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Reseach Article

Credit Scoring using Machine Learning Techniques

by Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 11
Year of Publication: 2017
Authors: Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande
10.5120/ijca2017912893

Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande . Credit Scoring using Machine Learning Techniques. International Journal of Computer Applications. 161, 11 ( Mar 2017), 1-4. DOI=10.5120/ijca2017912893

@article{ 10.5120/ijca2017912893,
author = { Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari, Rupali Hande },
title = { Credit Scoring using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number11/27189-2017912893/ },
doi = { 10.5120/ijca2017912893 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:09.954964+05:30
%A Sunil Bhatia
%A Pratik Sharma
%A Rohit Burman
%A Santosh Hazari
%A Rupali Hande
%T Credit Scoring using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 11
%P 1-4
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Lenders such as banks and credit card companies while reviewing a client’s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized by using various statistical models. In this study a wide range of statistical methods in machine learning have been applied, though the datasets available to the public is limited due to confidentiality concerns. Problems particular to the context of credit scoring are examined and the statistical methods are reviewed.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Credit Scoring Logistic Regression LDA XGBoost Random Forest.