Fraud Identification in Online Financial Transactions Combining SMOTE and Ensemble Classifiers

Abstract

The increasing reliance on digital payment systems has intensified the exposure of financial institutions to credit card fraud, particularly under conditions of extreme class imbalance where fraudulent transactions account for less than 0.2% of total records. This study presents SMOVO, a Synthetic Minority Oversampling Technique (SMOTE)-based Voting Ensemble. It is a fraud detection framework designed to address this imbalance through the integration of three complementary components: Analysis of Variance (ANOVA) F-test-based feature selection, SMOTE-based data resampling, and a heterogeneous soft-voting ensemble comprising Random Forest (RF), XGBoost, LightGBM, and AdaBoost. Evaluated on the European Credit Card Fraud Dataset, SMOVO attained a recall of 0.962, F1-score of 0.864, Area Under the Curve (AUC) of 0.982, Matthews Correlation Coefficient (MCC) of 0.868, and Geometric mean (G-mean) of 0.980. In comparison with generative oversampling approaches such as ESMOTE-GAN, SMOVO exhibits more stable training behavior, reduced computational overhead, and improved model interpretability. These results indicate that SMOVO provides an effective and practically deployable solution for fraud detection in highly imbalanced financial transaction environments.