Income Tax Fraud: Awareness, Preparedness, Prevention and Detection
1. Income Tax Fraud:
Awareness, Preparedness,
Prevention and Detection
NANCY GUGLIELMO, BITS - MODERATOR
JODI PATTERSON, INTERNAL REVENUE SERVICE
TERESA THORNTON, COMERICA BANK
GLEN SGAMBATI, EARLY WARNING SERVICES
March 13. 2013
2. Agenda
• Nancy Guglielmo
- Introduction of the Tax Fraud Issue
- Initial BITS Efforts
• Jodi Patterson
- The Identity Theft Threat
- IRS Prevention and Detection
- 2013 Outlook
• Teresa Thornton
- Financial Institution Perspective
- BITS Efforts
• Glen Sgambati
‒ EWS Prevention Efforts and Solutions 2
3. Income Tax Fraud - Introduction
• Income Tax Fraud, specifically ‘Refund’ Fraud is on the
rise
• Crime against all of us
• Impacts victim taxpayers
• Impacts Financial Institutions
• Impacts the IRS
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4. Initial BITS Efforts - Outreach to the
IRS
• BITS/Financial Services Roundtable Members reported a sharp
increase in income tax refund fraud Q1 2012
• Issued advisory to Fraud Working Group in March 2012
• Reached out to the IRS to encourage collaboration
– Sent letter to IRS Commissioner in April
– BITS coordinated Financial Institution/IRS Face-to-Face Meetings July
and August
o Discussed ways the IRS can improve fraud detection, automate the return
process and coordinate Hold Harmless Process and what financial institutions
can do to help the IRS
– Developed specific taskforces of BITS Fraud Program members and IRS
representatives for future coordination between Financial Institutions and
IRS
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6. Identity Theft - A Persistent Threat to
Taxpayers
• Identity theft: number one consumer complaint reported to FTC
• Over the past few years, the IRS has seen an increase in refund
fraud schemes in general and those involving identity theft in
particular
• IRS sees two types of ID theft
– Using Social Security numbers of taxpayers who have a filing requirement
– Using Social Security numbers of decedents, minors, elderly, and others
who have no requirement to file a tax return
• IRS developed a comprehensive identity theft strategy focused on:
– Prevention
– Detection
– Victim assistance
– Enforcement
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7. Combating Fraud through Prevention
and Detection
• IRS has implemented a number of new fraud/ID theft filters that all
refund returns go through
• In 2012, IRS stopped more than 3 million fraudulent returns
• Prevented approximately $20 billion worth of bad refunds from being
issued
• Issued 250,000 IP PINS to taxpayer victims to facilitate filing of return
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8. More Detection Capabilities in 2013
• IRS has developed many new filters to address the ever-changing
face of fraud
• New capabilities for addressing duplicate conditions, including bank
accounts and addresses
• Will issue more than 600,000 IP PINS to taxpayer victims
• Will continue to work closely with the financial industry
• Will pilot use of NACHA reject reason codes to protect/recover
revenue identified as mismatch because the name on the account
does not match the return information.
• Will implement two additional NACHA reject reason codes to further
protect/recover revenue identified as fraud or ID theft
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10. Fraud Scenarios
2012 included:
• ACH Returns
• Forged Endorsement
• Identity Theft / Synthetic ID Theft
• Refund Anticipation
• Prepaid Debit Cards
2013 things to consider:
• Tax Preparer Verification/Requirements
• Institution Training
• Account/Customer Review
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11. BITS
Income Tax Refund Fraud Project Team
Key Areas of Collaboration
• Tax Preparer Identification
• Identity Theft / Synthetic ID
• Criminal Investigation and Escalation
• Tax Fraud Education Program
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12. Criminal Investigations and
Escalation
• Exchange information on criminal actors
• Investigations data sharing IRS and Financial
Institutions
• Develop local agency and institution partners
• Data Analytics and external leads
• Programs
‒ Identity Theft
‒ Questionable Refund
‒ Return Preparer
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13. Tax Fraud Education Program
• Collaborate with IRS on marketing and educational
publications
• Engage BITS Security Awareness and Education
Subgroup
• Communications packet from IRS
• Share tax preparers consumer education, irs.gov
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14. Tax Fraud Advisories
• Income Tax Fraud Introduction and Current Schemes
Overview
• ACH Schemes / Scenarios
• Check Fraud Schemes
• Prepaid Card Schemes
• Tax Preparers
• Escalation Matrix
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15. Thank You
Disclaimer: The foregoing suggestions are for informational purposes only. These
suggestions are not intended nor should they be used as an exclusive list of potential
solutions aimed at the detection and prevention of any fraud related risks.
