Modern machine learning and deep learning algorithms for preventing credit card frauds

Abstract

Credit card fraud poses a significant threat to financial institutions and consumers, particularly in the context of online transactions. Conventional rule-based systems often struggle to keep pace with the evolving tactics of fraudsters. This research paper investigates the application of advanced machine learning and deep learning algorithms for credit card fraud detection. By reviewing existing methodologies and addressing the challenges associated with fraud detection, we explore the potential of stateof-the-art techniques in enhancing detection accuracy and efficiency. Key aspects such as transaction data analysis, feature engineering, model evaluation metrics, and practical implementations are discussed. The findings underscore the importance of leveraging advanced algorithms to combat fraudulent activities effectively, thereby safeguarding the integrity of online transactions.