Abstract
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.
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Acknowledgments
This work is supported by “The Fundamental Theory and Applications of Big Data with Knowledge Engineering” under the National Key Research and Development Program of China with Grant No. 2016YFB1000903, the National Science Foundation of China under Grant Nos. 61502379, 61472317, 61532015, and Project of China Knowledge Centre for Engineering Science and Technology.
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Wei, W., Yan, Z., Ruan, J., Zheng, Q., Dong, B. (2017). Mining Suspicious Tax Evasion Groups in a Corporate Governance Network. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_33
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DOI: https://doi.org/10.1007/978-3-319-65482-9_33
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