Deep Learning Approaches for Credit Card Fraud Detection: A Data Balancing Perspective

Credit card fraud detection is a critical challenge in modern financial systems, requiring robust and efficient solutions to identify suspicious transactions accurately. This study addresses the issue by utilizing machine learning techniques to detect potentially fraudulent transactions. A key focus of the research is the handling of imbalanced datasets, where genuine transactions vastly outnumber fraudulent ones. To address this imbalance, we increased the sample size of fraudulent data using oversampling techniques and subsequently applied four distinct machine learning models to assess their performance. Through iterative experimentation, we identified the optimal magnification ratio that enhances the model's ability to distinguish between legitimate and fraudulent transactions. The results demonstrate that balancing the dataset significantly improves detection accuracy, providing insights into effective model configurations for realworld applications. This research contributes to the development of more reliable and efficient fraud detection systems in the financial sector.