IEEE Trans Vis Comput Graph. 2021 Feb;27(2):849-859. doi: 10.1109/TVCG.2020.3030370. Epub 2021 Jan 28.
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 have failed to support the analysis and exploration of the 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 respective 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.
逃税是许多国家面临的一个严重经济问题，因为它会破坏政府的税收制度，并导致不公平的商业竞争环境。最近的研究已经应用数据分析技术来分析和检测个体纳税人的逃税行为。然而，它们未能支持对涉及一组纳税人的相关方交易逃税 (RPTTE) 行为（例如，转让定价）的分析和探索。在本文中，我们提出了 TaxThemis，这是一个交互式可视分析系统，通过分析异构的税务相关数据，帮助税务人员挖掘和探索可疑的逃税群体。构建了纳税人网络，并与各自的贸易网络融合，以检测可疑的 RPTTE 群体。设计了丰富的可视化效果，以促进对相关纳税人之间可疑交易的探索和调查，包括利润和拓扑数据分析。具体来说，我们提出了一个带有精心设计编码方案的日历热图，直观地显示通过关联方交易转移收入的证据。我们通过对真实税务相关数据的两个案例研究和与领域专家的访谈，展示了 TaxThemis 的有用性和有效性。