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16. Early Warning Services
A Collaborative Approach to
Mitigating Tax Refund Fraud Losses
GLEN SGAMBATI
EARLY WARNING SERVICES
17. Early Warning
A Fraud Prevention and Risk Management Company
Who we are What we do How we do it Who we serve
Ownership Structure Protect the Balance Sheet Data Financial Institutions
SM
• 100% owned by Bank of • Reduce losses – deposit, The National Shared Database Processors
America, BB&T, Capital One, JP open-to-buy, portfolio
• 95% of open and active deposit Check Acceptance Companies
Morgan Chase and Wells Fargo monitoring
accounts1
Unique Business Model • Move to earliest point- Government
• Largest source of shared
of-impact
• Revenue sharing based data on: Channel Partners
on value of data provided • Expand real-time
• Consumers who have committed Financial Institution Segments
defense network or attempted fraud
• “Give to Get” model allowing
access to shared data Enhance Customer Experience • Deposit Risk
• Item level information
• Operating Rules govern use, • Accelerated hold notification • Human Resources
• Identity to account matching
provision and security of data • Credit Cards
• Account owner authentication • Financial institution employee
• Advisory Committee guides fraud • Mortgages
• Customer retention
product roadmaps
• Reputation risk Security • HELOCs
Capture Value of Data Asset • Be the benchmark in data security • LOCs
• Protect data asset Network • Treasury Services
• Create value • Early Warning’s Risk Intelligence
SM
Network
1As
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of Q4 2011. Coverage is inferred from Early Warning’s ability to respond to all inquiries using the Participant and Scored Account databases.
18. National Shared DatabaseSM
as of Q3 2012
Trusted Custodian®
ACCOUNTS ANNUAL TRANSACTIONS
Participant Accounts – 479M All Items – 16.5B
Accounts with Owner Records – 228M Incoming Return – 34.2M
DDA / Savings – 219M Outgoing Return – 70.7M
CD / IRA – 9M Deposit / Payment Inquiries – 3.9B
Scored Accounts – 82M Identity Verifications – 47.1M
Stop Payments – 27.2M
Bank Control
Governance
ACH – 11.0B
ENTITIES OTHER IN PROGRESS
Account Owners – 315M Deposit Balances
DDA / Savings – 305M SM Credit Card Account Owners
CD / IRA – 11M Credit Card Performance Data
Deposit Account Abuse – 43M Credit Card Account Abuse
Deposit Shared Fraud – 697K Credit Card Shared Fraud
Internal Fraud – 14K
SSN / Name – 3rd Party – 265M
Decedent Data – 92M
Security
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20. Why do this Analysis?
