استفاده از تکنیک داده کاوی به منظور افزایش عملکرد آشکارسازی فرار مالیاتی

Currently, tax authorities face the challenge of identifying and collecting from businesses that have successfully evaded paying the proper taxes. In solving the problem of tax evaders, tax authorities are equipped with limited resources and traditional tax auditing strategies that are time-consuming and tedious. These continued practices have resulted in the loss of a substantial amount of tax revenue for the government. The objective of the current study is to apply a data mining technique to enhance tax evasion detection performance. Using a data mining technique, a screening framework is developed to filter possible non-compliant value-added tax (VAT) reports that may be subject to further auditing. The results show that the proposed data mining technique truly enhances the detection of tax evasion, and therefore can be employed to effectively reduce or minimize losses from VAT evasion.

Tax revenue is one of the most necessary financial resources of a government for accomplishing specific goals. However, some businesses often attempt to evade their payment of correct taxes. Consequently, tax evasion creates a critical impact on the budgetary income of these businesses and of the government. These businesses incur additional social costs because they spend their valuable resources in finding means to evade taxes, rather than focus on their operations. On the side of the government, tax authorities have to bear the costs of the detection and prevention of illegal tax evasion activities. As a result, effective ways to detect related tax evasion activities have always been an important and challenging issue for tax authorities in any country. If the government cannot effectively detect illegal tax evasion activities, public investment would be negatively affected due to the budgetary shortage resulting from the loss of tax revenues. VAT (value-added tax) evasion is one of the important issues for many tax authorities. Gebauer, Nam, and Parsche (2007) report that, based on German data, the VAT revenue gap derived from the comparison of the quantified, hypothetical, and the actual collected revenues increased from 5.1% in 1995 to 7.5% in 1996. They also estimate that VAT revenue losses were approximately EURO 18 billion in 2001 for Germany alone. Gebauer et al. (2007) also suggest that VAT evasion not only leads to significant revenue losses, but also to a considerable increase in administrative costs used to detect the illegal tax evasion activities. In addition to the decreased tax revenue and increased administrative costs, VAT evasion also has a significant negative side effect on the collection of income taxes from businesses. This occurs because VAT evasion, which implies an indirectly underreported taxable income from business, is often directly accompanied by underreported sales revenues. In order to realize the benefits of spending valuable, albeit limited, resources to detect VAT evasion, tax authorities need to deploy their resources wisely. As such, tax authorities have often relied on the sampling method and the personal judgment of tax auditors in selecting suspicious tax reports to audit for potential tax evasion activities. Thus, the purpose of the current study is to determine a more scientific approach to improve tax auditor’s productivity and performance in handling the detection tasks of VAT tax evasion. All over the world, tax authorities are under increasing pressure to locate underreporting taxpayers, collect additional tax revenues, and predict the irregular behavior of non-paying taxpayers. Without the assistance of information technology tools, most tax authorities need to pull in tax data from a variety of independent sources or perform data matching and checking with other sources to find cases of non-compliance. As a result, tax evasion detection performance has been rather limited. Business intelligence (BI) in general, and data mining in particular, may be effective tools for enhancing the efficiency and effectiveness of the detection of illegal tax evasion (Fadairo, Williams, Trotman, & Onyekelu-Eze, 2008). In the US, Texas was one of the first states to apply data mining techniques for detecting suspicious tax evasion reports and thereby recoup unpaid taxes (Hoover, 2009). Songini (2004) reports that Texas uses a BI system that is able to flag a situation in which a business is suspected to be evading taxes. This suspicious tax report is referred to an audit staff for further investigation. Since the introduction and application of the BI system, USD 362 million of tax losses have already been recovered. The tax authority in Texas has also committed strongly to data mining for spotting suspicious tax reports. As cited by Songini (2004), Lisa McCormack, a manager in the tax audit division in Austin, Texas, claims, “We only audit 1% of the taxpayers… We have to try and figure out how to make the best use of the [government’s investigative] resources.” The current study intends to utilize data mining as a tool to enhance tax evasion detection performance. Data mining is a methodology used to discover hidden information from rough data (Fayyad et al., 1996 and Yoon, 1999). It can be applied in the process of decision support, prediction, forecasting, and estimation (Liao, 2003). Moreover, data mining techniques are able to efficiently handle a large number of records and data (Ravisankar, Ravi, Raghava Rao, & Bose, 2011). Compared to general statistics, data mining is able to identify certain patterns and match specific data via efficient computing technology. In other words, the interpretation of data allows flexibility (Liao, 2003). This study employs the association rule of the data mining technique to the VAT database to uncover patterns and relationships among attributes that are useful for identifying problematic tax evasion reports. In this research, a screening model will be developed based on specific patterns or rules discovered from identified VAT evasion tax reports. This screening model is utilized to select the cases that are suspected to be non-compliant VAT reports for further auditing checks. In other words, the goal of using data mining as a technique in detecting VAT evasion enhances the tax auditor’s productivity in recovering tax revenue losses. The current paper is organized as follows. After the introductory section, Section 2 provides a literature review. Section 3 illustrates the proposed framework. Sections 4 and 5 discuss the design and development of the screening model and the experimental results, respectively. Finally, Section 6 provides the conclusion, including the limitations of this study and future implications.

The goal of the current study is to use data mining techniques to identify and select suspicious VAT evasion reports for further auditing. Compared with the manual screening method, the proposed data mining technique is a more scientific and resource-saving approach. Using the data mining technique on a large amount of tax data to derive tax evasion patterns can improve the accuracy rates in screening potential tax evasion reports. Thus, the data mining method can be employed to screen all tax reports and reduce the unnecessary wasting of auditing staff resources. The data mining screening model is designed to help tax auditors personnel perform their tax evasion screening tasks more efficiently, thereby enhancing the productivity of auditing possible tax evasion cases. In addition, by informing taxpayers that their tax reports can be quickly and scientifically analyzed, voluntary compliance rates are expected to improve. This will save valuable resources and improve recovery of tax losses. The current study has three contributions. First, the data mining tool can be supported for filtering possible non-compliant VAT reports. Instead of relying on manual methods and personal judgments in selecting suspicious tax reports, tax authorities now have a more scientific way of identifying possible evaders. Second, although limited studies utilize mining association rules to detect tax evasion, the mining outcome with association rules presented in the current study provides a direction for future research within tax field. Third, the current study has identified specific patterns and significant features of illegal taxpayers. Thus, the tax auditors can combine this method with their professional experience to detect further cases of tax evasion. However, the current study has some limitations. Due to budget limitation, the current study used IBM DBMiner 2.0 as the data mining tool, rather than more advanced software. Other data mining software might be able to identify a more effective association rule to improve tax evasion detection performance. Moreover, this study only filtered out suspicious tax evasion case without processing real auditing.