Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier

Abstract

Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for banks and financial institution, to minimize their losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unavailable credit card transactions dataset for researchers. The most commonly techniques used fraud detection methods are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. But amongst all existing method, ensemble learning methods are identified as popular and common method, not because of its quite straightforward implementation, but also due to its exceptional predictive performance on practical problems. In this paper we trained various data mining techniques used in credit card fraud detection and evaluate each methodology based on certain design criteria. After several trial and comparisons; we introduced the bagging classifier based on decision three, as the best classifier to construct the fraud detection model. The performance evaluation is performed on real life credit card transactions dataset to demonstrate the benefit of the bagging ensemble algorithm.