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

Motedayen, Nasrin; Nazarian, Rafik; Damankeshideh, Marjan; Seifi pour, Roya

doi:10.52547/jemr.12.45.199

Volume 12, Issue 45 (11-2021) jemr 2021, 12(45): 199-230 | Back to browse issues page

‎ 10.52547/jemr.12.45.199

‎ 20.1001.1.22286454.1400.12.45.6.5

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Motedayen N, Nazarian R, Damankeshideh M, Seifi pour R. Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine. jemr 2021; 12 (45) : 6
URL: http://jemr.khu.ac.ir/article-1-2256-en.html

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

Nasrin Motedayen¹

, Rafik Nazarian ^*

², Marjan Damankeshideh¹

, Roya Seifi pour¹

1- Islamic Azad University
2- Islamic Azad University , r-nazarian@yahoo.com

Abstract: (2701 Views)

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.

Article number: 6

Keywords: Credit Risk, Credit Rating, Financial Ratios, Neural Network Model, Support Vector Machine.

Full-Text [PDF 512 kb] (1627 Downloads)

Type of Study: Applicable | Subject: رشد و توسعه و سیاست های کلان
Received: 2022/06/1 | Accepted: 2022/07/24 | Published: 2022/11/6

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