Presentation on theme: "Using Data Mining for Screening Tax Returns. References 1.Roung-Shiunn Wu, C.S. Ou, Hui-ying Lin, She-I Chang, David C. Yen, Using data mining technique."— Presentation transcript:
References 1.Roung-Shiunn Wu, C.S. Ou, Hui-ying Lin, She-I Chang, David C. Yen, Using data mining technique to enhance tax evasion detection performance, Expert Systems with Applications, Volume 39, Issue 10, August 2012, Pages 8769-8777, ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2012.01.204. http://dx.doi.org/10.1016/j.eswa.2012.01.204 2.Keith Blackburn, Niloy Bose, Salvatore Capasso, Tax evasion, the underground economy and financial development, Journal of Economic Behavior & Organization, Volume 83, Issue 2, July 2012, Pages 243-253, ISSN 0167-2681, http://dx.doi.org/10.1016/j.jebo.2012.05.019.http://dx.doi.org/10.1016/j.jebo.2012.05.019 3.Show-Jane Yen, Yue-Shi Lee, An efficient data mining approach for discovering interesting knowledge from customer transactions, Expert Systems with Applications, Volume 30, Issue 4, May 2006, Pages 650-657, ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2005.07.035.
Problems Several persons and citizens try to evade tax Big Corporation as well as smaller ones all do same  Sources of fraud Unreported income Abusing tax Shelters Several Solutions have been proposed and used to detect fraudulent tax activity Some manual and others Data mined  Present Data mining solution by 
Abusive Tax Shelters Non-declaration of Income A lot has been done about this Abusive Tax Shelters Tax payer makes some huge gain Tax advisor(promoter) helps to exploit the loophole in the tax law Set up a partnership together Tax payers buys call options and transfers to partnership Call option is sold by tax payer Ignores liability Sale results in tax payer claims of the same amount of loss Loss offsets the original gains Partnership S Corporation Tax Payer
Data Set Source : Internal Revenue Service Data Entities Entity NameInstances in 2003(mil)Number of Attributes High-Income tax-payer1.91000 Trust3.5200 Partnership2.5100 Sub-chapter S Corporation3.4100
Solution Built a single-class Model using Support Vector Machine (SVM) Results Successfully identified and ranked some transactions are fraudulent. Revealed $200 mil of previously uncovered tax shelter losses Although 90% accuracy gained Transactions were missed Improved Model was built by relaxing the target criteria Based on expert domain information Resulted in Shelter Risk Function Improved identification of further losses.
Problem 2 How about Groups of High-income individuals working together though other promoters Promoter Entities selling the tax shelter fraud to individuals High-income Individuals Partnerships organizations
Solution Modify SRF to have groups of SRV New Model: Promoter Risk Function In view of Speed of operations, Irrelevant links in the mined relationships were pruned Filtering and merging of groups Based on promoters levels of support in group
Overall Results Found 500 meta-groups of potential promoters and individuals (SSNs) involved in the tax shelter fraud Savings of $5bil of sheltered income 50% of the amount was associated with the top 20% of the groups and meta groups identified The process is automated and not as laborious as the other manual processes