Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Subrata Saha, Sajjad Waheed

Subrata Saha and Sajjad Waheed. Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network. International Journal of Computer Applications 161(3):39-43, March 2017. BibTeX

	author = {Subrata Saha and Sajjad Waheed},
	title = {Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {3},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {39-43},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913170},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The aim of this paper is to present a model based on Multi-layer perceptron neural networks to recognize bad or good credit customers. Credit risk is one of the major problems in banking sector. Banks are faced with credit Risk while doing their tasks. Credit risk is the probability of non-repayment of bank loan granted to lenders. Decreasing Credit Risk, banks may perform better duties and responsibilities successfully for the economic growth of the country. This study will help for a banker to select a right borrower for investing bank fund and hereby may reduce non-performing loan. Artificial neural network is used for loan applicants' credit risk measurement and the calculations have been done by using SPSS and WEKA software. Number of samples was 101 and 12 variables were used to identify good customers from bad customers. The results showed that, History of borrower (Defaulter or non-defaulter), amount of loan, type of collateral security (physical assets or financial assets) and Value of collateral security had most important effect in identifying classification criteria of good and bad borrowers. The main contribution of this paper is specifying for credit rating of bank customers in Bangladesh’s banking sector.


  1. Angelini, E., di Tollo, G., &Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755.
  2. Matoussi, H., &Abdelmoula, A.k (2009). Using a Neural Network-Based Methodology for Credit–Risk Evaluation of a Tunisian Bank. Middle Eastern Finance and Economics, 4, 117-140.
  3. Vasconcelos, G. C., Adeodato, P. J. L., &Monteiro, D. S. M. P. (1999, July 20-22). A NeuralNetwork Based Solution for the Credit Risk Assessment Problem. Paper presented at the IV Brazilian Conference on Neural Networks, São José dos Campos.
  4. Abdou, H. A., &Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting, Finance and Management, 18(2-3), 59-88.
  5. Salehi, M., &Mansoury, A. (2011). An evaluation of Iranian banking system credit risk: Neural network and logistic regression approach. International Journal of the Physical Sciences, 6(25), 6082-6090.
  6. Eletter, S. F., &Yaseen, S. G. (2010). Applying Neural Networks for Loan Decisions in the Jordanian Commercial Banking System. International Journal of Computer Science and Network Security, 10(1), 209-214.
  7. Haykin SS. Neural networks: a comprehensive foundation. London: Prentice-Hall; 1999: 842.
  8. Ripley BD. Pattern recognition and neural networks. Cambridge: Cambridge university press; 1996: 403.
  9. Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon press; 1995: 482.
  10. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323:533-536.
  11. Gil D, Johnsson M, Garcia Chamizo JM, Paya AS, Fernandez DR. Application of artificial neural networks in the diagnosis of urological dysfunctions. Expert SystAppl 2009; 36:5754- 5760.
  12. Gil D, Girela JL, De Juan J, Gomez-Torres MJ, Johnsson M. Predicting seminal quality with artificial intelligence methods. Expert SystAppl 2012; 39:12564-12573.
  13. Pal M, University of Nottingham - GB. Factors Influencing the Accuracy of Remote Sensing Classification: A Comparative Study. University of Nottingham; 2002:
  14. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 2009; 11:10-18.
  15. WEKA Open Sources tools for Data Mining;
  16. IBM - Statistical analysis software package - SPSS Statistics;


Credit risk, neural network, multilayer perceptron, Bank credit Customers