Data Breaches & Identity Theft Report 2013

Insights Report Brochure

2013 IDENTITY FRAUD REPORT:

Data Breaches Becoming a Treasure Trove for Fraudsters

Fe brua ry 20 13

T +1 925 225 9100 · F +1 925 225 9101 · 4301 Hacienda Dr. · Pleasanton, CA 94588 USA · inquiry@javelinstrategy.com · www.javelinstrategy.com



A
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Javelin Strategy & Research, a division of
Greenwich Associates, provides strategic
insights into customer transac ons,
increasing sustainable profits for financial
ins tu ons, government, payments
companies, merchants and other technology
providers.
A
:
Alphonse Pascual, Senior Analyst, Security,
Risk and Fraud
Sarah Miller, Analyst, Security, Risk and Fraud

R
:
Lorie Cur s, Research Associate
James Jarzab, Research Specialist
Aleia Van Dyke, Analyst
Daniel Van Dyke, Research Associate
Paul Wangsvick, Research Specialist

C
:
Jim Van Dyke, CEO
Mary Monahan, Execu ve Vice President and
Research Director

P
D :   February 2013

P
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$3,000 ‐ Department license

$5,500 ‐ Enterprise license

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82 pages

56 charts/graphs

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Iden ty fraud incidence increased in 2012 for the second
consecu ve year, affec ng 5.26% of U.S. adults. This increase
was driven by drama c jumps in the two most severe fraud
types, new account fraud (NAF) and account takeover fraud
(ATF). Javelin’s “2013 Iden ty Fraud Report” provides a
comprehensive analysis of fraud trends in the context of a
changing technological and regulatory environment in order to
inform consumers, financial ins tu ons, and businesses on the
most effec ve means of fraud preven on, detec on, and
resolu on. This year, Javelin conducted a thorough explora on
of the rela onship between the compromise of personal
informa on in a data breach and fraud incidence. This report
also expounds current trends in online retail fraud and familiar
fraud, and implicates key factors in vic ms’ suscep bility and
responses to fraud. “2013 Iden fy Fraud Report” data was
gathered by a survey of a representa ve sample of 5,249 U.S.
adults, including 857 consumers who were fraud vic ms in the
past six years. This report has been issued as a longitudinal
update to the Javelin 2005, 2006, 2007, 2008, 2009, 2010, 2011
and 2012 iden ty fraud reports, and the Federal Trade
Commission’s “2003 Iden ty The Survey” report.

The survey was made possible in part by Ci Group Inc.,
Intersec ons LLC, and Visa Inc.  To preserve the project’s
independence and objec vity, the sponsors of this project were
not involved in the tabula on, analysis, or repor ng of final
results.

© Copyright 2013 Javelin Strategy & Research, a division of Greenwich Associates.  All rights reserved. It is protected by copyright and other intellectual property laws. You may display or
print the content available for your use only. You may not sell, publish, distribute, re‐transmit or otherwise provide access to the content of this report.

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M

The 2013 Javelin “Iden ty Fraud Survey Report” provides consumers and businesses an in‐depth and comprehensive examina on of
iden ty fraud in the United States. Its purpose is to help readers understand the causes and incidence rates of iden ty fraud and the
success rates of methods used for its preven on, detec on, and resolu on.

This report builds on Javelin’s annually published “Iden ty Fraud Survey Report” and the Federal Trade Commission’s “2003 Iden ty
The Survey Report.”

2012 S

D

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Javelin’s ID fraud survey was historically fielded as a landline survey using computer‐assisted telephone interviewing (CATI). At the
me of the survey’s incep on in 2003, landlines provided a rela vely comprehensive coverage of the U.S. popula on. However, with
advent of me and technology, landline coverage has been shrinking — thus the ID fraud survey has had increasingly less penetra on
into the younger, more‐mobile popula on. Cognizant of this shi , in 2011 Javelin fielded the ID fraud survey through the
KnowledgePanel®. Javelin con nued to use KnowledgePanel for its 2012 and 2013 ID fraud surveys in order to obtain the most
representa ve sample of U.S. adults.

