Modeling Access Environment and Behavior Sequence for Financial Identity Theft Detection in E-Commerce Services

Online-to-Offline (O2O) e-commerce service platforms and their users are faced with various fraud risks. Among them, financial identity theft is a widely existing challenge. However, existing methods are insufficient to detect this type of fraud. In this paper, we address the financial identity theft detection problem in e-commerce services by leveraging access environment and behavior sequence. To explore the fraud patterns, we first make a detailed analysis using real cases of identity theft from Meituan, a leading O2O e-commerce platform in China. Our findings are twofold. First, fraudulent accounts sharing the same personal ID would have different access environments, such as devices and IP addresses. Second, a group of fraudulent accounts may have aggregations of devices, IP addresses, and delivery addresses. Based on these observations, we propose a hybrid method termed EnvIT to detect financial identity theft based on the heterogeneous graph and the behavior sequence. EnvIT is able to characterize the access environment and the historical behavior of the accounts. Furthermore, an attentive module is adopted to assign weights to different features automatically. We further evaluate EnvIT via extensive experiments using a real-world dataset from Meituan. Our experimental results demonstrate that EnvIt outperforms several baseline methods in fraudulent account detection and achieves an AUC of 0.9210.