Using data mining technique to enhance tax evasion detection performance

• Published in: Expert systems with applications Vol. 39; no. 10; pp. 8769 - 8777
• Main Authors: Wu, Roung-Shiunn, Ou, C.S., Lin, Hui-ying, Chang, She-I, Yen, David C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2012
• Subjects: Association rule; Data mining; Expert systems; Governments; Screening; Sound filters; Strategy; Tax evasion; Taxes; Value-added tax; VAT; Value-added tax; Data mining; Association rule; Tax evasion
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.01.204

► The data mining tool can be supported for filtering possible non-compliant VAT reports. ► The mining outcome with association rules provides a direction for future research. ► The current study has identified specific patterns and significant features of illegal taxpayers. 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.