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.
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