Loan Customer Analysis System using Column-wise Segmentation of Behavioural Matrix (CSBM)

Authors

  • Mandal RK Department of Computer Science & Application University of North Bengal, India
  • NR Manna Department of Computer Science & Application University of North Bengal, India

Keywords:

Loan, Customer, ANN, Column-wise Segmentation, Perceptron, Behavioural Pattern

Abstract

In order to approve bank loans, modern day researchers and bankers are involved in different types of work related to analysis of the behaviour of the loan applicants. Customer data are collected to analyze their behaviour which may predict the possibility of repayment of the EMIs. In this paper, a new approach has been made to make this process fast. The data used to analyze customer behaviour are actually behavioural patterns. Artificial Neural Networks (ANNs) are very good tool to train a system for known patterns and which can later be used to identify unknown patterns. Two dimensional binary pattern matrixes are formed considering different behaviour of customers from different views. Matrixes are further segmented column-wise and each column is presented to Perceptron (ANN) in order to train the ANN for the known patterns. Later on, unknown patterns of customer behaviour can be presented to the net to reach to the decision to provide loan to a customer or not.

References

Rakesh Kumar Mandal and N R Manna, "Hand Written English Character Recognition using Column-wise Segmentation of Image Matrix (CSIM)", WSEAS Transactions on Computers, Volume 11, Issue 5, May 2012.

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Apash Roy and N R Manna,"Character Recognition using Competitive Neural Network with Multi-scale training", UGC Sponsor National Symposium on Emerging Trends In Computer Science (ETCS 2012) on 20-21 January 2012, pp 17-20.

Apash Roy and N R Manna, "Competitive Neural Network as applied for Character Recognition " - International Journal of advanced research in Computer science and Software Engineering, Volume 2, Issue 3, 2012, pp 06-10.

V Moonasar, Credit Risk Analysis using Artificial Intelligence: Evidence from a Leading South African Banking Institution. Available: www.academia.edu/502093/credit_risk_analysis_using_artificial_intelligence_evidence_from_a_leading_South_African_banking_institution.

Ifeyinwa Ajah, Chibueze Inyiama, Loan Fraud Detection And IT-Based Combat Strategies, Journal of Internet Banking and Commerce, 2011, Vol. 16 No. 2, pp 1-13. Avialable at: www.arraydev.com/commerce/JIBC/2011_08/Ajah.pdf

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Published

2015-02-28

How to Cite

[1]
R. K. Mandal and N. Manna, “Loan Customer Analysis System using Column-wise Segmentation of Behavioural Matrix (CSBM)”, Int. J. Comp. Sci. Eng., vol. 3, no. 1, pp. 18–22, Feb. 2015.

Issue

Vol. 3 No. 1 (2015): IJCSE Special Issue February Edition

Section

Research Article

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