A Survey of Tax Risk Detection Using Data Mining Techniques

Qinghua Zheng, Yiming Xu, Huixiang Liu, Bin Shi, Jiaxiang Wang, Bo Dong

Engineering ›› 2024, Vol. 34 ›› Issue (3) : 43-59.

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Engineering ›› 2024, Vol. 34 ›› Issue (3) : 43-59. DOI: 10.1016/j.eng.2023.07.014
Research
Review

A Survey of Tax Risk Detection Using Data Mining Techniques

Author information +
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Highlights

・To the best of our knowledge, we are the first to systematically review the research progress and development trends of tax risk detection worldwide.

・We introduce the relevant background knowledge related to tax risk detection, including the causes and harms of tax risk behaviors, along with the development process of tax risk detection. In addition, we provide a formal definition of tax risk detection and introduce details of the input data.

・We comprehensively sort out the research on tax risk detection, divide the existing methods into two categories, then list and introduce the 14 kinds of methods identified. Furthermore, we summarize the advantages and disadvantages of each method.

・We summarize the main problems faced in current tax risk detection practice, and further suggest a list of future research direction.

Abstract

Tax risk behavior causes serious loss of fiscal revenue, damages the country’s public infrastructure, and disturbs the market economic order of fair competition. In recent years, tax risk detection, driven by information technology such as data mining and artificial intelligence, has received extensive attention. To promote the high-quality development of tax risk detection methods, this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide. More specifically, it first discusses the causes and negative impacts of tax risk behaviors, along with the development of tax risk detection. It then focuses on data-mining-based tax risk detection methods utilized around the world. Based on the different principles employed by the algorithms, existing risk detection methods can be divided into two categories: relationship-based and non-relationship-based. A total of 14 risk detection methods are identified, and each method is thoroughly explored and analyzed. Finally, four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed, including the difficulty of integrating and using fiscal and tax fragmented knowledge, unexplainable risk detection results, the high cost of risk detection algorithms, and the reliance of existing algorithms on labeled information. After investigating these issues, it is concluded that knowledge-guided and data-driven big data knowledge engineering will be the development trend in the field of tax risk in the future; that is, the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

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Keywords

Tax risk detection / Data mining / Knowledge guide / Informatization / Intellectualization

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Qinghua Zheng, Yiming Xu, Huixiang Liu, Bin Shi, Jiaxiang Wang, Bo Dong. A Survey of Tax Risk Detection Using Data Mining Techniques. Engineering, 2024, 34(3): 43‒59 https://doi.org/10.1016/j.eng.2023.07.014

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