Credit risk management in the banking system - A comparative approach of Data Envelopment Analysis and Neural Network and Logistic Regression

This research has been done with the aim of identification of effective factors which influence credit risk and designing model for estimating credit Rating of the companies which have borrowed from a commercial Bank in the one-year period by using Data Envelopment Analysis and neural network model and comparison of these two models . For this purpose the necessary sample data on financial and non-financial information of 146 companies (as random simple) was selected. In this research, 27 explanatory variables (include financial and non-financial variables) were obtained, by application of factor analysis and Delphi method for examination. Finally 8 variables which had significant effect on credit risk were selected and entered to DEA model. Efficiency of companies was calculated with these variables. Also variables as well as the input vector three-layer perceptron neural network models were added to the model .finally data was processes with logistic regression. Results from data envelopment analysis model and Neural network and Logistic regression in comparisons to the actual results obtained from neural network models to predict credit risk legal customers and credit rating suggest that neural network is more efficient than data envelopment analysis and logistic regression.