Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Abstract

Affiliated-transaction-based tax evasion (ATTE) is a new strategy in tax evasion that is carried out via legal-like transactions between a group of companies that have heterogeneous, complex and covert interactive relationships to evade taxes. Existing studies cannot effectively detect ATTE behaviors since (i) they perform well only for determining the abnormal financial status of individuals and ineffectively address the interactive relationships among companies, (ii) they aim at detecting ATTE from the perspective of structural characteristics, which leads to a poor false-positive rate, and (iii) few of them perform well in most sectors of companies. Effectively detecting suspicious groups according to both structural characteristics of ATTE groups and business characteristics of ATTE means (BC-ATTEM) remains an open issue. In this paper, we propose an affiliated-parties interest-related network (APIRN) for modeling affiliated parties, interest-related relationships, and their properties for identifying ATTE. Then, we identify the behavioral patterns of ATTE via topological pattern abstraction from APIRN and the-

Combining Network Visualization and Data Mining for Tax Risk Assessment

“…This human validation activity is fundamental for the tax administration, which must have complete control over the taxpayer selection process. In fact, thanks to a visual exploration of the social network, the analyst can better assess the real risk profile of taxpayers, thus carrying out a more effective selection of tax audits [9], [10]. Namely, the analyst can find new risky graph patterns or false negative cases, as it will be shown in our case studies.…”

“…The idea of modeling the taxpayers data by means of a suitable network is motivated by the fact that many real cases of tax evasion are implemented through the interaction of various subjects. Examples include carousel VAT frauds, exchange of false invoices, and tax avoidance implemented through transfer pricing between corporate groups (see, e.g., [7]- [10]). The MAtch phase is aimed to define suspicious graph patterns that represent risky schemes in the taxpayer social network.…”

“…Network-based analysis are also applied in various economic and financial fields, as well as in crime detection systems, to detect anomalous relational patterns [10], [22]- [29]. The Belgian government uses a data mining technique to find corporate residence frauds [23].…”

“…Vlasselaer et al [19] apply a network-based approach to detect frauds related to companies which intentionally go bankrupt in order to avoid paying taxes. Network-based methods to find a specific type of tax evasion performed by affiliated-transaction and involving medium and large companies are proposed in [10], [25], [26].…”

Audit and tax in the context of emerging technologies: A retrospective analysis, current trends, and future opportunities

“…Having used a 50-state comparison, the authors recommended using emerging technologies in tax collection, audit, and monitoring. Furthermore, Ruan et al, (2019) stated that the complex and voluminous transactions offer a chance for the business to manipulate their taxes. Hence, many technologies were developed to enhance the effectiveness of tax authorities' work.…”