Detection of Credit Card Fraud using a Hybrid Ensemble Model

Abstract

The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards. A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud. In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions. During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced. The oversampling method is used to balance both datasets. To overcome the problem of unbalanced data oversampling method is used. The model is trained to predict output results by combining random forest with Adaboost. The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

“…In study [12] the author examined the utilization of supervised and unsupervised methods to identify inconsistencies in financial transaction records. The research in [13] proposed a hybrid ensemble model to detect anomalies in credit card transactions. The research used adaboost, random forest, and logistic regression as classifiers, imbalanced dataset was addressed by oversampling method and removal of outliers.…”