Financial fraud poses a significant threat to the digital economy, with credit card fraud being a prevalent challenge. This study evaluates the performance of Logistic Regression (LR) and Extreme Gradient Boosting (XG Boost) models in detecting fraudulent transactions using financial datasets. The study uses practical data from 284,807 transactions, but only 492 are fraudulent; the imbalanced class issue is solved using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that XG Boost with Random Search selection is better than Logistic Regression in all aspects. XG Boost yielded an accuracy of 99.96%, precision of 95.11%, recall of 79.61%, and F1 score of 86.61%, while for Logistic Regression, the corresponding percentages were 99.92%, 88.1%, 60.5%, and 71.7%. The AUC statistic of 0.98 for XG Boost against 0.97 for LR classified the model as having better discriminant power. The results show that XG Boost is more suitable for real-time fraud detection. However, computational limitations and explainability issues should be considered. For future work, it is suggested that semi-supervised and supervised learning approaches be investigated and work with larger datasets to improve fraud detection in financial systems.