Evaluating the Corporate Tax Performance and Analyzing the Tax Trends through the Utilization of Data Mining Algorithms
There is always a considerable difference between the corporate performance and the tax levy that is identified by the taxation authorities which has become a common practice. This fact has led to no fairness among taxpayers, a fact that influences the horizontal and vertical sides of equity. Horizo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
University of Tehran
2015-09-01
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| Series: | تحقیقات مالی |
| Subjects: | |
| Online Access: | https://jfr.ut.ac.ir/article_57311_e47b9a6a26850fb9407b7cd94fe95f4e.pdf |
| Summary: | There is always a considerable difference between the corporate performance and the tax levy that is identified by the taxation authorities which has become a common practice. This fact has led to no fairness among taxpayers, a fact that influences the horizontal and vertical sides of equity. Horizontal equity is created when people feel the benefits of the tax gain that is proportional to the loss of benefits. People with more financial means should also pay more taxes that is equivalent to vertical equity. One reason for the difficulty of attaining the horizontal and vertical equities is to identify the taxpayers based on their previous taxation behavior and to deal with them effectively. The aim of this study is the design of a predictive system that evaluates the corporates taxation behavior based on their previous payments. The predicting system uses key performance variables that are identified during research and it will also help in the classification of companies based on their taxation behavior into three groups of high risk, medium risk and low risk. The system is specifically designed for the taxation authorities who are attempting to effectively assessing the risk of corporate taxes gaining. In this study, the taxation clusters of customers are identified and a decision tree is designed with 80% of accuracy by the utilization of clustering and classification algorithms and effective validation methods. The resulting models of applied algorithms investigate the taxation behavior of each customer and are capable of predicting the tax payment risk of taxpayers in the future with the addition of new corporates to the list. |
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| ISSN: | 1024-8153 2423-5377 |