International Journal of Management and Applied Science (IJMAS)
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Volume-4,Issue-9  ( Sep, 2018 )
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Statistics report
Dec. 2018
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 55
Paper Published : 3411
No. of Authors : 7033
  Journal Paper




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

Indexed : Google Scholar


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