Egyptian Credit Risk Rating Model
Abstract—Credit risk rating is a method of measuring the credit worthiness in enterprises and banks by analyzing their historical data. Credit risk rating is one of the most important problems in finance. Most Egyptian commercial banks unable to determine and predict for credit risk rating and so far there is no accurate model in Egypt for determining and predicting for credit risk rating of these commercial banks. In this paper, the researchers propose a fuzzy logic based model that can be used to assist in determining and predicting for bank credit risk rating. Taking the rating scale of Moody's as an output for the proposed model. The proposed model is based on financial ratios used in Egyptian commercial banks i.e. profitability, debt-paying …show more content…
Furthermore, there is no accurate model in Egypt for determining and predicting for credit risk rating of these commercial banks.
In Egypt, application of machine learning techniques has been very limited in the context of economics and studies of finance. This study shows that incorporating machine learning techniques in the methodology for assessing credit rating of listed commercial banks would solve a number of current problems to achieve the following outcomes: (i) provision of a set of rating criteria; (ii) a proposed method of quantitative evaluation based on financial ratios; and (iii) a classification of "financial health" and the growth prospects among all listed commercial banks in Egypt.
In this paper, the researchers propose a fuzzy logic based model that can be used to assist in determining and predicting for bank credit risk rating. This paper focuses on commercial banks in Egypt that have suffered from few models for credit risk rating in recent years which led to lose finance in these banks. Taking the rating scale of Moody's as an output for the proposed model. This model could help the decision makers in the Egyptian commercial banks to the right decisions to determine the credit risk rating in these
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In symbols:
A⊂B□(⇔)f_A≤f_B (3)
Union: The union of two fuzzy sets A and B with respective membership functions f_A (x) and f_A (x) is a fuzzy set C, written as C=A∪B, whose membership function is related to those of A and B by:
f_C (x)=Max[f_A (x),f_B (x)] x∈X (4)
Intersection: The intersection of two fuzzy sets A and B with respective membership functions f_A (x) and f_A (x) is a fuzzy set C, written as C=A∩B, whose membership function is related to those of A and B by:
f_C (x)=Min[f_A (x),f_B (x)] x∈X (5) Fuzzy rules: A fuzzy rule is a conditional statement of the form IF A THEN B, where A and B are terms with a fuzzy meaning [9]. Related Work
In general, several approaches have been proposed in order to establish a model that is capable of determining and predicting for credit risk rating. For example, L. Yijun, C. Qiuru, L. Ye and Q. Jin [10] proposed a neural network model to make an effective analysis for corporation credit rating. H. A. Abdou [11] conducted a study to investigate the ability of genetic programming (GP) in the analysis of credit scoring models in Egyptian public sector banks. W. Hongxia, L. Xueqin and L. Yanhui [12] proposed a model based on fuzzy clustering and decision tree for
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