Exploring Deep Learning Techniques for Credit Card Fraud Detection in Digital Payment Systems

Abstract

The rise of digital payment systems has brought unprecedented convenience but also an increase in credit card fraud. Deep learning techniques have emerged as a transformative approach for detecting fraudulent transactions by analyzing complex patterns and large-scale data. This paper explores various deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, for credit card fraud detection. Emphasis is placed on their ability to process high-dimensional data and improve detection accuracy while minimizing false positives. The study reviews the challenges in data imbalance, real-time processing, and model interpretability, proposing solutions through advanced preprocessing techniques and hybrid models. Through a synthesis of recent research and practical applications, this paper demonstrates how deep learning enhances the security and trustworthiness of digital payment systems.