Paper Title :Credit Scoring using Aggregation: An Empirical Study
Author :Vishesh Jindal, Medant Bansal, Shubham Gulati
Article Citation :Vishesh Jindal ,Medant Bansal ,Shubham Gulati ,
(2017 ) " Credit Scoring using Aggregation: An Empirical Study " ,
International Journal of Management and Applied Science (IJMAS) ,
pp. 93-96,
Volume-3,Issue-12
Abstract : When it comes to the area of finance, Credit Scoring has been regarded as one of the most important appraisal
tools of institutions in the last few decades. A number of statistical models are being used for credit scoring using a lot many
prediction techniques. In this paper, we propose an ensemble technique that aggregates a number of existing models such as
Random Forests, Support Vector Machine (SVM), Logistic Regression and Artificial Neural Nets, in order to better predict
credit scores and obtain a much higher accuracy rate than these individual techniques. A comparative analysis of various
traditional models, as well as the aggregated model is also provided.
Keywords - Credit scoring, Random Forests, SVM, Logistic Regression, Neural networks, Bagging
Type : Research paper
Published : Volume-3,Issue-12
DOIONLINE NO - IJMAS-IRAJ-DOIONLINE-10590
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Copyright: © Institute of Research and Journals
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Published on 2018-02-19 |
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