Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank

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Md.Mahbubur Rahman 1,* Samsuddin Ahmed 1 Md. Hossain Shuvo 1

1. Dept. of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

* Corresponding author.


Received: 24 Aug. 2013 / Revised: 6 Jan. 2014 / Accepted: 26 Mar. 2014 / Published: 8 Jul. 2014

Index Terms

Credit Evaluation, Decision Process, Backpropagation, Nearest Neighbor Rule, Gradient Descent Algorithm


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.

Cite This Paper

Md.Mahbubur Rahman, Samsuddin Ahmed, Md. Hossain Shuvo, "Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.8, pp.60-68, 2014. DOI:10.5815/ijisa.2014.08.07


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