Identifying Tax Audit Cases with Multi-task Learning
Li Guofeng1,Li Zuojuan1(),Wang Zheji1,Wu Meng2
1School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China 2School of Economics, Shandong University of Finance and Economics, Jinan 250014, China
[Objective] This paper integrates tax-related data from multiple sources, and uses machine learning methods to identify the illegal corporate tax evasions. [Methods] First, we use web-scraping, text mining, and other methods to collect business financial data, executive information, and media coverage of the corporations. Then, we used the random forest method for feature selection and established indictors for the candidate companies. Then, we built a discriminatory model with the multi-task sparse structure learning based on the improved focal loss function. Finally, we trained the model with different types of tax audits to identify the needed candidates. [Results] We examined our model with real world datasets and found it had good performance for various applications. Its mean recall rate reached 0.830 9, which was 0.135 1 and 0.103 3 higher than the logistic method and the traditional multi-task sparse structure learning. [Limitations] The model needs to be examined with datasets not from the listed companies. [Conclusions] The new model could identify the target enterprises with various dishonest tax evasions. This study provides new directions for smart tax audit by the government.
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