Assessing the Effectiveness of Convolutional Neural Networks in Real-Time Detection and Prevention of Credit Card Fraud

Abstract

Credit card fraud has become a significant concern for financial institutions and cardholders worldwide, leading to substantial financial losses and compromised trust in the payment system. Traditional fraud detection methods often struggle to keep pace with the evolving tactics employed by fraudsters, highlighting the need for more advanced and efficient detection techniques. Convolutional Neural Networks (CNNs), a class of deep learning algorithms, have shown remarkable success in various domains, including image and pattern recognition. This research explores the effectiveness of CNNs in detecting and preventing credit card fraud in real-time transactions. By leveraging the ability of CNNs to automatically learn and extract complex patterns from large volumes of transactional data, this study aims to develop a robust and accurate fraud detection system. The proposed CNN-based approach is evaluated using real-world credit card transaction datasets, and its performance is compared against traditional machine learning techniques. The findings of this research contribute to the advancement of fraud detection strategies and provide valuable insights for financial institutions seeking to strengthen their fraud prevention measures in the face of increasingly sophisticated fraudulent activities.