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

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

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

3

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

4

Income Tax Fraud

Internal Revenue Service’s Perspective

JODI PATTERSON, IRS

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

6

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

7

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

8

Income Tax Fraud

Financial Institution’s Perspective

TERESA THORNTON

COMERICA BANK

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

10

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

11

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

12

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

13

Tax Fraud Advisories

• Income Tax Fraud Introduction and Current Schemes

Overview

• ACH Schemes / Scenarios

• Check Fraud Schemes

• Prepaid Card Schemes

• Tax Preparers

• Escalation Matrix

14

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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Early Warning Services

A Collaborative Approach to

Mitigating Tax Refund Fraud Losses

GLEN SGAMBATI

EARLY WARNING SERVICES

Early Warning

A Fraud Prevention and Risk Management Company

Ownership Structure

• 100% owned by Bank of America, BB&T, Capital One, JP Morgan Chase and Wells Fargo

Unique Business Model

• Revenue sharing based on value of data provided

• “Give to Get” model allowing access to shared data

• Operating Rules govern use, provision and security of data

• Advisory Committee guides product roadmaps

Who we are What we do

Protect the Balance Sheet

• Reduce losses – deposit, open-to-buy, portfolio monitoring

• Move to earliest point- of-impact

• Expand real-time defense network

Enhance Customer Experience

• Accelerated hold notification

• Account owner authentication

• Customer retention

• Reputation risk

Capture Value of Data Asset

• Protect data asset

• Create value

Data

The National Shared DatabaseSM

• 95% of open and active deposit accounts1

• Largest source of shared data on:

• Consumers who have committed or attempted fraud

• Item level information

• Identity to account matching

• Financial institution employee fraud

Security

• Be the benchmark in data security

Network

• Early Warning’s Risk Intelligence Network

SM

How we do it Who we serve

Financial Institutions

Processors

Check Acceptance Companies

Government

Channel Partners

Financial Institution Segments

• Deposit Risk

• Human Resources

• Credit Cards

• Mortgages

• HELOCs

• LOCs

• Treasury Services

1As of Q4 2011. Coverage is inferred from Early Warning’s ability to respond to all inquiries using the Participant and Scored Account databases. 17

National Shared DatabaseSM

ANNUAL TRANSACTIONS

All Items – 16.5B

Incoming Return – 34.2M

Outgoing Return – 70.7M

Deposit / Payment Inquiries – 3.9B

Identity Verifications – 47.1M

Stop Payments – 27.2M

ACH – 11.0B

ACCOUNTS

Participant Accounts – 479M

Accounts with Owner Records – 228M

DDA / Savings – 219M

CD / IRA – 9M

Scored Accounts – 82M

ENTITIES

Account Owners – 315M

DDA / Savings – 305M

CD / IRA – 11M

Deposit Account Abuse – 43M

Deposit Shared Fraud – 697K

Internal Fraud – 14K

SSN / Name – 3rd Party – 265M

Decedent Data – 92M

Security

Go

vern

ance B

ank

Co

ntr

ol

Trusted Custodian®

OTHER IN PROGRESS Deposit Balances

Credit Card Account Owners

Credit Card Performance Data

Credit Card Account Abuse

Credit Card Shared Fraud

SM

as of Q3 2012

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Tax Refund Fraud Analysis

Jan-May 2012

February, 2013

• 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

Why do this Analysis?

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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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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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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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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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Timing of Account Open and Closing

Tenure

from Open

to Refund ACH Trans Amount

Months

from Refund

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

Open to

Refund

Months from

Refund to

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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Multiple Refunds to a Single

Consumer Address

~0.1% of addresses have ≥ 5 refunds accounting for $57MM. Example shows multiple refunds going to one address linked to 3 bank accounts.

10 Refunds totaling $15K went to this 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

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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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Wrap-Up

• Questions?

Thank you for attending, - Nancy, Jodi, Teresa and Glen

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