A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling

The existing fraud detection methods present limitations such as imbalanced data, incorrect identification of fraudulent cases, limited applicability to different scenarios, and difficulties processing data in real-time. This paper proposes an ensemble machine-learning model for detecting fraud in credit card transactions. It also integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbor (ENN) to address the problem of the imbalanced datasets. The experimental results show that our approach performs better than the existing methods. Therefore, it will establish an essential framework for the ongoing investigations in developing more robust and flexible systems for fraud detection.