TaxThemis: Interactive mining and exploration of suspicious tax evasion group

• Main Authors: LIN, Yating, WONG, Kamkwai, WANG, Yong, ZHANG, Rong, DONG, Bo, QU, Huamin, ZHENG, Qinghua
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Visual Analytics; Tax Network; Tax Evasion Detection; Anomaly detection; Multidimensional data; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5346; https://ink.library.smu.edu.sg/context/sis_research/article/6350/viewcontent/2009.03179__1_.pdf
• Institution: Singapore Management University

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 carefullydesigned 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.