Using data mining technique to enhance tax evasion detection performance

Authors:
Roung-Shiunn Wu;C. S. Ou;Hui-ying Lin;She-I Chang;David C. Yen
Affiliations:
Department of Information Management, National Chung Cheng University, 168 University Rd., Min-Hsiung, Chia-Yi, Taiwan, ROC;Department of Accounting and Information Technology, National Chung Cheng University, 168 University Rd., Min-Hsiung, Chia-Yi, Taiwan, ROC;Department of Accounting and Information Technology, National Chung Cheng University, 168 University Rd., Min-Hsiung, Chia-Yi, Taiwan, ROC;Department of Accounting and Information Technology, National Chung Cheng University, 168 University Rd., Min-Hsiung, Chia-Yi, Taiwan, ROC;Department of DSC and MIS, Miami University, 2042C, FSB, Miami University, Oxford, OH 45056, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.