Credit card fraud detection remains a critical challenge in today’s digital economy, with financial institutions incurring billions in losses annually. This paper presents a comprehensive study on employing artificial intelligence techniques for credit card fraud detection. We detail the historical context, background, and statistics motivating this research, followed by a review of related work. Our proposed methodology leverages a hybrid machine learning approach combining ensemble methods and deep learning to improve detection accuracy while reducing false positives. Experimental results on a publicly available credit card dataset are discussed, with performance metrics and comparative evaluations demonstrating significant improvements over baseline models. Finally, we conclude with insights and future research directions.

Big Data, Business Management, Credit Card Fraud, Machine Learning.