Manual reviews and rule-based systems, along with data mining techniques such as clustering and classification algorithms, are crucial for identifying credit card fraud as they help identify fraudulent transactions. Despite challenges in gathering training data, more data has recently become available; however, a comprehensive comparison of current machine learning approaches has yet to be conducted. Algorithms like XGBoost, AdaBoost, and Gradient Boosting Machine frequently outperform older methods. This study compares boosting algorithms to traditional approaches using three different credit card transaction datasets: synthetic, balanced with 50% fraudulent transactions, and highly unbalanced with only 0.17% fraudulent transactions. The genuine transaction datasets contained 28 anonymized parameters, including time and location. Each method was evaluated using the F1 score, accuracy, precision, and recall. This study offers recommendations on which algorithms to use in real-world scenarios, providing valuable insights for future research and practical applications in credit card fraud detection.