Fraud Detection With a New Composite Bagging Model Using Classifier Algorithms

In this paper, an innovative fraud detection model built upon existing data mining and fraud detection methods has been proposed.Here a bagging model has been applied and has been compared with other methods such as Logistic Regression, Naïve Bayes and Decision tree (DD). We use these methods as basic classifiers and make a bagging model according to them. A variety of measures is used for measuring and evaluating the efficiency and performance of each classifier and then all of them with the proposedmodel. This study is based on real world dataset which has been divided into 4 smaller datasets with different fraudulent transaction rates. The proposed bagging model has shown higher performance compared to other mentioned models regarding almost all measures. The introduced model is using a virtual binary dataset which has been derived from the real life dataset.

Keywords:

Bagging model , Fraud detection , Bagging , Naïve Bayes , Logistic Regression , Decision Tree ,