Financial fraud is one of the most critical challenges in digital banking systems. Even a small percentage of fraudulent transactions can lead to significant financial losses for banks and customers. In this project, I developed an end-to-end machine learning pipeline to detect fraudulent credit card transactions using historical transaction data. The workflow included: • Data preprocessing and feature engineering using Pandas and NumPy • Handling severe class imbalance using SMOTE and undersampling techniques • Exploratory data analysis and correlation analysis to identify fraud patterns • Training multiple machine learning models including Logistic Regression, Random Forest, and XGBoost • Hyperparameter tuning and cross-validation to optimize model performance • Model evaluation using Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix • Visualization of fraud detection insights using Matplotlib and Seaborn The final model achieved strong fraud detection performance while minimizing false positives, which is critical for real-world financial systems. This project demonstrates how machine learning can be applied to detect financial fraud and improve transaction security in banking and fintech systems.