Rating the Actual Customers of Banks based on Credit Risk using Multiple Criteria Decision Making and Artificial Intelligence Hyperbolic Regression

Document Type : Original Article

Authors

1 Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran. (Corresponding Author)

2 Ph.D. Student, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Assessment Management of Keshavarzi Bank in Mazandaran Province, Nashtaroud Branch in Tonekabon

Abstract

This study wants to investigate the rating of the actual customers of banks based on credit risk using multiple criteria decision making and artificial intelligence hyperbolic regression. This is an applied research. The statistical population of the study includes the credit customers of Agriculture Bank in west branches of Mazandaran province, Iran in 2012-2016. A total of 100 cases have been evaluated. AHP method has been used in the case of elites' comments on the prioritized seven key factors using the corresponding weighting matrix. To model the classes of creditworthy and non-creditworthy customers and to predict the appropriate model based on the evidence in the customer's credit file, artificial intelligence hyperbolic regression has been employed. Using AHP method, the rating include customer revenue, credit in the market, customers' job, the duration of the relationship with the bank, the type of collateral, the value of collateral, average account balance to facilitate the credit risk of the actual customers, respectively. Using artificial intelligence hyperbolic regression, prioritization is based on the amount of credit in the market, customer revenue, the value of collateral, the duration of the relationship with the bank, the type of collateral and customers' job.

Keywords

References

1)     Altman,E.I.(1968).Financial Ratio,  discriminate analysis and the prediction of Corporate Bankruptcy. The Journal of Finance.23 (14), 589-609.
2)     Arabmazar, Abbas, invulnerable, Pune. (2006). Factors affecting the credit risk of bank customers. The Agricultural Bank Case Study.
3)     Azevedo,Paulo,F.Reibero,Paulo.Rodrigues,Garbeilia.(2019). Credit portability and spreads: Evidence in the Brazilian market. Journal of Economics and Business. Available online 18 May 2019, 105844.
4)     Crohi. M,Galai.D,Mark.R.(2001).Risk Management, Comprehensive chapters on market,credit and operational risk.United States of America,McGraw-Hill.
5)     Glantz, Morton.(2004).managing Bank Risk.Academic Press.pp107-108.
6)     Jalili, Mohammad. (2007).Iran-based credit evaluation system of behavioral change Iran's banking system. the consulting firm's rankings.2(12),22-38.
7)     Jamshidi, Saeid. (2004).Customers validation practices. Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran.
8)     Hashemkhany, Meysam. (2010).How banks and financial institutions to manage the risk of non-performing loans?. Economic Research Journal.5(2),45-67.
9)     Karami, Mustafa. (2004). A model of assessment and customer credit risk to banking facilities. Journal of Banking and Economics.14(3),25-38.
10)  Khalili Iraqi, Maryam. (2005).Credit Risk Management Applying decision. Economic Bulletin,24(11),34-52.
11)  Kalantary, Khalil. (2003).Processing and analysis of data on socio-economic research software using SPSS.
12)  Mansouri, Ali. (2003).Define and design a mathematical model to estimate credit Banking (the classic models and neural network approach). PhD thesis, Faculty of Humanities, Tarbiat Modarres University.
13)  Malekpour gol sefidnd, Soheil. (2006). Risk management and credit risk in banks. Journal of Banking and Economics.78(12),15-33.
14)  Mohtashami, Toktam; salami, Habibullah. (2007). Factors distinguishing low-risk corporate clients of the bank's risk customers: A Case Study of the Agricultural Bank; Case Study Agricultural Bank. The sixth conference of Iran's agricultural economy.
15)  Mousavi, Seyed Reza. Gholipour, Elnaz. (2009). Rating Criteria validation Bank customers with Delphi approach. First International Conference on Marketing of banking services.
16)  Nili, Masoud; Sabzevari, Hasan. (2008).Credit rating logit model to estimate and compare with hierarchical analysis (AHP). Journal of Banking and Economics.14(4),202-238.
17)  Rosen,Dan.Saunders,David.(2009). Risk factor contributions in portfolio credit risk models.Journal of Banking & Finance.34(2),336-349.
18)  Shamspour,Anastasiya.Weil,Laurent.(2019). Does bank efficiency influence the cost of credit? Journal of Banking & Finance.105, 62-73.
19)  Shayan Arani shahin. (2001). Islamic banking risk management and non-governmental. Proceedings of the twelfth conference of Islamic banking.
20)  Tehrani, Reza. Fallah Shams, Mirfeizi. (2005). Define and design a model credit risk in the banking system. Journal of Humanities and Social Sciences, Shiraz University, Twenty-second period, the second number.
21)  Wang, i.Zhao,Ruiqing.Chen,Huiru.(2018). Optimal credit period and green consumption policies with cash-credit payments under asymmetric information. Journal of Cleaner Production.205(20),706-720.
22)  Yang Lui,(2001).New Issues In Credit Scoring Applications. George August,University Gottingen, Institue For Wirtschafts informatics.p3.
23)  Zekavat, Morteza, Mohammadian, Mehdi.(2006). Customers of banks using internal ratings models legit  regression. Sharif University of Technology Research Center.
 
International Journal of Finance & Managerial Accounting
Volume 4, Issue 16
January 2020
Pages 51-63

Files

Share

How to cite

Statistics