A Comparative Study of Sampling Techniques for Imbalanced Credit Card Fraud Detection
Shyam Penumala · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: Credit card fraud detection remains a critical challenge for financial institutions due to the highly imbalanced nature of the data, where fraudulent transactions are vastly outnumbered by legitimate ones. This study presents a comparative analysis of various sampling techniques to address this imbalance and enhance fraud detection performance. We explore and evaluate methods including Tomek Links Undersampling, Borderline-SMOTE, and hybrid techniques combining Borderline-SMOTE with Tomek Links and BIRCH Clustering. Using a synthetic dataset from the PaySim, we assess the effectiveness of these techniques across multiple machine learning models. Our results demonstrate that hybrid approaches, particularly those integrating both oversampling and undersampling, significantly improve classification metrics such as F1-score, ROC-AUC, and precision-recall. This comprehensive evaluation provides valuable insights into the strengths and limitations of each method, offering practical guidelines for selecting appropriate sampling strategies in fraud detection systems.