There is a new tendency for corporations to evade tax via Interest Affiliated Transactions (IAT) that are controlled by a potential “Guanxi” between the corporations’ controllers. At the same time, the taxation 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 coin a definition of controller interlock, which characterizes the interlocking relationship between corporations’ controllers. Next, we present a colored and weighted network-based model for characterizing economic behaviors, controller interlock and other relationships, and IATs between corporations, and generate a heterogeneous information network-corporate governance network. Then, we further propose a novel Graph-based Suspicious Groups of Interlock based tax evasion Identification method, named GSG2I, which mainly consists of two steps: controller interlock pattern recognition and suspicious group identification. Experimental tests based on a real-world 7-year period tax data of one province in China, demonstrate that the GSG2I method can greatly improve the efficiency of tax evasion detection.
CITATION STYLE
Wei, W., Yan, Z., Ruan, J., Zheng, Q., & Dong, B. (2017). Mining suspicious tax evasion groups in a corporate governance network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10393 LNCS, pp. 465–475). Springer Verlag. https://doi.org/10.1007/978-3-319-65482-9_33
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