A Comparative Study of Machine Learning Algorithms for Credit Card Fraud Detection

Credit card fraud refers to the physical loss of credit card or loss of sensitive credit card information. Many machine learning algorithms can be used for detection. This research shows several algorithms that can be used for classifying transactions as fraud or genuine ones. Credit Card Fraud Detection dataset was used in the research. Because the dataset was highly imbalanced, to solve the issue of class imbalance, we re-sampled the dataset using the Synthetic Minority over-sampling Technique (SMOTE). This framework was evaluated using several machine learning (ML) methods, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), and Adaptive Boosting (AdaBoost). The models were evaluated using the accuracy, the recall, and the precision. The proposed model can be used for the detection of other irregularities.