A Novel Tax Evasion Detection Framework via Fused Transaction Network Representation
Yingchao Wu, Bo Dong, Qinghua Zheng, Rongzhe Wei, Zhiwen Wang, Xuanya Li
Abstract
Tax evasion usually refers to the false declaration of taxpayers to reduce their tax obligations; this type of behavior leads to the loss of taxes and damage to the fair principle of taxation. Tax evasion detection plays a crucial role in reducing tax revenue loss. Currently, efficient auditing methods mainly include traditional data-mining-oriented methods, which cannot be well adapted to the increasingly complicated transaction relationships between taxpayers. Driven by this requirement, recent studies have been conducted by establishing a transaction network and applying the graphical pattern matching algorithm for tax evasion identification. However, such methods rely on expert experience to extract the tax evasion chart pattern, which is time-consuming and labor-intensive. More importantly, taxpayers' basic attributes are not considered and the dual identity of the taxpayer in the transaction network is not well retained. To address this issue, we have proposed a novel tax evasion detection framework via fused transaction network representation (TED-TNR), to detecting tax evasion based on fused transaction network representation, which jointly embeds transaction network topological information and basic taxpayer attributes into low-dimensional vector space, and considers the dual identity of the taxpayer in the transaction network. Finally, we conducted experimental tests on real-world tax data, revealing the superiority of our method, compared with state-of-the-art models.