<![CDATA[The rapid growth of digital financial transactions has increased the demand for intelligent, scalable, and real-time fraud detection systems capable of identifying fraudulent activities with high accuracy and low latency. This paper proposes an optimized hybrid machine learning framework for real-time financial fraud detection by integrating Random Forest and XGBoost within a weighted soft-voting ensemble classifier. To address the severe class imbalance commonly found in financial datasets, the transaction data were preprocessed using the SMOTE-ENN hybrid resampling technique and Min-Max normalization. Bayesian optimization was employed to tune model hyperparameters and improve generalization while reducing overfitting. The proposed framework was trained and evaluated using stratified data partitioning and 5-fold cross-validation, with performance assessed using Accuracy, Precision, Recall, F1-score, AUC-ROC, and false-negative rate. Experimental results demonstrate that the hybrid ensemble consistently outperforms the individual base classifiers, achieving improved fraud detection capability while maintaining high processing throughput suitable for real-time deployment in a Kafka-based streaming environment. These findings indicate that the proposed framework provides an effective and scalable solution for modern financial fraud detection systems.]]>
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