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JOURNALS || EIJO Journal of Engineering, Technology and Innovative Research (EIJO – JETIR) [ ISSN : 2455 - 9172 ]
Weighted Edge Computation for Mining Tax Evasion
Author Names : Shivani Korde, Pratiksha Wasekar, Rohini Patil, Amruta Kale, Rajesh H Kulkarni
Volume 2 Issue 2
Abstract
Tax evasion is the illegal activity done by individuals, corporations, and trusts to avoid tax. There is an evidence that an increasing the property to avoid tax in an unobserved way. At the same time, taxation information related data is classic kind of big data. The issue challenges the effective solution of traditional tax evasion detection method. To avoid this, we first investigate the tax evasion cases and apply a graph-based method to characterize their property that describes two suspicious relationship trails with a same antecedent node behind an Interest Affiliated Transaction (IAT).After finding the tax avoidance cases then heterogeneous information network is applied and using this network properties are analyzed. After that CNBN (colored network based model) is used for characterizing economic behavior and social relationship IATs between the taxpayers. This method is beneficial to improve the efficiency of tax evasion detection.
Keywords: Weighted edge, Graph mining, tax evasion, interest affiliated transaction, heterogeneous information network.
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