KnowledgePanel is the only probability‐based online panel in the U.S. The panel recruits households with no access to Internet (at the
me of recruitment) as well as cell‐phone‐only households, through mail recruitment. The panel offers a mix of RDD‐based
recruitment (1999–present) and address‐based sampling (introduced in 2008 with a full rollout in 2009).

The 2013 ID fraud survey was conducted among 5,249 U.S. adults over age 18 on KnowledgePanel; this sample is representa ve of
the U.S. census demographics distribu on, recruited from the Knowledge Networks panel. Data collec on took place between
September 20 and October 12, 2012. Final data was weighted by Knowledge Networks, while Javelin was responsible for data
cleaning, processing, and repor ng. Data is weighted using 18+ U.S. Popula on Benchmarks age, gender, race/ethnicity, educa on,
census region, and metropolitan status from the most current CPS targets.

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Given the departure from the previous computer‐assisted telephone interviewing (CATI) through random digit dialing (RDD), Javelin
also fielded a parallel CATI survey among 1,000 18+ adults in 2011 through Opinion Access in order to derive calibra on es mates to
trend and update longitudinal data. For all ques ons asked of the en re popula on, 2003–2010 data has been calibrated using ra os
derived from comparing 2011 CATI vs. web‐based survey. For other ques ons affec ng only a subset of the popula on, a three‐year
rolling average has been applied to smooth year‐over‐year trending data.


3




In adherence with best prac ces, in 2011 Javelin also moved from bracketed dollar amount calcula ons to true open‐end numerical
dollar calcula ons. On con nuous variables captured from numerical open‐ended items, extreme outliers were iden fied using a
standard rule of approximately two standard devia ons above the mean to retain consistency year over year. These extreme outliers
were replaced with mean values to minimize their dispropor onate impact on final weighted es mates. Where responses pertained
to a range in value (e.g., “one day to less than one week”), the midpoint of the range, rounded up to the nearest whole unit, was
used to calculate the median or mean value. For example: If the response selected for number of days to detec on was one day to
less than one week, the assigned value would be the median of one day and seven days, inclusive, or four days. To ensure consistency
in comparing year‐to‐year changes, historical figures for average fraud amounts have been adjusted for infla on using the Consumer
Price Index.

Due to rounding errors, the percentages on graphs may add up to 100% plus or minus 1%.

C

F

FTC

With one excep on, this report con nues to classify fraud within the three categories originally defined by the FTC in 2003. For 2005
and beyond, debit card fraud has been recategorized as exis ng card account fraud50 instead of exis ng non‐card account fraud.51
Javelin believes this change reflects a more accurate representa on of debit card fraud, because much of its means of compromise,
fraudulent use, and detec on methods parallel those of credit cards.

The categories of fraud are listed below from least to most serious:


Exis ng card account: This category includes both the account numbers and/or the actual card for exis ng credit and card‐
linked debit accounts. Prepaid cards were added for 2007 and subsequently removed due to extremely low incidence.



Exis ng non‐card account: This category includes exis ng checking and savings accounts, and exis ng loans, insurance,
telephone, and u li es accounts.



New account and other fraud: This category includes new accounts or loans for commi ng the , fraud, or other crimes
using the vic m's personal informa on.

Many vic ms experience iden ty fraud within more than one of these categories. In repor ng the overall incidence rates of the three
categories or types of accounts, the vic ms of crimes to more than one type of account are categorized based on the most serious (as
designated by the FTC) problem reported. Thus, vic ms who reported that new accounts had been opened using their informa on
and also that their exis ng credit cards had been misused would be placed in the new account and other fraud classifica on, not in
the exis ng card account classifica on. This categoriza on is applicable only for repor ng the rates of the three types of fraud.


