6 research outputs found
TaxThemis: Interactive mining and exploration of suspicious tax evasion group
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.Comment: 11 pages, 7 figure
Combining Network Visualization and Data Mining for Tax Risk Assessment
This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: ( ) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; ( ii ) an initial set of risky taxpayers is identified by applying machine learning algorithms; ( iii ) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; ( iv ) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency