International Conference on Computer and Knowledge Engineering

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Ensemble-Based Fraud Detection: A Robust Approach Evaluated on IEEE-CIS

Authors :

Fatemeh Moradi¹ Mehran Tarif² Mohammadhossein Homaei³

1- Faculty of Engineering Isfahan (Khorasgan) Branch, Islamic Azad University Isfahan, Iran 2- Department of Computer Science University of Verona Verona, Italy 3- Media Engineering Group University of Extremadura C´aceres, Spain

Keywords :

Credit Card Fraud Detection،Ensemble learning،Imbalanced Classification،IEEE-CIS Dataset،Machine Learning،Financial Security

Abstract :

Credit card fraud has increased with the fast expansion of online financial transactions, requiring the implementation of advanced detection systems. According to the IEEE-CIS dataset, this paper presents an extensive empirical assessment of ensemble learning methods for class-imbalanced fraud detection. By evaluating ensemble techniques such as Random Forest, XGBoost, LightGBM, and stacking approaches systematically, we address the critical issues of extreme class imbalance, concept drift, and real-time detection requirements. Our solution involves comprehensive feature engineering strategies tuned to the IEEE-CIS dataset, which consists of 590,540 transactions with a fraud rate of 3.5%, as well as advanced data balancing techniques (SMOTE, ADASYN, and Borderline-SMOTE). From experimental results, our ensemble stacking approach maintains low false positive rates while fraud is detected at high rates (0.918 AUC-ROC, 0.891 AUC-PR) and outperforms. The study offers useful implications for real-world practical implementation and empirical proof of the proficiency of ensemble approaches in dealing with highly imbalanced financial fraud datasets.