4




D

F

FTC

2003 M

R

When the report cites vic ms’ average financial damages or resolu on mes in dollars or hours, the en re amount of damages or
losses is placed into every type of fraud the vic ms suffered. For example, for a vic m who reports that a total of $100 is obtained for
both new account and other fraud category and exis ng card account, the $100 is counted in both categories. This method of
repor ng costs by types of fraud will not change the overall total costs of fraud across all three categories, but the average in dollars
or me associated in the three types of fraud should not be summed because the result will be overlapping amounts.

S

Q

The set of ques ons and underlying methodology used for this report were iden cal to or highly similar to those in the 2011, 2010,
2009, 2008, 2007, 2006, 2005, 2004, and 2003 surveys. Some structural adjustments were made in 2011 to adapt CATI‐based
ques ons to web‐based ques ons. All changes were made under the purview of experienced methodologists. Therefore, Javelin
con nues to provide longitudinal trends on various subjects, such as incidence rates and detec on methods.

In addi on, Javelin added a number of discreet new ques ons in 2012 to further explore the significance of past responses as well as
to iden fy new trends around emerging technologies. In par cular, Javelin sought to gain a be er understanding of consumer
behaviors regarding organiza ons contacted a er fraud incidence.

M

E

The ID fraud report es mates key fraud metrics for the current year using a base of consumers experiencing iden ty fraud in the past
six years.52 Other behaviors are reported based on data from all iden ty fraud vic ms in the survey (i.e., based on fraud vic ms
experiencing fraud up to six years ago) as well as total respondents, where applicable.

For ques ons answered by all 5,249 respondents, the maximum margin of sampling error is +/‐ 1.35 percentage points at the 95%
confidence level. For ques ons answered by all 857 iden ty fraud vic ms, the maximum margin of sampling error is +/‐ 3.35
percentage points at the 95% confidence level.

S

O

The project was made possible in part by Ci Group Inc., Intersec ons LLC and Visa Inc. To preserve the project’s independence and
objec vity, the sponsors of this project were not involved in the tabula on, analysis, or repor ng of final results.


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Table of Contents
OVERVIEW ....................................................................................................................................................................................
6
INTRODUCTION ............................................................................................................................................................................
.
7
Execu ve Summary ........................................................................................................................................................
7
Major Findings .................................................................................................................................................
.
7
Recommenda ons ....................................................................................................................................................... 1
.
1
Consumer Recommenda ons for Preven on, Detec on and Resolu on™ of Iden ty Fraud ...................... 1
.
1
Recommenda ons for Financial Ins tu ons.................................................................................................. 3
1
Recommenda ons for Merchants ................................................................................................................. 5
1
QUICK REFERENCE GUIDE ........................................................................................................................................................... 7
1
FRAUD TYPES .............................................................................................................................................................................. 1
2
New Account Fraud ...................................................................................................................................................... 1
2
Compromising Iden

es ................................................................................................................................ 2
2

