Abstract :

card fraud continues to pose a significant threat to financial institutions and consumers worldwide. In recent years, the proliferation of advanced technology has enabled fraudsters to develop increasingly sophisticated methods for perpetrating fraudulent transactions. To combat this ever-evolving challenge, this study explores the application of state-of-the-art machine learning and deep learning algorithms for credit card fraud detection. This research leverages a comprehensive dataset containing both legitimate and fraudulent credit card transactions, allowing for the evaluation of various detection methods. We employ a diverse set of machine parameter tuning to optimize the algorithms' performance. We also explore ensemble techniques, such as stacking and boosting, to harness the strengths of multiple models and enhance overall fraud detection capabilities.