TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Group
This addresses tax evasion detection for tax officers by enabling analysis of group behaviors, though it is incremental as it builds on existing data analytics with new visualizations.
The paper tackles the problem of detecting related party transaction tax evasion (RPTTE) groups, which existing methods fail to address, by presenting TaxThemis, an interactive visual analytics system that mines suspicious groups using fused taxpayer and trade networks, with case studies and expert interviews demonstrating its effectiveness.
Tax evasion is a serious economic problem for many countries, as it can undermine the government' s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they failed to support the analysis and exploration of the uprising related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data, and interviews with domain experts.