1. Using Data Mining for Screening Tax Returns

2. References • 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. • 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. • 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.

3. Problems • Several persons and citizens try to evade tax • Big Corporation as well as smaller ones all do same [3] • 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 [2] • Present Data mining solution by [1]

4. Abusive Tax Shelters Partnership • 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 S Corporation Tax Payer

5. Data Set • Source : Internal Revenue Service • Data Entities

6. 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.

7. Problem 2 • How about Groups of High-income individuals working together though other promoters High-income Individuals Entities selling the tax shelter fraud to individuals Promoter Partnerships organizations

8. 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

9. 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

10. Questions?