TTED-PU:A Transferable Tax Evasion Detection Method Based on Positive and Unlabeled Learning

Tax evasion usually refers to taxpayers making false declarations in order to reduce their tax obligations. One of the most common types of tax evasion is to lower the declared taxable amount. This kind of behavior will lead to the loss of tax revenues and damage the fairness of taxation. One of the main roles of the tax authorities is to conduct tax evasion testing through efficient auditing methods. At present, by using machine learning technology along with large amounts of labeled data, tax evasion detection models have achieved good results in specific areas. However, it is a long and costly process for tax experts to label large amounts of data. Since, the data distribution characteristics vary from region to region, models cannot be used across regions. In this paper, we propose a new method called a transferable tax evasion detection method based on positive and unlabeled learning (TTED-PU), which uses only semi-supervised techniques to detect tax evasion in the source domain. In addition, we use the idea of transfer to adapt to the domain to predict tax evasion behavior on the target domain where labeled tax data are unavailable. We evaluate our method on real-world tax data set. The experimental results show that our model can detect tax evasion in both the source and target domains.