An Ensemble Machine Learning Approach for Financial Fraud Detection in Banking Systems

Abstract

Financial fraud detection is a critical challenge in modern banking systems, where fraudulent transactions represent less than 0.13% of total transactions, creating severe class imbalance. Traditional rule-based systems struggle to adapt to evolving fraud patterns, necessitating machine learning approaches that can learn complex patterns from historical data while handling extreme class imbalance. This study implements and evaluates three feature selection techniques Minimum Redundancy-Maximum Relevance (mRMR), Multi Spatially Uniform ReliefF (MultiSURF), and Hilbert-Schmidt Independence Criterion (HSIC) combined with eight optimized machine learning classifiers including Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), AdaBoost (Ada), Bagging (Bag), Stacking (Stack), and Voting classifiers. The financial payment services fraud dataset containing 6,362,620 transactions was preprocessed using under sampling for class balancing and Standard Scaler for normalization. GridSearchCV with 5-fold cross-validation was employed for hyperparameter optimization across 32 experimental configurations. Experimental results demonstrate that Bagging classifier without feature selection achieves the highest performance with 99.51% accuracy, 99.15% precision, 99.88% recall, 99.51% F1-score, and 99.92% ROC-AUC in 10.67 seconds training time. Among feature selection methods, MultiSURF maintains competitive performance, while mRMR and HSIC show performance degradation. Ensemble methods consistently outperform single classifiers across all experimental scenarios. This study provides empirical evidence that for fraud detection with well-engineered features in low-dimensional spaces, ensemble methods yield optimal results. The undersampling strategy proves effective for handling class imbalance while maintaining computational efficiency. The findings demonstrating that feature selection is not always beneficial when original features are already highly relevant.