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All Journal Control Systems and Optimization Letters
Md Zahidul Islam Sany
Hubei University of Automotive Technology
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Optimized Hybrid Machine Learning Model for Real-Time Financial Fraud Detection Md Zahidul Islam Sany; Zhang Wubo; Shahin Alam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.332

Abstract

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.
Co-Authors Shahin Alam Zhang Wubo