عنوان مقاله [English]
Evaluating the credit risk has been the most important factor in determining the customer's credit status. In the past, credit risk often determined by intuitive judgment that was inefficient compared to statistical and artificial intelligence methods which recently have been considered. Whereas the use of statistical methods requires specific data distribution, on the other hand use of artificial intelligence requires complex and costly calculations, and its obtained models are not interpretable. Hence, this paper attempts to use a new approach of applying multi criteria decision making models while reducing the computational complexity and lack of need for specific assumption for data to classify the credit customers. In this study C-TOPSIS approach that is based on the TOPSIS technique was used as a new approach of applying MADM for classifying the credit customers of bank. To assess the validity of C-TOPSIS approach, the performance of this method was compared to the performance of logistic regression model in credit status detection of Sina bank customers in the period 1388-1392. The results indicated that C-TOPSIS model has more accuracy (58.82 %) than logistic regression model (54.4%), in fact Type-I error and Type-II error of C-TOPSIS significantly decreased compared to other method
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