Fraud Detection in Financial Transactions Using Ensemble Machine Learning Models

Financial fraud has been identified as a critical challenge in the banking and e-commerce sectors, necessitating the need for accurate and efficient detection systems. Therefore, this study proposes the adoption of an XGBoost-based machine learning model for credit card fraud detection by leveraging on publicly available transactional datasets. Preprocessing steps, including normalization of numerical features and Principal Component Analysis (PCA) on anonymized components were further applied in order to enhance model learning and reduce dimensionality, while class imbalance was addressed using scale_pos_weight and the model was trained and evaluated using stratified train-test splits and hyperparameter optimization, with performance of the model assessed through accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results in the study demonstrated that the proposed system achieves high predictive performance, with a validation accuracy of 94.9%, precision of 92.8%, recall of 90.5%, and ROC-AUC of 94.7%, thereby effectively detecting fraudulent transactions while minimizing false positives. Finally, comparative analysis was conducted and it indicated that the model performs competitively against existing methods, highlighting the importance of robust preprocessing and feature engineering. The proposed system is modular and scalable, offering practical applicability for real-time financial fraud detection, thereby enhancing transaction security and reliability.