TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Group
Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng,
View presentation: 2020-10-30T16:00:00Z GMT-0600 Change your timezone on the schedule page
Direct link to video on YouTube: https://youtu.be/knd8s4qjFkM
Visual Analytics, Tax Network, Tax Evasion Detection, Anomaly detection, Multidimensional data
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.