A Novel Tax Evasion Detection Framework via Fused Transaction Network Representation

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.