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Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM)

Rakesh Kumar Mandal1

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-10 , Page no. 41-43, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i10.4143

Online published on Oct 31, 2018

Copyright © Rakesh Kumar Mandal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Rakesh Kumar Mandal, “Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM),” International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.41-43, 2018.

MLA Style Citation: Rakesh Kumar Mandal "Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM)." International Journal of Computer Sciences and Engineering 6.10 (2018): 41-43.

APA Style Citation: Rakesh Kumar Mandal, (2018). Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM). International Journal of Computer Sciences and Engineering, 6(10), 41-43.

BibTex Style Citation:
@article{Mandal_2018,
author = {Rakesh Kumar Mandal},
title = {Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM)},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2018},
volume = {6},
Issue = {10},
month = {10},
year = {2018},
issn = {2347-2693},
pages = {41-43},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2978},
doi = {https://doi.org/10.26438/ijcse/v6i10.4143}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i10.4143}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2978
TI - Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM)
T2 - International Journal of Computer Sciences and Engineering
AU - Rakesh Kumar Mandal
PY - 2018
DA - 2018/10/31
PB - IJCSE, Indore, INDIA
SP - 41-43
IS - 10
VL - 6
SN - 2347-2693
ER -

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Abstract

Different types of studies are going on among researchers and different approaches are adopted by the bankers to analyze the behavior of the loan applicants to approve those loans. Bankers collect customer data to analyze their behavior in order to predict the possibility of recovery of the amount. Domain experts can think about a new approach to make this process fast. The data used to analyze customer behavior are actually patterns. And Artificial Neural Networks (ANN) are very good tool to train a system for known patterns and later can be used to identify unknown patterns. In this paper a two dimensional binary pattern matrix is formed on the basis of some questionnaires to identify different customer behavior. The matrix is further segmented row-wise and each row is presented to perceptron for training purpose of the ANN, which is used to complete the process of loan approval.

Key-Words / Index Term

ANN, Row-wise Segmentation, Perceptron, Behavioral Pattern of customers

References

[1] Rakesh Kumar Mandal and N R Manna, “Loan Customer Analysis System using Column-wise Segmentation of Behavioural Matrix (CSBM)”, International Journal of Computer Sciences and Engineering, Volume-3, Special Issue-1 E-ISSN: 2347-2693, pp 18-22, February, 2015. Available Online: http://www.ijcseonline.org/pdf_spl_paper_view.php?paper_id=3&PID%203.pdf
[2] G.N. Swamy, G. Vijay Kumar, “Neural Networks”, Scitech, India, 2007.
[3] L. Fausett, “Fundamentals of Neural Networks, Architectures, Algorithms and Applications”, Pearson Education, India, 2009.
[4] 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.
[5] 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.
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