Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank
Автор: Md.Mahbubur Rahman, Samsuddin Ahmed, Md. Hossain Shuvo
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 8 vol.6, 2014 года.
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Bank plays the central role for the economic development world-wide. The failure and success of the banking sector depends upon the ability to proper evaluation of credit risk. Credit risk evaluation of any potential credit application has remained a challenge for banks all over the world till today. Artificial neural network plays a tremendous role in the field of finance for making critical, enigmatic and sensitive decisions those are sometimes impossible for human being. Like other critical decision in the finance, the decision of sanctioning loan to the customer is also an enigmatic problem. The objective of this paper is to design such a Neural Network that can facilitate loan officers to make correct decision for providing loan to the proper client. This paper checks the applicability of one of the new integrated model with nearest neighbor classifier on a sample data taken from a Bangladeshi Bank named Brac Bank. The Neural network will consider several factors of the client of the bank and make the loan officer informed about client’s eligibility of getting a loan. Several effective methods of neural network can be used for making this bank decision such as back propagation learning, regression model, gradient descent algorithm, nearest neighbor classifier etc.
Credit Evaluation, Decision Process, Backpropagation, Nearest Neighbor Rule, Gradient Descent Algorithm
Короткий адрес: https://sciup.org/15010593
IDR: 15010593
Список литературы Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank
- Daniel Sabet and Ahmed S. Ishtiaque,”Understanding The Hallmark-Sonali Bank Loan Scandal , pp.2”, “University of Liberal Arts of Bangladesh”, January 2013.
- Meliha Handzic, Felix Tjandrawibawa and Julia Yeo,” How Neural Networks Can Help Loan Officers to Make Better Informed Application Decisions, pp.2”,” The University of New South Wales, Sydney, Australia”, ”June 2003”.
- C. N. Dragotă,” The Prediction of Bankruptcy Using Backpropagation Algorithm for “IO” Model Analysis, pp.11”,” Babeş – Bolyai” University, Cluj – NapocaRomania”,”January 2007”,”section 2”.
- Kevin Pang,” Self-organizing Maps”,” Neural Networks”,”Fall-2003”,.
- Meliha Handzic, Felix Tjandrawibawa and Julia Yeo”, How Neural Networks Can Help Loan Officers to Make Better Informed Application Decisions”,” The University of New South Wales, Sydney, Australia, pp.3”,”June-2003”.
- Charles Elkan” Nearest Neighbor Classification, p.p. 3”,elkan@cs.ucsd.edu”,” January 11, 2011.
- Bishop, C. M. 1995. Neural Networks for PatternRecognition. Oxford University Press, Oxford, U.K.
- Desai, V. S., J. N. Crook, G. A. Overstreet Jr. 1996.comparison of neural networks and linear scoring models in the credit union environment. Eur. J. Oper. Res. 95(1).
- Nauck, D. 2000. Data analysis withneur o-fuzzymethods. Habilitation thesis, University of Magdeburg, Germany.
- Capon, N. 1982. Credit scoring systems: A critical analysis. J. Marketing 46 82–91.
- West, D. 2000. Neural network credit scoring models. Comput. Oper. Res. 27 1131–1152.
- Gately E. J., Neural Networks for Financial Forecasting, WIG-Press, Warszawa, 1999 (in Polish).
- Rahimian E., Singh S., Thammachote T., Virmani R, "Bankruptcy Prediction by Neural Network",Probus Publishing Company, Chicago- London, 1993, pp. 159 – 176.
- M. Handzic, F. Tjandrawibawa, and J. Yeo, “How/neuralnetworks can help loan officers to make better informed applications decisions,” in Proc. 2003 Informing Science+ IT Education Conference 2003, pp. 97-108.
- J. E. Boritz and D. B. Kennedy, “Effectiveness of neural Network Types for prediction of business failure,” Expert Systems with Applications, vol. 9, no. 4, pp. 503-512, 1995..
- M. Bensic, N. Sarlija, and M. Zekic-Susac, “Modeling small-business credit scoring by using logistic regression, neural networks and decision trees,” international Journal of Intelligent Systems In Accounting, Finance And Management, vol. 13, no. 3, pp.133-150, July2005.
- J. Zurada and M, Zurada., “How Secure Are “Good loans”: validating loan-granting decisions and predicting default rates on consumer loans,” The review of business information systems, vol. 6, no.3, pp. 65-83, 2002.
- H. G. Nguyen, “Using neural network in predicting corporate failure,” Journal of Social Sciences, vol.1, no. 4, pp.199-202, 2005.
- C. L. Huang, M. C. Chen, and C. J. Wang, “Credit scoring with data mining approach based on support vector machines,” Expert systems with applications, vol. 33, no.4, pp. 847-856, November 2007.
- Y. W. Chen and C. J. Lin, "Combining SVMs with various feature selection strategies,” in Feature extraction, foundations and applications, New York: Springer, 2005, pp.319-328.
- I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, pp. 1157-1182, 2003.
- Y. Q. Wang, “Building credit scoring systems based on support-based support vector machine,” in Proc. Fourth international conference on natural computation October 2008, pp. 323-327.
- Bart Baesens,Rudy Setiono,Christophe Mues and Jan Vanthienen,"Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation", Management Science , Vol. 49, No. 3, March2003 pp. 312–329.