Abstract
There is evidence that an increasing number of enterprises plot together to evade tax in an unperceived way. At the same time, the taxation information related data is a classic kind of big data. These issues challenge the effectiveness of traditional data mining-based tax evasion detection methods. To address this problem, we first investigate the classic tax evasion cases, and employ 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). Next, we propose a Colored Network-Based Model (CNBM) for characterizing economic behaviors, social relationships, and the IATs between taxpayers, and generating a Taxpayer Interest Interacted Network (TPIIN). To accomplish the tax evasion detection task by discovering suspicious groups in a TPIIN, methods for building a patterns tree and matching component patterns are introduced and the completeness of the methods based on graph theory is presented. Then, we describe an experiment based on real data and a simulated network. The experimental results show that our proposed method greatly improves the efficiency of tax evasion detection, as well as provides a clear explanation of the tax evasion behaviors of taxpayer groups.
Original language | English |
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Pages (from-to) | 2651 - 2664 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 28 |
Issue number | 10 |
Early online date | 8 Jun 2016 |
DOIs | |
Publication status | Published - 1 Oct 2016 |
Bibliographical note
This paper is not yet available on the repositoryKeywords
- big data
- Graph mining
- tax evasion
- interest-affiliated transaction
- heterogeneous information network