Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine

Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article was to estimate the credit risk of Mellat Bank's legal customers. In this study, the statistical information of 7330 real customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on personality, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. Comparing the results obtained from the prediction accuracy of different models, it was observed that the explanatory power of the support vector machine model and the use of the survival probability function was higher than that of the simple neural network model for the studied groups of real customers.