Paper Title
Credit Scoring using Aggregation: An Empirical Study

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