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Information Journal Paper

Title

Tax Evasion Modeling of Related Party Transactions: A Hybrid Approach of Graph Mining and Deep Neural Network

Pages

  7-52

Abstract

 Tax evasion based on related party transactions is a new strategy in tax evasion that is carried out through legal transactions, such as transactions between a group of companies that have heterogeneous, complex, and hidden interaction relationships for tax evasion. Existing studies cannot effectively identify tax evasion behaviors of related parties because the machine learning-based audit method can detect the abnormal financial status of individuals with high accuracy and efficiency. However, it is helpless when faced with heterogeneous, complex, and hidden interaction relationships and cannot identify tax evasion groups with related party transactions. The hybrid of graph mining and deep neural network approaches has the ability to detect anomalies in complex organizational structures. In this study, 1,780 companies with related party transactions, including 523 companies located in free trade zones and 1,257 companies located outside free trade zones, which have a common board member and economic activity of production or trade, were selected. In this study, financial and tax data from tax returns and the systems of the Iranian Tax Administration from 2016 to 2019 were used. This study is practical in terms of purpose. Python software and the NetworkX package were used to estimate the model. To predict tax evasion in related party transactions, three algorithms were used: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multilayer Perceptron Neural Network (MLP) in deep mode. To identify suspicious groups, three steps were taken; first: detecting tax rate differences, matching the topological pattern, and identifying tax burden anomalies; second: experimental tests based on data from 16,756 related party transaction purchases and sales in the country; third: estimating the coefficients and the relationship between the topological pattern in the two cases of profit retention and profit transfer based on the graph mining approach and deep neural network. The results show that both profit retention and profit shifting exist in tax evasion of related party transactions. However, based on the results, the intensity of the profit retention relationship in tax evasion of related party transactions is stronger than the profit shifting relationship. Based on the results, the graph mining approach was more accurate than the logit, probit, and linear probability models.

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