Designing Credit Risk Early-warning System for Individual and Corporate Customers of the Banks using Neural Network Models, Survival Probability Function and Support Vector Machine

Subject Areas : Management

Roya Derakhshani ¹ , Mirfeiz Fallah ² , hosein jahangirnia ³ , Reza Gholami jamkarani ⁴ , Hamidreza kordlouie ⁵

1 - Department of Finance and Accounting, Qom Branch, Islamic Azad University, Qom, Iran
2 - Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran and a member of the New Financial Risks Research Group
3 - Department of Finance and Accounting, Qom Branch, Islamic Azad University, Qom, Iran
4 - Department of Finance and Accounting, Qom Branch, Islamic Azad University, Qom, Iran
5 - Department of Financial Management, Islamshahr Branch, Islamic Azad University, Tehran, Iran and a member of the New Financial Risks Research Group

Received: 2022-12-12 Accepted : 2023-01-01 Published : 2023-09-23

Keywords: Support vector machine, Credit Risk, Financial ratios, Credit rating, Neural Network Model,

Abstract :

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 is to estimate the credit risk of individual and corporate customers. In this study, the statistical information of 400 individual customers and7500 corporate 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 their personal, 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. The comparison of the results of the prediction accuracy shows the higher explanatory power of the support vector machine model and the use of the survival probability function than the simple neural network model for both groups of customers.