  Credit card fraud has significant implications at both an individual and
societal level, making effective prevention essential. Current methods rely
heavily on feature engineering and labeled information, both of which have
significant limitations. In this work, we present GraphGuard, a novel
contrastive self-supervised graph-based framework for detecting fraudulent
credit card transactions. We conduct experiments on a real-world dataset and a
synthetic dataset. Our results provide a promising initial direction for
exploring the effectiveness of graph-based self-supervised approaches for
credit card fraud detection.
