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 behavior, 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.