Using data mining technique to enhance tax evasion detection performance

被引:67
|
作者
Wu, Roung-Shiunn [2 ]
Ou, C. S. [3 ]
Lin, Hui-yng [3 ]
Chang, She-I [3 ]
Yen, David C. [1 ]
机构
[1] Miami Univ, Dept DSC & MIS, FSB, Oxford, OH 45056 USA
[2] Natl Chung Cheng Univ, Dept Informat Management, Chiayi, Taiwan
[3] Natl Chung Cheng Univ, Dept Accounting & Informat Technol, Chiayi, Taiwan
关键词
Data mining; Value-added tax; Tax evasion; Association rule; ASSOCIATION RULES; KNOWLEDGE DISCOVERY;
D O I
10.1016/j.eswa.2012.01.204
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:8769 / 8777
页数:9
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