Predictive Risk Categorization of Retail Bank Loans Using Data Mining Techniques

Authors(2) :-M Mubasher Hassan, Tabasum M

In the present highly competitive environment of the banking industry, reducing default and preventing NPA loans in retail banking is a major challenge. Data mining techniques are already popular in different banking sectors for mining important information to discover knowledge that can be used for marketing, analysis and predictive purpose for tremendous available data of already existing customers. We are using classification algorithms SVM, CART, j48 algorithm for predicting risk and then categorization of loan customer into any of three risk categories i.e ‘low risk’, ’medium risk’ and ‘high risk’. The risk category of customer will be used as a suggestive indicator for customization of the repayment schedule and follow up procedure required.

Authors and Affiliations

M Mubasher Hassan
Dept. of ITE BGSB University Rajouri, Jammu & Kashmir, India
Tabasum M
Education Department, Govt. of J&K, Jammu & Kashmir, India

Data Mining, Loan Prediction, Classification Algorithm, Credit Risk Assessment and J48 Algorithm

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Publication Details

Published in : Volume 4 | Issue 1 | March-April 2018
Date of Publication : 2018-04-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 302-306
Manuscript Number : CSEIT411850
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M Mubasher Hassan, Tabasum M, "Predictive Risk Categorization of Retail Bank Loans Using Data Mining Techniques", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.302-306, March-April-2018.
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