Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

Abstract

Deep learning (DL), a branch of machine learning (ML), is the core technology in today's technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literature are based on traditional ML algorithms, and recently, there has been a rise in applications based on deep learning techniques. This study reviews the recent DL-based literature and presents a concise description and performance comparison of the widely used DL techniques, including convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Additionally, an attempt is made to discuss suitable performance metrics, common challenges encountered when training credit card fraud models using DL architectures and potential solutions, which are lacking in previous studies and would benefit deep learning researchers and practitioners. Meanwhile, the experimental results and analysis using a real-world dataset indicate the robustness of the deep learning architectures in credit card fraud detection.