Fine-Grained Behavioral Modeling with Graph Neural Networks for Financial Identity Theft Detection

Online-to-Offline (O2O) e-commerce services and their users confront a spectrum of fraud risks, where financial identity theft is prevalent and severe. However, current approaches are inadequate to cover such fraud. To address this problem, we consider both environmental entity interactions and activity sequences to model more granular user behaviors. According to our preliminary study, we discovered that fraudulent users exhibit high aggregations of various environmental entities and fraudulent individuals using the same personal ID that features diverse interactions with different environmental entities. We further investigate the abnormal behaviors of individual fraudsters. Motivated by these discoveries, we propose a deep learning-based behavior modeling framework named EnvIT to capture the above behavior patterns. Therefore, EnvIT is sufficiently general to learn user representations for various e-commerce fraud situations. Extensive experiments are conducted on two real-world datasets provided by Meituan and Vesta, respectively. The results demonstrate the superiority of our method, with a 0.17%-13.50% improvement in AUC and 1.13%-22.57% in R@90%P on the Meituan dataset, and a 0.71%-11.94% improvement in AUC and 2.99%-21.19% in R@90%P on the Vesta dataset, respectively.