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

Credit card use has grown to become more widespread thus leading to higher incident rates of fraudulent transactions. The current standard detection systems encounter problems with both high numbers of wrong alarms and slow reactions that have negative effects on institution security protocols and user confidence levels. The research measures the performance of Logistic Regression, Decision Tree, Random Forest, and XGBoost in identifying fraud in real-time conditions. The evaluation of the models focuses on their accuracy rates and precision levels and recall measures and F1-score along with their computational speed and throughput. A hybrid ensemble model serves as a proposal to achieve performance- speed balance which makes it fit for deployment in real-world financial applications. The analysis studies the challenges between complex models and easy interpretation of financial systems and reveals their necessity for making transparent decisions. All results show different models lead at specific points since assessments vary with context which underlines the requirement for operation-dependent optimization. Research in development will employ adaptive learning to detect new types of evolving fraudulent patterns.