Detection of Credit Card Fraud using a Hybrid Ensemble Model

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