Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2021

Abstract

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.

Keywords

Visual Analytics, Tax Network, Tax Evasion Detection, Anomaly detection, Multidimensional data

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Visualization and Computer Graphics

First Page

1

Last Page

12

ISSN

1077-2626

Publisher

Institute of Electrical and Electronics Engineers

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