NEUD-TRI: Network Embedding Based on Upstream and Downstream for Transaction Risk Identification

Invoices serve as records of financial transactions of taxpayers and significant basis to controlling tax source and collection of tax, via analyzing which, we can discern diversified tasks of tax risk, such as industry identification, hidden transaction detection, and illegal behavior mining. Among all the existing studies related to the identification of tax risk, there are some weaknesses through the machine learning model and network analysis because of the dependence on tax knowledge. Different from the manual selection of indicators and the manual definitions mode with the guidance of tax knowledge in the past, in this paper, we propose a novel method, namely, network embedding based on upstream and downstream for tax risk identification (NEUD-TRI), which considers the taxpayers serving as both seller and purchaser. The method designs optimization functions respectively to capture local and global static network structures and dynamic network structure. In view of the significant discrepancy of weights in the transaction network, this paper normalizes the weight within the range of the upstream and downstream of the vertex. Negative sampling and edge sampling are adopted to deal with the large-scale trait of the transaction network. Empirical results on tax data-sets of Shanxi province substantiate the effectiveness of our models.