The rapid proliferation of digital banking services has substantially elevated the risk of financial fraud, rendering conventional rule-based detection mechanisms insufficient for contemporary threats. This paper presents a comprehensive machine learning framework designed for real-time banking fraud detection that leverages supervised ensemble methods — specifically Random Forest and Gradient Boosting — augmented by data preprocessing strategies including Synthetic Minority Oversampling Technique (SMOTE) to address inherent class imbalance. Historical transaction data encompassing features such as transaction amount, geographic location, temporal patterns, device metadata, and behavioral analytics are used to train and validate classification models. Empirical evaluations demonstrate that the proposed ensemble-based architecture achieves a classification accuracy of 94%, precision of 0.96, recall of 0.93, and an F1-score of 0.94 on an imbalanced benchmark dataset. Comparative analysis against Logistic Regression, Decision Trees, and Support Vector Machines confirms the superiority of ensemble methods for complex, high-volume financial datasets. The system is further validated in a simulated real-time streaming environment using API-based integration, demonstrating sub-second latency and robust scalability. The findings affirm that intelligent, adaptive machine learning pipelines represent a practical and effective solution for combating evolving fraud patterns in digital financial ecosystems.
Shivani et al. (Mon,) studied this question.
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