Credit Scoring using Machine Learning Techniques
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10.5120/ijca2017912893 |
Sunil Bhatia, Pratik Sharma, Rohit Burman, Santosh Hazari and Rupali Hande. Credit Scoring using Machine Learning Techniques. International Journal of Computer Applications 161(11):1-4, March 2017. BibTeX
@article{10.5120/ijca2017912893,
author = {Sunil Bhatia and Pratik Sharma and Rohit Burman and Santosh Hazari and Rupali Hande},
title = {Credit Scoring using Machine Learning Techniques},
journal = {International Journal of Computer Applications},
issue_date = {March 2017},
volume = {161},
number = {11},
month = {Mar},
year = {2017},
issn = {0975-8887},
pages = {1-4},
numpages = {4},
url = {http://www.ijcaonline.org/archives/volume161/number11/27189-2017912893},
doi = {10.5120/ijca2017912893},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, 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.
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Keywords
Data Mining, Credit Scoring, Logistic Regression, LDA, XGBoost, Random Forest.