Operation Ability Ratio Analysis

Operation ability ratio: This section gives samples of the operation ability ratio rules of the fuzzy inference engine that are presented in Table 15. Operation ability ratio is based on calculating the following indicator.

TAT = Sales or Revenues / Total Assets

TABLE 15
Operation Ability Ratio Rules Samples

Rule # Fuzzy Rule
1 IF TAT is low
THEN Operation Ability is low
2 IF TAT is medium
THEN Operation Ability is medium
3 IF TAT is high
THEN Operation Ability is high

Liquidity ratio: This section gives samples of the Liquidity ratio rules of the fuzzy inference engine which are presented in Table 16. Liquidity ratio is based on calculating the following indicators:

SA = Securities

It is also used to input the bank’s data and receive the output. Defuzzification Process: A task of defuzzification is to convert a fuzzy output of the inference engine to crisp output of the system. The input for the defuzzification process is the aggregate output fuzzy set and the output is a single number. There are several defuzzification methods. Each provides a means to choose a single output based on the implied fuzzy sets. Commonly used defuzzifying methods are:

The mean of maximum method. The maximizing decision. The center of gravity method. [23]

In this paper, the center of gravity method is used as a defuzzification strategy. The Algorithm of the proposed model
This section presents an algorithm of the proposed model for determining and predicting the credit risk rating for Egyptian commercial banks. Figure 12 shows the flow chart of the proposed model.

Login into the system. Input the values of indicators for each financial ratio used in the credit risk rating assessment. Determine the membership function numerical range for each indicator linguistic value. If new bank {
Input indicators values and data of the bank

This paper also provides a set of financial indicators which can be used in the assessment of the credit risk rating. By using the proposed model, decision makers will be able to determine the class of credit risk rating in commercial banks, and receive remedial advices for solving the bank financial problems and enhancing the financial indicators values. Fuzzy logic technique is one of the most important machine learning techniques used to predict credit risk rating in commercial banks. The results from this study showed that fuzzy logic is scalable, reliable, stable, and different from classical methods and based on natural language. It is recommended as a future work to integrate other machine learning techniques such as neural networks with the proposed model in order to enhance the accuracy of the model