T-EGAT: A Temporal Edge Enhanced Graph Attention Network for Tax Evasion Detection

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.