAs by data mining technique for clustering these ECAs during 2005 to 2020 for all ECAs.…”