Credit Risk Modeling of Large-Scale Banks Bank Mellat Case Study

Document Type : Original Article

Authors
Sakineh Mohammadyari 1
Akbar akbareftekhari 2
Hamidreza kordlouie 3
Mirfaiz Fallah shams 4
Mohammad Hamed khanmohammadi 5
1 PhD Student in International Finance, Department of Management, Tehran University of Research Sciences, Islamic Azad University, Tehran, Iran.
2 Faculty Member, Assistant Professor, Financial Management, Mehr Alborz Institute of Higher Education, Iran
3 Faculty Member, Associate Professor, Financial Management Department, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran.
4 Faculty Member, Associate Professor, Financial Management Department, Islamic Azad University, Central Tehran Branch, Islamic Azad University, Tehran, Iran
5 Faculty Member, Associate Professor, Accounting Department, Damavand Branch, Islamic Azad University, Tehran, Iran
10.30495/jik.2025.77920.4607
Abstract
So far, many models in the category of statistical and experimental models and methods based on artificial intelligence; Regression models are presented for credit risk assessment. Examining these models shows that they all have a common feature, or in other words, they all missed an important point. A customer who has obtained a good credit rating may show a different credit behavior due to various personal and social reasons such as economic recession or embargo. Based on this, the need to model the credit risk of banks is very important. modeling based on linear regression, due to multiple regression assumptions; It mainly has high error; Therefore, in recent years, researches based on Bayesian approaches have been developed. Based on this, the main problem of the current research is credit risk modeling in large-scale banks (Bank Mellat). The time period of the current research is seasonal data from 1380 to 1401. In this research, 45 variables affecting credit risk were included in dynamic averaging (TVP-DMA), selective averaging (TVP-DMS), Bayesian averaging (BMA) and weighted average least squares (WALS) models. Among the mentioned models, the weighted average least squares model was determined as the most efficient model. Based on the results, 15 variables with the highest level of influence on credit risk were identified. Based on inflation, the most important factor affecting Bank Mellat's credit risk was identified.
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