• Hypothesis: Analysis could identify incremental potentially
bad payments not currently defined as high-risk based on
anomalies in Account Ownership and/or matches to
negative shared databases
– Significant increase in tax refund fraud over the last few years
– Early Warning FSO shared data coupled with analytics could help in
identifying high-risk payments
• Payments to known fraudsters/account abusers
• Payments to dead people
• Payments to accounts where the account owner name or other
demographic information doesn’t match the tax payment
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21. Analysis Summary
• Early Warning databases utilized included ACH, Account Owner
Elements, Shared Fraud, and Account Abuse Negative Files
• Data analyzed included ACH transactions only (check deposits are
additional opportunities) being deposited into DDA
• The Analysis included 3.6 billion financial transactions totaling $8.3
trillion that occurred from January 2012 through May 2012
‒ From this Analysis, 15.7 million financial transactions totaling
$43.5 billion were identified as tax refunds
‒ The next step in the Analysis was to match individuals receiving
ACH refunds to the data on the ownership of the Account being
credited:
o Account and routing numbers
o SSN
o Name and Address
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22. Analysis Details
• Following identification of “no-match” individuals, additional analysis
included:
– Matching “no-match” individuals to the SSA Death Master File
– Matching “no-match” individuals to Early Warning’s Fraud and Abuse Negative
File
– Comparing the SSNs, Names and addresses of the remaining “no-match”
individuals and establishing potential risk
– Analyzing the timing of opening and closing of accounts being utilized for
deposit
– Identification of individuals with addresses on accounts that had multiple
refunds deposited
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23. The Results
• 65% coverage on total tax payments match the Account Owner
databases
• In 8% (842,000 transactions totaling $1.9 billion) some type of high-
risk indicator existed
– 177K payments for $373M matched either the SSA Death Master, or the
Early Warning Shared Fraud, Internal Fraud, or Account Abuse databases
– An additional 91K for $181M had mismatches where the
name/SSN/Address did not match the Bank contributed data on file at
Early Warning.
• An additional 56K payments totaling $371M were part of multiple
deposits(3 or more) going into the same account
– 2K had 10+ transactions totaling $26M
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24. Where from here?
• As initially stated, the focus of this Analysis is:
‒ To highlight the concerns of our financial institution customers and
to demonstrate their support for addressing tax refund fraud.
‒ Illustrate the potential of Early Warning’s databases to assist in
identifying requesters who present significant potential risk of
attempting to defraud the government and refer these individuals
for additional investigation prior to the payment of tax refunds.
‒ Offer Early Warning’s support in utilizing its financial institution
contributed data to enhance tools for this purpose.
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25. Timing of Account Open and Closing
Tenure Months
from Open from Refund
to Refund ACH Trans Amount to Closing ACH Trans Amount
0-1m 61,747 $ 167,917,016 0-1m 88,364 $ 237,237,813
2-3m 130,981 $ 314,209,671 2-3m 121,189 $ 295,841,383
4-6m 191,495 $ 457,175,541 4-6m 288,252 $ 723,346,324
6-12m 453,157 $ 1,055,973,800 7-9m 142,901 $ 392,493,920
12-24m 851,255 $ 2,057,207,770 Unmatched 15,009,605 $ 41,885,600,000
25-36m 912,861 $ 2,299,659,169 Total 15,650,311 $44B
37-48m 856,117 $ 2,191,069,172
>48m 6,698,882 $ 19,536,400,000
UnMatched 5,493,816 $ 15,448,990,000
Total 15,650,311 $44B
Tenure from Months from
Open to Refund to
Refund Closing ACH Trans Amount Amount/Trans
0-1m 0-1m 1,394 $ 4,215,711 $ 3,024
2-3m 0-1m 1,594 $ 3,847,356 $ 2,414
Customers who open an account soon before a Tax Refund (within 3 months) and close
within 1 month could be candidates for a performance risk indicator where bank opening
information is a predictor.
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26. Multiple Refunds to a Single
Consumer Address
# Refunds/Address # Addresses % Addresses Amount
10+ 1,696 0.02% $26,375,298
9 288 0.00% $2,430,191
8 396 0.00% $3,271,531
7 504 0.01% $4,448,052
6 839 0.01% $7,096,991
5 1,642 0.02% $13,822,077
4 6,095 0.06% $46,817,836
3 44,925 0.48% $266,929,984
2 479,270 5.08% $2,109,679,557
1 8,901,598 94.32% $25,114,830,000
10 Refunds totaling $15K went to this address
~0.1% of addresses have ≥ 5 refunds accounting for $57MM. Example shows multiple refunds
going to one address linked to 3 bank accounts.
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27. Resources
• Internal Revenue Service
http://www.irs.gov/uac/Tax-Fraud-Alerts
‒ Identity Theft: http://www.irs.gov/uac/Identity-Protection
‒ Tax Preparer Information: http://www.irs.gov/for-Tax-Pros
‒ NACHA Opt In information: https://www.nacha.org/node/1271
• American Bankers Association
http://www.aba.com/Solutions/Fraud/Pages/TaxRefundFraud.aspx
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