# A Credit Rating Model in a Fuzzy Inference System Environment
## 1 Introduction  
Due to the strong expansion of international trade, many countries have established export credit agencies (ECAs) to protect exporters from bankruptcy due to political and commercial risks. Their agents evaluate foreign buyers and determine whether to grant credit to these exporters to protect them from risks. The agents evaluate the buyers based on their countries' sovereign credit. In this paper, we use a Fuzzy Inference System (FIS) to evaluate exporters' credit in an uncertain environment. For each contract, the sellers need to know about the financial situation of the buyers. This study helps them to evaluate the ability of buyers to repay their debt, and to determine the probability of default. There are two types of credit ratings: (1) sovereign credit rating and (2) corporate credit rating. To ascertain the risk level of the buyers' investment, we evaluate them as well as their government. Credit risk can be analyzed using various financial tools, which are affected by political, social, and economic factors. Decision-making is an essential function of any enterprise. It is very difficult for decision makers (DMs) to utilize quantitative variables since many evaluation attributes are vague. For this type of uncertain environment, linguistic and verbal scales can be helpful in making an appropriate decision. Thus, the fuzzy set theory of linguistic variables can express the preferences of decision makers in an uncertain environment. Fuzzy logic and fuzzy set theory, introduced independently by [PERSON] [1] and [PERSON], have inspired many scholars over the decades. Since then, fuzzy models have found wide areas of application, including various economically important areas. For instance, in [2], an adaptive neuro-fuzzy inference system was used for selecting vehicle routes under uncertainty conditions. In [3], a similar type of model was used for determining economic order quantities (such as for procurement or production planning). Based on the Boston Consulting Group (BCG) portfolio matrix, a neuro-fuzzy approach is elaborated in [4] for analyzing human resources. Further application examples include the multiobjective route planning for the transport of hazardous material [5] or location planning for city logistics [6]. In addition, many studies have been published on using fuzzy modelling in the context of buyer evaluation and credit scoring, including those by [PERSON] [7] who investigated loan defaults. Due to financial crises, many financial institutes aim for accurate credit scoring models. [PERSON] and [PERSON] [8] utilized a modified analytic network process (ANP) and fuzzy inference system to establish a risk assessment model for third-party e-procurement systems. As part of a modified ANP, decision makers are encouraged to express their preferences verbally rather than via a numerical rating system. [PERSON] et al. [9] used an adaptive neural fuzzy inference system to create inputs, outputs, membership functions, and fuzzy rules. The results indicated that these sets of constraints lead to similar constraint categories with output fuzzy trained systems. [PERSON] et al. [10] used the FIS method to map the model. They found that a fuzzy inference system could be helpful in this type of research. The results showed that two factors were particularly effective for mapping via FIS. In the first step, FIS was used to weight factors. In the next step, the factors were integrated using FIS, and the final step revealed the results of exploratory boreholes. [PERSON] and [PERSON] [11] tested a model to predict stock prices by using the Self-Evolving Recurrent Neuro-Fuzzy Inference System (SERNFIS) and modified differential harmony search. They used stock market time series data utilizing diverse time frames. The Takagi-Sugeno-Kang (TSK) model and fuzzy IF-THEN rules were also used.