Designing an Expert System for Credit Rating of Real Customers of Banks Using Fuzzy Neural Networks

Document Type: Research Paper

Authors

1 Department of Accounting, Varzeghan Branch, Islamic Azad University, Varzeghan, Iran

2 Department of Accounting, Varzeghan Branch, Islamic Azad University, Varzeghan, Iran.

10.22034/amfa.2019.577561.1128

Abstract

Currently, in Iran's banking system, non-repayment of facilities has become one of the biggest issues, and due to the lack of a proper system for proper allocation of facilities, they face a number of problems, including the problem of allocation of loans, the problem of failure to repay loans Of the central bank, or the amount of facilities increased from the amount of reimbursement. The solution of this problem is the credit rating of the customers, which is based on a model based on the theory of fuzzy sets for validation of real customers of the Maskan bank of the East Azer-baijan in Iran in 2016. In this research a structured model was obtained for deter-mination and categorization of input variables for application in the system by factorial analysis then a expert fuzzy system was modelled that consist of six steps. In the first step a fuzzy system is designed that its inputs are financial capacity, support, reliability, repayment record and its outputs is customer credit. In the second step input and outputs are partitioned, in the third step thee partitioned inputs and outputs are converted into fuzzy numbers. The fuzzy inference is compiled in step four. In step five the defuzzifier is conducted. Finally the designed model is tested in step six. These results indicate research model efficiency compared to bank credit measuring experts that they predicate applicants performance according their judgment and intuition.

Keywords

Main Subjects


[1]    Abiyev, R., Kaynak, O., Type 2 fuzzy neural structure for identification and control of time-varying plants. IEEE Transactions on Industrial Electronics, 2010, 57(12), P. 4147–4159. Doi:10.1109/TIE.2010.2043036.

[2]    Altman, E., Financial ratios discriminate analysis and the prediction of corporate Bankruptcy, The Journal of finance, 1968.

[3]    Arabmazar, A., Effective factors on bank customers credit risk, case study of Keshavarzi bank, Economic Researches Scientific Quarterly, 2006, 3(6), P.45-80 (In Persian)

[4]    Bazmara M., Jafari S., Pasand F., A Fuzzy expert system for goalkeeper quality recognition, International Journal of Computer Science Issues (IJCSI), 2012, 9(5-1), P.318-322

[5]    Bazmara, A., Sardar Donighi, S., Classification of bank customers for granting banking facility using fuzzy expert system based on rules extracted from the banking data, Journal of Basic and Applied Research, 2014, 3(12), P.379-384. Doi: 10.5815/ijisa.2014.11.04

[6]    Beaver, W.H., Financial ratios as predictors of failure, Journal of Accounting Research, 1966, 4, P.71-111.Doi: 10.2307/2490171.

[7]    Bekhet, H.A., Eletter S.F.K., Credit risk assessment model for Jordanian commercial banks: Neural scoring approach, Review of Development Finance, 2014, 4, P.20–28, Doi:10.1016/j.rdf.2014.03.002.

[8]    D. E. Allen, R. J. Powel., Credit risk measurement methodologist, 19th International Congress on Modelling and Simulation, Perth, Australia, 2011, P.12–16.

[9]    Deakin, E., A discriminate analysis of predictors of business failure, Journal of Accounting Research, 1972, 10(1)., P.167-169. Doi:10.2307/2490225.

[10]  Deakin, E., Rational economic behavior and lobbying on accounting issues. Evidence from the Oil and Gas Industry, The Accounting Review, 1989, 66(1). Doi:10.2307/2288398.

[11]  Durand, D., Risk element in consumer installment lending, National Bureau of Economic Research, New York, 1941.

[12]  Fisher, R., The use of multiple measurements in taxonomic problem, Annals of Eugenics, 1936.

[13]  Gönen, G. B., Gönen, M., Gürgen, F., Probabilistic and discriminative group-wise feature selection methods for credit risk analysis. Expert Systems with Applications, 2012, 39(14), P.11709-11717.

[14]  Izadikhah. M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P.27-41, Doi:10.22034/AMFA.20 18.540829.

[15]  Jamshidi, S., Customers credit measuring methods, Bank and Monterey research centre Central bank of Islamic republic of Iran, 2003, (In Persian).

[16]  Kalantari, N., Rahmatolah Mohammadi Pour, R., Seidi, A. Shiri, A., Azizkhani M., Fuzzy Goal Programming Model to Rolling Performance Based Budgeting by Productivity Approach (Case Study: Gas Refiner-ies in Iran. Advances in Mathematical Finance and Applications, 2018, 3(3), P. 95-107, Doi:10.22034/AM FA.2018.544952

[17]  Sabzevari, H., Soleymani, M., and Noorbakhsh, E., A Comparison between statistical and data mining methods for credit scoring in case of limited available data, 2012, (In Persian).

[18]  Sadat S.R., Gholamian, M.R., Shahanaghi, K., Combination of Feature Selection and Optimized Fuzzy Ap-riority Rules: The Case of Credit Scoring, The International Arab Journal of Information Technology, 2015, 12(2), P.138-145.

[19]  Sarlak, A., Johari.F., The analysis of the existence of the hypothesis of adverse selection on the relationship between off-balance sheet items and the bank's risk. Advances in mathematical finance & applications, 2016, 1(1), P.85-94, Doi:10.22034/AMFA.2016.526246

[20]    Thomas, L.C., A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 2000, 16(2), P. 149-172, Doi:10.1016/S0169-2070(00)00034-0

[21]  Oreski S., Oreski G., Genetic algorithm-based heuristic for feature selection in credit risk assessment, Expert Systems with Applications, Elsevier Ltd., 2014, 41(4), P.2052–2064. Doi:10.1016/j.eswa.2013.09.0 04.

[22]  Oreski S., Oreski D., Oreski G., Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment, Expert Systems with Applications, Elsevier Ltd, 2012, 39(16), P.12605–12617, Doi:10.1016/j.eswa.2012.05.023.

[23] Liu, Y., New Issues in Credit Scoring Applications, Work Report, Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Germany, 2001.

[24] Zhang, L., Hu, H., Zhang D., A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance, Financial Innovation 1, 2015, 1:14, Doi:10.1186/s40854-015-0014-5.