Designing Credit Risk Early-Warning System for Individual and Corporate Customers of The Bank Using Multiple Logit Comparison Model and Survival Function

Document Type : Original Article

Authors

1 Associate Professor of Azad University, Tehran markaz branch, Tehran, Iran Islamic Azad University of Central Tehran Branch.(Modern Financial Risk Research Group)

2 Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran

3 Qom branch, Islamic Azad University, Qom, Iran

4 faculty of management and accounting, Islamic Azad university, Islam Shahr branch The Islamic Azad University of Eslamshahr.(Modern Financial Risk Research Group)

5 Finance and accounting, Faculty of Humanities, Qom Islamic Azad University, Qom, Iran

Abstract

This article aims to estimate the credit risk of individual and corporate customers of Iran's banking system. The estimation of credit risks of banks, financial institutions and insurance companies is not possible without an accurate credit scoring of the customers. Credit scoring or credit rating is a process in which the credit amount of individual and corporate customers of the financial-credit institution and banks is measured using the information provided by the customers. The process makes it possible to obtain a wider knowledge of the people's situation to repay the credit received and, or to measure the loan default probability. The statistical data of 399 individual customers and 780 corporate customers from 2011 to 2019 (7500 data) are used to design credit risk models in this study. Multiple Logit Regression, Survival function and Support Vector Machine (SVM) are used to design credit risk models. The results indicate that the selected factors have a significant impact on the customer default probability and credit risk calculation, based on personality, financial and economic characteristics. The Comparison of the results obtained from the accuracy of the forecast shows a higher explanatory power of the Support Vector Machine model and survival function than the Multiple Logit model for both groups of customers.

Keywords

References

Eskandari, Meysam Jafari and Rouhi, Milad
(2016), Credit Risk Management of Bank
Customers Using the Revised Decision Vector
Machine by Genetic Algorithm with Data Analysis
Approach, Quarterly Journal of Asset Management
and Financing, No. 1, Pp: 12-38.
2) Tehrani, Reza, Fallah Shams, Mir Feyz (2005),
Risk Credit Model Designing and Explaining in
the Iran Banking System, Journal of Social
Sciences and Humanities, Shiraz University, No.
43, Pp: 45-60.
3) Abdoh Tabrizi, Hossein, Raadivar, Meysam
(2009), Measuring and Managing of Market Risk,
Value at Risk Approach, Pishbord Publication, No.
5, Pp: 34-52.
4) Sham Al-Dini, Akbar (2010), Principles of Credit
Risk Management, Saderat Bank, No. 20, Pp: 84-
87.
5) Khodaverdi, Omid (2009), Credit Risk Scoring of
the Insured Customers Using Smart Methods
(Case Study: An Export Credit Institution), Master
Thesis, University of Tehran.
6) Khalili-Iraqi, Maryam (2005), Credit Risk
Management Using Decision Making Models,
Quarterly Journal of Economic Research, No. 16,
Pp: 183-212.
7) Abdoli, Ghahraman , Fard-Hariri, Alireza (2015),
Modeling the Credit Risk Assessment of Juridical
Customers of Refah Bank, Quarterly Journal of
Applied Theories of Economics, No. 1, Pp: 1-24.
8) Arab Mazar, Abbas, Rouyintan, Pooneh (2006),
Factors Affecting Credit Risk of the Bank
Customers, Case Study of Agricultural Bank,
Quarterly Journal of Economic Researches, No. 6,
Pp: 45-80.
9) Issazadeh, Saeid, Oryani, Bahareh (2010),
Juridical Customers' of the Banks' Rating based on
Credit Risk Using Data Envelopment Analysis: A
Case Study of Agricultural Bank Branches,
Quarterly Journal of Economic Researches and
Policies, No. 18 (55), Pp: 59-86.
10) Salahi, Mohammad (2011), Review and
Prioritization of the Factors Affect Bank
Customers' Credit Using AHP Method, Case Study
of Sina Bank, School of Management, Financial
Management, University of Tehran.
11) Fard-Hariri, Alireza (2008), Risk Modeling and
Credit Ranking of Bank Systems' Customers,
Master Thesis, Faculty of Economics, University
of Tehran.
12) Mousavi, Seyyed Reza, Gholipour, Elnaz (2009),
Credit Criteria Ranking of Bank Customers using
Delphi Approach, The First International
Conference on Bank Service Marketing.
13) Mirzaei, Hossein, Nazarian, Raafik and Bagheri,
Rana (2011), Investigating the Factors Affect
Juridical Customers of the Banks' Credit Risk
(Case Study: Melli Bank Branches, Tehran
Province), Quarterly Journal of Economic
Research, 16th Year, Pp: 67-98.
14) Mirghafouri, Seyyed Habibollah, Ashouri, Zohreh
(2015), Credit Risk Assessment of Bank
Customers, Quarterly Journal of Business
Management Research, Volume 7, No. 13, Pp:
147-166.

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