Multi-Granularity Augmented Graph Learning for Spoofing Transaction Detection

Spoofing is a deceptive trading strategy where fraudsters place a large number of fake orders to manipulate market prices, severely distorting market fairness and threatening market stability. With the advancement of fraudulent tactics, spoofing patterns span across various levels of interaction, involving not only the local structure of individual spoofing transactions but also spoofing groups and global patterns. Relying solely on local context makes it challenging to capture multi-granularity risk signals, especially for organized and covert spoofing. Additionally, existing methods fail to consider the differences and relative importance between features of varying granularity, leading to feature distortion and noise. Therefore, we propose a multi-granularity augmented graph learning method that differentially captures fraud signals at local, group, and global levels. It utilizes multi-hop differential aggregation and community-augmented strategy to capture information from local to global perspectives, adaptively distinguishing the contributions of different granularity. To avoid excessive fusion of multi-granularity information, we combine contrastive loss and cross-entropy loss for joint optimization, preserving key features while enhancing the method’s robustness and accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed approach in spoofing detection, providing a robust solution for regulatory agencies. Our work will help financial institutions enhance their regulatory capabilities, protect investors’ interests, and promote the healthy development of financial markets.