An Efficient Method for Credit Card Fraud Detection using Machine Learning

In an era dominated by digital transactions, credit card fraud has become a pervasive threat, costing businesses billions annually. Traditional rule-based systems for fraud detection lack adaptability and struggle to keep pace with evolving fraudulent techniques. Consequently, there's a growing reliance on machine learning (ML) techniques to bolster fraud detection efforts. However, the imbalanced nature of fraud datasets, coupled with the dynamic and constantly evolving nature of fraudulent activities, presents significant challenges. This paper presents a comprehensive investigation into the efficacy of various supervised ML algorithms for credit card fraud detection. Specifically, we explore the performance of support vector machine (SVM), extreme Gradient Boosting (XGBOOST), and Adaptive Boosting (Adaboost).