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