Applying supervised machine learning algorithms and ensemble models to enhance credit card fraud detection

Credit card fraud has become a significant threat to individuals, financial institutions, businesses, and governments, causing substantial annual economic losses through increasingly sophisticated fraudulent activities. This research aims to enhance credit card fraud detection by leveraging machine learning algorithms and ensemble learning techniques to improve detection accuracy and model robustness. Various supervised machine learning algorithms were implemented, including Decision Tree (DT),