Fraudsters Prefer New Card Accounts ........................................................................................................... 3
2
Exis ng Account Fraud ................................................................................................................................................. 4
2
Exis ng Card Fraud ....................................................................................................................................................... 5
2
Payment Card Data Targeted Through Mul ple Vectors ............................................................................... 6
2
Addressing Fraud A empts ............................................................................................................................ 8
2
The Eﬀect of EMV ........................................................................................................................................... 8
2
Exis ng Non‐Card Fraud ............................................................................................................................................... 0
3
Account Takeover ......................................................................................................................................................... 2
3
Accessing Exis ng Accounts ........................................................................................................................... 3
3
Accounts Targeted and the Eﬀect on Consumers .......................................................................................... 5
3
Familiar Fraud ............................................................................................................................................................... 7
3
Despite Changes in Fraud Trends, Familiar Fraud Pa erns Remain Constant ............................................... 7
3
Mo va on and Opportunity .......................................................................................................................... 8
3
One‐Stop Shopping: Access to PII and the Severity of Familiar Fraud ........................................................... 9
3
PREVENTION ............................................................................................................................................................................... 2
4
Data Breaches: Precursors to Fraud ............................................................................................................................. 2
4
Online Retail Fraud ....................................................................................................................................................... 4
4
Mobile Consumers Are Vulnerable Targets .................................................................................................................. 7
4
Social Media ................................................................................................................................................................. 0
5
Risky Prac ces on Social Networking ............................................................................................................. 0
5
DETECTION ................................................................................................................................................................................. 2
.
5
Methods of Detec on .................................................................................................................................................. 2
5
Means of Detec on by Fraud Type ................................................................................................................ 2
5
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Table of Contents
Means of Detec on Among Exis ng Debit and Credit Card Fraud Vic ms ................................................... 4
.
5
Effect of Common Fraud Detec on Types on FI Fraud Vic m Reten on ...................................................... 5
5
Length of Fraudulent Ac vity Before Detec on by Detec on Methods ....................................................... 6
.
5
Detec ng Familiar Fraud: Perpetrators Represent Camouflaged Threat ..................................................................... 6
5
RESOLUTION ............................................................................................................................................................................... 8
5
Fraud Resolu on Rates Reach All‐Time High................................................................................................................ 8
5
Resolu on by Fraud Type ............................................................................................................................................. 9
5
Exis ng Card Fraud Resolu on Is Quicker Because the Process Is Streamlined ............................................ 0
6
Exis ng Non‐Card Fraud Resolu on ............................................................................................................... 1
6
New Account Fraud Resolu on ...................................................................................................................... 2
6
Account Takeover Fraud Resolu on .............................................................................................................. 3
6
Severity of Fraud and Resolu on Ac ons .................................................................................................................... 4
.
6
Lower‐Income Consumers Are More Severely Affected by Fraud ................................................................. 4
6
Low‐Income Consumers Know the Perpetrators and Take Legal Ac on ....................................................... 6
6
Fraud Severity and Responses to Fraud ......................................................................................................... 8
6
Demographic Determinants of Resolu on Ac on ........................................................................................................ 9
6
Consumer Responses to Fraud Depend on Age ............................................................................................. 9
6
APPENDIX .................................................................................................................................................................................... 2
7
METHODOLOGY .......................................................................................................................................................................... 4
7
2012 Survey Data Collec on ........................................................................................................................................ 4
.
7
Longitudinal Trending ................................................................................................................................................... 4
7
Categorizing Fraud by FTC methodology ...................................................................................................................... 5
7
Devia on From FTC and 2003 Methodology and Repor ng ........................................................................................ 5
7
Survey Ques onnaire ................................................................................................................................................... 6
7
Margin of Error ............................................................................................................................................................. 6
7
Contribu ng Organiza ons .......................................................................................................................................... 6
7
GLOSSARY ................................................................................................................................................................................... 7
7
RELATED RESEARCH .................................................................................................................................................................... 0
8
COMPANIES MENTIONED .......................................................................................................................................................... 82

