DOIONLINE 
DOIONLINE Publish In |
International Journal of Management and Applied Science (IJMAS)-IJMAS |
 Journal Home Volume Issue |
||||||||
Issue |
Volume-3,Issue-12 ( Dec, 2017 ) | |||||||||
Paper Title |
Credit Scoring using Aggregation: An Empirical Study | |||||||||
Author Name |
Vishesh Jindal, Medant Bansal, Shubham Gulati | |||||||||
Affilition |
Software Engineer, Grofers, Gurugram, India Analyst, Goldman Sachs Services Pvt Ltd, Bengaluru, India Software Engineer, Zomato, Gurugram, India | |||||||||
Pages |
93-96 | |||||||||
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 | |||||||||
| View Paper | ||||||||||
