Bank Customer Credit Scoring by Using Fuzzy Expert System

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Ali Bazmara 1,* Soheila Sardar Donighi 2

1. Department of Management and Economics, Science and Research Branch Islamic Azad Universities, Tehran, Iran

2. Department of Management and Social Science, Islamic Azad University North Tehran Branch, Tehran, Iran

* Corresponding author.


Received: 17 Feb. 2014 / Revised: 21 May 2014 / Accepted: 10 Jul. 2014 / Published: 8 Oct. 2014

Index Terms

Credit Scoring, Bank Customer, Fuzzy Expert System


Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts.
For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

Cite This Paper

Ali Bazmara, Soheila Sardar Donighi, "Bank Customer Credit Scoring by Using Fuzzy Expert System", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.11, pp.29-35, 2014. DOI:10.5815/ijisa.2014.11.04


[1]T. S. Lee, C. C. Chiu, Y. C. Chou and C. J. Lu, "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines". Computational Statistics & Data Analysis, vol. 50, no. 4, pp. 1113-1130, 2006.

[2]S. Moro, R. Laureano, P. Cortez, P. Novais, J. Machado, C. Analide and A. Abelha, "Using data mining for bank direct marketing: An application of the CRISP-DM methodology", Proc. Eur. Simul. Model. Conf., pp. 117 -121, 2011.

[3]L.C Thomas, "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers", International Journal of Forecasting, vol.16, no. 2, pp. 149-172, 2000; 

[4]T. P. Hong and C. Y.Lee, "Induction of fuzzy rules and membership functions from training examples", Fuzzy Sets and Systems, vol. 84, no. 1, pp. 33-47, 1996.

[5]W. Siler, and J. J. Buckley, Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons, 2005.

[6]L.H. Chen and T.W. Chiou, "A fuzzy credit-rating approach for commercial loans: a Taiwan case", Omega, vol. 27, no. 4, pp. 407-419 , 1999.

[7]V.Gestel, B.Baesens, I.J. Garcia and P. Van Dijcke, "A support vector machine approach to credit scoring, Forum Financier-Revue Bancaire Et Financiaire Bank En Financiewezen", Citeseer, pp.73-82, 2003.

[8]M.C. Chen, S.H. Huang, "Credit scoring and rejected instances reassigning through evolutionary computation techniques", Expert Systems with Applications, vol 24, no. 4, pp. 433-441, 2003.

[9]N. C. Hsieh, "An integrated data mining and behavioural scoring model for analyzing bank customers". Expert systems with applications, vol. 27, no. 4, pp. 623-633, 2004.

[10]A. H. Abdou, "Genetic programming for credit scoring: the case of Egyptian public sector banks". Expert Systems with Applications, vol 36, pp. 11402-11417, 2009.

[11]A. Lahsasna, R.N. Ainon and T.Y. Wah, "Credit Scoring Models Using Soft Computing Methods: A Survey", Int. Arab J. Inf. Technol. vol. 7, no.2, pp. 115-123, 2010.

[12]F.L. Chen, F.C. Li, "Combination of feature selection approaches with SVM in credit scoring", Expert Systems ,ith Applications, vol. 37, no. 7, pp. 4902-4909, 2010.

[13]A.M. Madani, Y. Madani, M. EbrahimZadeh,G.M. Shahmorad, "Modeling credit Rating for bank of Eghtesade Novin in Iran". Journal of basic and applied scientific research, vol. 2, no. 5, pp. 4423- 4432, 2012.

[14]G. Wang, J. Ma, L. Huang and K. Xu, "Two credit scoring models based on dual strategy ensemble trees". Knowledge-Based Systems, vol. 26, pp. 61-68, 2012.

[15] M. Dastoori, S. Mansouri, "Credit Scoring Model for Iranian Banking Customers and Forecasting Creditworthiness of Borrowers", International Business Research, vol. 6, no. 10, 2013. 

[16]E. Kambal, I. Osman, M. Taha, N. Mohammed and S. Mohammed, "Credit scoring using data mining techniques with particular reference to Sudanese banks", In Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on, August 2013, pp. 378-383.

[17]A. Bazmara, S. Sardar Donighi, "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, vol. 3, no. 12, pp. 379-384, 2013.

[18] L. A. Zadeh, "Fuzzy sets", Information Control, vol. 8, no. 3, pp. 338-353, 1965.

[19]C.S. Lee, M.H. Wang, "A fuzzy expert system for diabetes decision support application", Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 41, no. 1, pp.139-153, 2011.

[20]D. M. W. Powers, "Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation". Journal of Machine Learning Technologies; vol. 2, no.1. pp. 37-63, 2011.

[21]L. Lam and C.Y. Suen, "Application of majority voting to pattern recognition: an analysis of its behavior and performance". Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 27, no. 5, pp. 553-568, 1997.