7




Table of Figures
Figure 1: Overall Iden ty Fraud Incidence Rate and Total Fraud Amount by Year ....................................................................... 7
Figure 2: Breakdown of Iden ty Crime Types ............................................................................................................................. 17
Figure 3: Overall Measures of the Impact of Iden ty Fraud, 2004–2012.................................................................................... 18
Figure 4: Iden ty Fraud Overview: Exis ng Account Fraud ........................................................................................................ 18
.
Figure 5: Iden ty Fraud Overview: Exis ng Card Fraud .............................................................................................................. 19
Figure 6: Iden ty Fraud Overview: Exis ng Non‐Card Fraud ...................................................................................................... 19
Figure 7: Iden ty Fraud Overview: New Account Fraud ............................................................................................................. 20
Figure 8: Iden ty Fraud Overview: Account Takeover Fraud ...................................................................................................... 20
Figure 9: New Account Fraud Incidence and Total Fraud Amount by Year ................................................................................. 21
Figure 10: Types of New Fraudulent Accounts Opened .............................................................................................................. 23
Figure 11: Exis ng Account Fraud Incidence and Total Fraud Amount by Year .......................................................................... 24
Figure 12: Exis ng Card Fraud Incidence and Total Fraud Amount by Year ................................................................................ 25
Figure 13: Type of Exis ng Card Misused by Age ........................................................................................................................ 26
Figure 14: Type of Personal Informa on Compromised in a Data Breach .................................................................................. 27
Figure 15: Road Map for EMV Migra on And Shi ing Liability ................................................................................................... 29
Figure 16: Exis ng Non‐Card Fraud Incidence and Total Fraud Amount by Year ........................................................................ 30
Figure 17: Consumer Out‐of‐Pocket Costs As a Percent of Fraud Losses .................................................................................... 31
Figure 18: Account Takeover Fraud Incidence and Total Fraud Amount by Year ....................................................................... 32
Figure 19: Informa on Changed on Accounts Taken Over .......................................................................................................... 33
Figure 20: Recent Use of Security So ware ................................................................................................................................ 34
Figure 21: Types of Accounts Taken Over ................................................................................................................................... 35
Figure 22: Type of Card Account Misused Among Account Takeover Vic ms and All Fraud Vic ms ......................................... 36
Figure 23: Key Fraud Metrics among Familiar Fraud and Non Familiar Fraud Vic ms ............................................................... 37
.
Figure 24: Personal Acquaintance With the Perpetrator by Annual Household Income ............................................................ 38
Figure 25: Severity of Eﬀec ve Fraud by Familiar Fraud Vic ms, All Fraud Vic ms.................................................................... 39
Figure 26: Type of PII Compromised Among Familiar Fraud Vic ms, All Fraud Vic ms ............................................................. 40
.
Figure 27: Fraud Incidence by Data Breach Vic ms, Non‐Data‐Breach Vic ms and All Fraud Vic ms ....................................... 42
Figure 28: Incidence of Fraud Types by Type of Informa on Breached ...................................................................................... 43
Figure 29: Means of Misuse of Fraud Vic ms’ Informa on 2010–2012 ..................................................................................... 44
.
Figure 30: Online Retail Fraud Incidence vs. POS Fraud Incidence 2005–2012 .......................................................................... 45
Figure 31: Type of Exis ng Card Misused for Fraudulent Online vs. In‐Person Purchases ......................................................... 46
.
Figure 32: Mobile Consumers’ Percep ons of the Riskiness of Behaviors .................................................................................. 47
Figure 33: How Recently Mobile Consumers Have Downloaded Apps to their Mobile Device .................................................. 48
Figure 34: Fraud Incidence by Ownership of Tech Products ....................................................................................................... 49
Figure 35: Incidence Rate by Social Networking Ac vity, 2012 ................................................................................................... 51
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Table of Figures
Figure 36: Means of Fraud Detec on by Fraud Type .................................................................................................................. 52
Figure 37: Means of Fraud Detec on by Credit and Debit Card Vic ms ..................................................................................... 53
Figure 38: Fraud Vic ms Who Switched Their FI or Credit Card Provider by Detec on Method................................................ 54
Figure 39: Mean Detec on Time by Fraud Detec on Method.................................................................................................... 55
Figure 40: Detec on Times for All Fraud Vic ms vs. Familiar Fraud Vic ms .............................................................................. 56
Figure 41: Means of Discovery of Fraud by All Fraud Vic ms vs. Familiar Fraud Vic ms ........................................................... 57
Figure 42: Percent of Fraud Vic ms Who Have Completely Resolved Their Fraud ..................................................................... 58
Figure 43: Percent of Vic ms Who Have Resolved Their Fraud by Fraud Type, 2011 and 2012 ................................................. 59
Figure 44: Number of Organiza ons Contacted for Assistance by Fraud Type ........................................................................... 60
Figure 45: Resolu on Time by Fraud Type .................................................................................................................................. 61
Figure 46: Organiza ons Contacted by Fraud Type .................................................................................................................... 62
.
Figure 47: Severity of Eﬀect of Fraud by Fraud Type .................................................................................................................. 63
.
Figure 48: Fraud Amounts and Consumer Costs As a Percent of Annual Household Income ..................................................... 64
Figure 49: Severity of Eﬀect of Fraud by Annual Household Income .......................................................................................... 65
Figure 50: Agencies Contacted by Familiar Fraud Vic ms and All Fraud Vic ms ........................................................................ 66
Figure 51: Legal Ac ons Taken by Familiar Fraud Vic ms and All Fraud Vic ms ........................................................................ 67
Figure 52: Responses to Fraud by Fraud Amount and Resolu on Hours .................................................................................... 68
Figure 53: Organiza ons Contacted by Severity of Fraud ........................................................................................................... 69
Figure 54: Fraud Vic ms’ Responses to Fraud by Age ................................................................................................................. 70
Figure 55: Vic ms’ Ac ons as a Result of Fraud by Year ............................................................................................................. 72
Figure 56: Types of Merchants Fraud Vic ms Avoid ................................................................................................................... 73

