Abstract
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: (i) 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.
Highlights
Tax noncompliance is a serious economic problem for many countries
OUR CONTRIBUTION To support AdE tax officers in the tax risk assessment, we present a novel approach, called MALDIVE (MAtch, Learn, DIffuse, and VisualizE), that combines different data mining and data analytics methods, such as graph pattern matching, social network analysis, machine learning, information diffusion, and network visualization
The more recent system EVA [50] integrates various techniques to detect fraudulent transactions within a financial institution. It offers a scoring mechanism based on data mining techniques and interactive visual analytics facilities to perform fraud validation, but it is not based on a network representation of the data
Summary
Tax noncompliance is a serious economic problem for many countries. It consists of a range of activities, such as tax evasion and tax avoidance, that undermine the government’s tax system. A. OUR CONTRIBUTION To support AdE tax officers in the tax risk assessment, we present a novel approach, called MALDIVE (MAtch, Learn, DIffuse, and VisualizE), that combines different data mining and data analytics methods, such as graph pattern matching, social network analysis, machine learning, information diffusion, and network visualization. OUR CONTRIBUTION To support AdE tax officers in the tax risk assessment, we present a novel approach, called MALDIVE (MAtch, Learn, DIffuse, and VisualizE), that combines different data mining and data analytics methods, such as graph pattern matching, social network analysis, machine learning, information diffusion, and network visualization This approach has been designed and implemented in collaboration with. Thanks to a visual exploration of the social network, the analyst can better assess the real risk profile of taxpayers, carrying out a more effective selection of tax audits [9], [10].
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