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#vision2016

First party fraud

Sifting through the noise to find and manage true risk

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Introducing:

#vision2016

2

Barbara Simcox Turnkey Risk Solutions

Kyle Hinsz Experian

Christopher Danese Experian

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Experian Public. #vision2016

FIRST

PARTY

FRAUD

Credit loss or

fraud loss?

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#vision2016 Barbara Simcox

Turnkey Risk Solutions

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#vision2016

Emergence of new threats

Bust Out

Credit abuse

Synthetic

identity Third party and first

party schemes have

the ability to be

interchangeable

Third party fraud has evolved

into first party fraud schemes

The

customer

is the victim

Counterfeit

Lost or stolen

Third party fraud

First party fraud

The

business or

institution

is the

victim

Identity theft

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#vision2016

Scheme was “make up, pump up

and run up”

7,000 fake identities for profit of $200 million

Scheme was simple; advertise on Craigslist to have fictitious identities added to real people’s accounts

Industry trends and impacts

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We believe it is one of the

biggest [bank frauds] the Department

of Justice has ever uncovered.

“

” — Matthew Reilly

New Jersey

U.S. Attorney’s Office

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#vision2016

$200 million first party fraud scheme

ALASKA (USA)

CANADA

UNITED STATES AMERICA

MEXICO

GREENLAND(DENMARK)

ICELAND

FARCA ISLANDS UNITED

KINGDOM

FRANCE

VENEZUELA CENTRAL

AFRICAN

REPUBLIC

DEMOCRATIC

REPUBLIC

OF CONGO

ALGERIA

NIGER MALI

CHAD

SUDAN

ERITREA

TUNISIA

MAURITANIA

WESTERN

SAHARA

SENEGAL

GUINEA

LIBERIA

NAMIBIA

ZIMBABWE

ANGOLA

ZAMBIA

BOTSWANA

SOUTH

AFRICA

KENYA

TANZANIA

CONOGO

CABON

CAMEROON

NIGERIA

BURKINA

FASO

COLOMBOA

GUYANA

SURINAME

FRENCHGUIANA

ECUADOR

PERU

BOLIVIA

BRAZIL

PARAGUAY

ARGENTINA CHILE

URUGUAY

BELIZEA

HONDURAS

NICARAGUA

GUATEMALA

SALVADOR

COSTA RLCA

PANAMA

HAITI

DOM.

CUBU

BAHAMAS

G.B.

SIERRA L. LVORY

COAST

TOGO

BENION

LES.

SWA.

MADAGASCAR

MALAWI

BU.

SWI.

GARMANY BEL.

NORWAY

DENMARK

ETHIOPIA

JAM.

MOZAMBIQUE

SOMALIA

LIBYA EGYPT

SAUDI

ARABIA OMAN

FRANCE

IRAQ IRAN

SYRIA

TURKEY

AFGHANISTAN

INDIA

TURKMENISTAN

UZBEKISTAN

KAZAKHSTAN

SRI LANKA

JORDAN

CYP.

GREECE

LTALY

BUL.

BOS.

HUN.

AUS.

SWI. M.

UKRAINE

POLAND

GARMANY

KUWAIT

QATER

M.

HOL.

MOROCCO

PORTUGAL SPAIN

RUSSIA

MONGOLIA

C H I N A

I N D O N E S I A

A U S T R A L I A

BHUTAN

BANGLADESH

MYANMAR

SRI LANKA

N.KOREA

S.KOREA

JAPAN

TAIWAN LOAS

THAILAND

CAMB. VIETNAM

MALAYSIA

BR.

PAPUA

NEW-GUINEA

NEW-ZEALAND

RW.

SWEDEN

EST.

BELARUS LIT.

LAT.

FINLAND

SLO.

S.

A.

ROMANIA

PAKISTAN

INDIA UAE

8

7,000 synthetic identities

25,000 credit cards

169 bank accounts

1,800 mailing addresses

80 fraudulent merchants

Scope AL, AZ, CA, CT, FL, GA, ID, IL,

IN, IA, KS, KY, ME, MD, MI,

MS, MO, NJ, NY, NC, OH, PA,

TX, UT, VT, VA, WA AND WI

28 states United States of America,

Canada, Romania, Pakistan,

India, United Arab Emirates,

China and Japan

8 countries

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#vision2016

Impacted industries

Financial Services

Consumer and

Business: Auto,

Credit Card,

Mortgages, Merchant

Processors, Retailer,

and Market Place

Lenders

Healthcare

Healthcare

Providers,

Hospitals,

Pharmaceutical

Companies, etc.

Insurance Industry

Auto, Medical,

and Personal

Liability Providers

Government Services

IRS, SSA, GSA,

Medicare/Medicaid,

Fannie Mae, HUD, etc.

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#vision2016

First party fraud implications

Regulatory Reputational Operational Financial

Compliance violations, fines, sanctions, etc.

Money laundering

Terrorism financing

Increased expense due to redundancy of effort

Significant

fraud or credit

losses

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#vision2016

What is in the data?

Definition vs. tagging

First party fraud

Individual receives credit with no intent to pay

Defaults on first payment

Never makes any payment

Unable to contact in collections

Misleading application information

First pay default

Multiple accounts go delinquent

Losses exceed credit limit

Returned payments

Credit limit increase requests

Bust out

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#vision2016

Not all data tells the same story

First

party

fraud

Bureau data

Application information

Identity verification

Identity link analysis

Device intelligence

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#vision2016

Assessing true risk

0%

80%

0% 10%

Pe

rce

nt

Bad

Percent Good

Bust out score

Credit score

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#vision2016

Synthetic ID life cycle

Detecting through analytic tools

Creation

Use

Bust Out

Device intelligence

Identity element link analysis

Identity verification scores

Know your customer

Identity verification scores

Never pay scores

Bust out scores

Device intelligence

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#vision2016 Who has the first

question?

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For additional information,

please contact:

@ExperianVision | #vision2016

Follow us on Twitter:

#vision2016 Kyle.Hinsz@experian.com

Christopher.Danese@experian.com

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