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E

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for the shortest period of me (48 days), and more of these cases

The incidence of iden ty fraud in the U.S. con nues to rise,

are being resolved (92% resolu on rate) than at any me in the
past seven years.

rebounding from a sharp decline between 2009 (6.0%) and 2010

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(4.35%), and increasing further from 4.9% in 2011 to 5.26% in

Major Findings

2012. Iden ty fraud affected 12.6 million consumers in 2012, and

New Account Fraud Increasingly Threatens Consumers and
Businesses

Having increased by 50%, from 0.82% of all adults in 2011 to 1.22%

at least half did not suffer any out‐of‐pocket costs (median cost to
consumers of $0) nor did they spend much me resolving these

cases (median resolu on me of three hours). That is not to say

that all cases of fraud have equally limited nega ve effects — the
mean cost to consumers as a result of fraud actually increased

from $354 in 2011 to $365 in 2012, with the bulk of the fraud costs
being borne by financial ins tu ons, merchants, and other
businesses. The mean resolu on me remains unchanged at 12
hours. On a posi ve note, consumer informa on is being misused

in 2012, NAF poses a growing threat to consumer iden

es and

private industry’s bo om line — especially as the total fraud loss
has doubled from 2011, to $9.8 billion. Monitoring of credit reports

can help detect many of these cases, as 57% of NAF cases involved
the establishment of new general‐use and store‐branded credit
cards.

Iden ty Fraud Rises for Second Consecu ve Year, Affec ng 5.26% of Consumers and Cos ng $20.9 Billion
Figure 1: Overall Iden ty Fraud Incidence Rate and Total Fraud Amount by Year
10%

$35
$32.0
$30

$31.4

9%

$28.9

$28.7

8%
$24.7

7%
$19.9

$20
$15

$20.9

5.04%

5.44%
4.71%

4.90%

4.51%

6%

$18.0

6.00%

5.26%

5%
4%

4.35%*

Percent of consumers

Billions in U.S. Dollars

$25

3%

$10

2%
$5

1%

$0

0%

2005

2006

2007

2008

2009

*Significance tested against 2012; Blue significantly lower, Red significantly higher.

© Copyright 2013 Javelin
division of Greenwich
reserved. It is protected

2010

2011

2012

October 2005 through 2012,
n= varies 4,784 ‐ 5,249.
Base: All Consumers.
© 2013 Javelin Strategy & Research

Strategy & Research, a
Associates.  All rights
by copyright and other
10




PREVENTION

D

B

: P

F

properly in its wake are necessary steps in the ba le against this

The most essen al of tools required to commit iden ty fraud is the

insidious criminal prac ce.

