DETECTING REAL-TIME E-COMMERCE FRAUD WITH ADVANCED ENSEMBLE META-MODELING

Abstract

As e-commerce transactions continue to surge, the threat of fraud has escalated, posing significant challenges due to class imbalances, rapidly evolving fraud tactics, and the critical need to balance false positives and negatives. This study effectively addresses these challenges through an advanced ensemble stacking approach, integrating Support Vector Machine (SVM), Neural Network, Gradient Boosting, and AdaBoost as base models, with a Random Forest as meta-model to deliver final predictions. Using an e-commerce transaction dataset, our approach achieved 99.87% accuracy, significantly outperforming individual models. The meta-model further demonstrated 0.99 precision, 0.98 recall, and 0.99 F1-score for fraud cases (Class 1), highlighting its strong ability to accurately detect fraudulent transactions while minimizing false positives and false negatives. While SVM had the longest execution time, the Neural Network was the most efficient, and AdaBoost contributed the most to the meta-model’s predictions. Model validation was performed using Local Interpretable Model-Agnostic Explanations (LIME), highlighting Transaction Hour, Transaction Amount, and Account Age Days as key predictive features. The model was successfully deployed to a web-based application, demonstrating real-time fraud detection capabilities. This research offers a robust, interpretable method for e-commerce fraud prevention, potentially reducing financial losses and enhancing online transactions.