In the fast-evolving landscape of credit card fraud detection, the imperative to swiftly and accurately identify fraudulent transactions has become paramount. Class imbalance poses a significant challenge to traditional methods, hindering their adaptability to the intricate nature of fraud. This study investigates the efficacy of Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Networks (GANs) in mitigating class imbalance. Utilizing a dataset of 1.75 million transactions, both methods generate balanced datasets, evaluated through Isolation Forest models. Despite suboptimal GAN training, the GAN-generated dataset closely aligns with SMOTE in performance metrics. Precision, recall, F1-score, and an overall accuracy of 0.51 for GAN and 0.54 for SMOTE reveal competitive results, indicating the promising potential of GANs in handling class imbalance for anomaly detection in credit card transactions. The study emphasizes the significance of advanced generative techniques for improved model performance and robust handling of imbalanced data scenarios.

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Credit card fraud detection class imbalance anomaly detection Synthetic Minority Over-sampling Technique (SMOTE) Generative Adversarial Networks (GANs) Isolation Forest machine learning