iden ty itself. In the past, this informa on was obtained a variety

No fying consumers of a breach has become common prac ce in

of ways that have included roo ng through vic ms’ trash, stealing

an effort to keep consumers informed, and in some instances as

records from their health care provider’s office, or manipula ng

necessitated by law. These no fica ons are generated by the

vic ms through social engineering. Advancement comes to crime,

organiza on that suffered the data breach or by the FI associated

as it does to most things in which a profit can be made, and data

with the account informa on that was breached. In keeping with

breaches have become the means de rigueur for pilfering

the trend of the past few years, consumers who were no fied that

voluminous amounts of consumer informa on with far less risk

they were vic ms of a data breach in 2012 were significantly more

than previous methods. The type of PII secured during these

likely to be vic ms of fraud than they were in 2011, with a fraud

breaches lend themselves to commi ng par cular types of fraud,

incidence rate of 22.5% (see Figure 27) compared with 5.3% of all

and criminals are using the breached informa on to do just that —

consumers.

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breaches beget fraud. Protec ng against a breach and responding

Increase in Fraud Rate Among Data Breach Vic ms Outpaces Increase in Overall Fraud Rate
Figure 27: Fraud Incidence by Data Breach Vic ms, Non‐Data‐Breach Vic ms and All Fraud Vic ms
25%
22.5%
18.9%

20%

15%
11.8%
All consumers
10%
Non data breach victims
5%

5.3%

4.9%

4.4%
1.4%

2.4%

2.9%

2011

Data breach victims

2012

0%
2010

Q2. In the last 12 months, have you been notified by a business
or other institution that your personal or financial information has
been lost, stolen or compromised in a data breach?

October 2010 ‐ 2012, n = varies 337 ‐ 5,249
Base: All consumers, data breach
victims, non data breach victims.


11



The number of mobile malware programs is growing at a rapid

fraudsters with a backdoor to transmissions carrying sensi ve

pace (see Account Takeover sec on, pg. 32, but consumers have

consumer data. Airports, hotels, and coffee shops are loca ons

the means to mi gate these threats. The successful malware that

that frequently offer Wi‐Fi hotspots as a convenience. Consumers

takes advantage of weaknesses in mobile device opera ng systems

could mistakenly connect to hotspots that appear to be legi mate

or other apps can be mi gated by installing the latest so ware

as they bear names similar to other business in the same loca on

patches and keeping opera ng systems up to date. Security

or they have names such as “Free Wi‐Fi” but are in fact controlled

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so ware, which 64% of smartphone owners have displayed a
43

by fraudsters. Once the consumer’s device is connected, any

willingness to use, can proac vely stop malware from being

transmi ed data is intercepted and may later be misused to

installed and eliminate any malware already on the device.

commit iden ty fraud. Such a scenario could be responsible for a

When a mobile network data connec on is either unavailable or

fraud incidence rate among public Wi‐Fi users that is 45% higher
than for those who have not used public hotspots.

inconvenient, consumers may choose to use public Wi‐Fi hotspots

as an alterna ve, but not all hotspots are created equal in terms of
security. While only 40% of mobile consumers consider such

Among technology product owners, tablet owners are most likely
to suffer from iden ty fraud compared with all other consumers
(see Figure 34), though this is likely a func on of mul ple factors.

ac vity risky (see Figure 32) public Wi‐Fi hotspots can provide

Tablet Owners More Than 80% More Likely Than All Other Consumers to Become Fraud Vic ms

Fraud incidence rate

Figure 34: Fraud Incidence by Ownership of Tech Products

15%
14%
13%
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%

9.6%

5.3%

5.6%

All consumers

Mobile phone
owners

6.0%

6.5%

6.3%

Laptop owners Desktop computer
owners

Q39A: Please indicate which of the following products do you personally own and use. Q5:
How long ago did you DISCOVER that your personal or financial information had been
misused?

Smartphone
owners

Tablet owners

October 2012, n = varies 1,062 to 5,249.
Base: All consumers, owners of various products.


12




Companies Mentioned

Amazon

MasterCard

American Express

McAfee

Apple

Microsoft

Discover

PayPal

EBay

Target

Europay

Trend Micro

Facebook

Visa

Global Payments

Wal‐Mart

Google

Zappo’s

Macy’s
13




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Data Breaches & Identity Theft Report 2013

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Data Breaches & Identity Theft Report 2013