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2013 Identity Fraud Report: Data Breaches Becoming a Treasure Trove for Fraudsters - Product Image

2013 Identity Fraud Report: Data Breaches Becoming a Treasure Trove for Fraudsters

  • ID: 2419354
  • February 2013
  • 82 Pages
  • Javelin Strategy & Research

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Identity fraud incidence increased in 2012 for the second consecutive year, affecting 5.26% of U.S. adults. This increase was driven by dramatic jumps in the two most severe fraud types, new account fraud (NAF) and account takeover fraud (ATF). Javelin's “2013 Identity 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 institutions, and businesses on the most effective means of fraud prevention, detection, and resolution.

This year, Javelin conducted a thorough exploration of the relationship between the compromise of personal information 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 victims' susceptibility and responses to fraud. “2013 Identify Fraud Report” data was gathered by a survey of a representative sample of 5,249 U.S. adults, including 857 consumers who were fraud victims 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 identity fraud reports, and the Federal Trade Commission's “2003 Identity Theft Survey” report.

The survey was made possible in part by CitiGroup Inc., Intersections LLC, and Visa Inc. To preserve the project's independence and objectivity, the sponsors of this project were not involved in the tabulation, analysis, or reporting of final results.

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FEATURED COMPANIES

  • Amazon
  • Discover
  • Facebook
  • Macy's
  • Microsoft
  • Trend Micro
  • MORE

OVERVIEW

INTRODUCTION
Executive Summary
Major Findings
Recommendations
Consumer Recommendations for Prevention, Detection and Resolution™ of Identity Fraud
Recommendations for Financial Institutions
Recommendations for Merchants

QUICK REFERENCE GUIDE

FRAUD TYPES
New Account Fraud
Compromising Identities
Fraudsters Prefer New Card Accounts
Existing Account Fraud
Existing Card Fraud
Payment Card Data Targeted Through Multiple Vectors
Addressing Fraud Attempts
The Effect of EMV
Existing Non-Card Fraud
Account Takeover
Accessing Existing Accounts
Accounts Targeted and the Effect on Consumers
Familiar Fraud
Despite Changes in Fraud Trends, Familiar Fraud Patterns Remain Constant
Motivation and Opportunity
One-Stop Shopping: Access
to PII and the Severity of Familiar Fraud

PREVENTION
Data Breaches: Precursors to Fraud
Online Retail Fraud
Mobile Consumers Are Vulnerable Targets
Social Media
Risky Practices on Social Networking

DETECTION
Methods of Detection
Means of Detection by Fraud Type
Means of Detection Among Existing Debit and Credit Card Fraud Victims
Effect of Common Fraud Detection Types on FI Fraud Victim Retention
Length of Fraudulent Activity Before Detection by Detection Methods
Detecting Familiar Fraud: Perpetrators Represent Camouflaged Threat

RESOLUTION
Fraud Resolution Rates
Reach All-Time High
Resolution by Fraud Type
Existing Card Fraud Resolution Is Quicker Because the Process Is Streamlined
Existing Non-Card Fraud Resolution
New Account Fraud Resolution
Account Takeover Fraud Resolution
Severity of Fraud and Resolution Actions
Lower-Income Consumers Are More Severely Affected by Fraud
Low-Income Consumers Know the Perpetrators and Take Legal Action
Fraud Severity and Responses to Fraud
Demographic Determinants of Resolution Action
Consumer Responses to Fraud Depend on Age

APPENDIX

METHODOLOGY

2012 Survey Data Collection
Longitudinal Trending
Categorizing Fraud by FTC methodology
Deviation From FTC and 2003 Methodology and Reporting
Survey Questionnaire
Margin of Error
Contributing Organizations

GLOSSARY

RELATED RESEARCH

Table of Figures

Figure 1: Overall Identity Fraud Incidence Rate and Total Fraud Amount by Year
Figure 2: Breakdown of Identity Crime Types
Figure 3: Overall Measures of the Impact of Identity Fraud, 2004–2012
Figure 4: Identity Fraud Overview: Existing Account Fraud
Figure 5: Identity Fraud Overview: Existing Card Fraud
Figure 6: Identity Fraud Overview: Existing Non-Card Fraud
Figure 7: Identity Fraud Overview: New Account Fraud
Figure 8: Identity Fraud Overview: Account Takeover Fraud
Figure 9: New Account Fraud Incidence and Total Fraud Amount by Year
Figure 10: Types of New Fraudulent Accounts Opened
Figure 11: Existing Account Fraud Incidence and Total Fraud Amount by Year
Figure 12: Existing Card Fraud Incidence and Total Fraud Amount by Year
Figure 13: Type of Existing Card Misused by Age
Figure 14: Type of Personal Information Compromised in a Data Breach
Figure 15: Road Map for EMV Migration And Shifting Liability
Figure 16: Existing Non-Card Fraud Incidence and Total Fraud Amount by Year
Figure 17: Consumer Out-of-Pocket Costs As a Percent of Fraud Losses
Figure 18: Account Takeover Fraud Incidence and Total Fraud Amount by Year
Figure 19: Information Changed on Accounts Taken Over
Figure 20: Recent Use of Security Software
Figure 21: Types of Accounts Taken Over
Figure 22: Type of Card Account Misused Among Account Takeover Victims and All Fraud Victims
Figure 23: Key Fraud Metrics among Familiar Fraud and Non Familiar Fraud Victims
Figure 24: Personal Acquaintance With the Perpetrator by Annual Household Income
Figure 25: Severity of Effective Fraud by Familiar Fraud Victims, All Fraud Victims
Figure 26: Type of PII Compromised Among Familiar Fraud Victims, All Fraud Victims
Figure 27: Fraud Incidence by Data Breach Victims, Non-Data-Breach Victims and All Fraud Victims
Figure 28: Incidence of Fraud Types by Type of Information Breached
Figure 29: Means of Misuse of Fraud Victims' Information 2010–2012
Figure 30: Online Retail Fraud Incidence vs. POS Fraud Incidence 2005–2012
Figure 31: Type of Existing Card Misused for Fraudulent Online vs. In-Person Purchases
Figure 32: Mobile Consumers' Perceptions of the Riskiness of Behaviors
Figure 33: How Recently Mobile Consumers Have Downloaded Apps to their Mobile Device
Figure 34: Fraud Incidence by Ownership of Tech Products
Figure 35: Incidence Rate by Social Networking Activity, 2012
Figure 36: Means of Fraud Detection by Fraud Type
Figure 37: Means of Fraud Detection by Credit and Debit Card Victims
Figure 38: Fraud Victims Who Switched Their FI or Credit Card Provider by Detection Method
Figure 39: Mean Detection Time by Fraud Detection Method
Figure 40: Detection Times for All Fraud Victims vs. Familiar Fraud Victims
Figure 41: Means of Discovery of Fraud by All Fraud Victims vs. Familiar Fraud Victims
Figure 42: Percent of Fraud Victims Who Have Completely Resolved Their Fraud
Figure 43: Percent of Victims Who Have Resolved Their Fraud by Fraud Type, 2011 and 2012
Figure 44: Number of Organizations Contacted for Assistance by Fraud Type
Figure 45: Resolution Time by Fraud Type
Figure 46: Organizations Contacted by Fraud Type
Figure 47: Severity of Effect of Fraud by Fraud Type
Figure 48: Fraud Amounts and Consumer Costs As a Percent of Annual Household Income
Figure 49: Severity of Effect of Fraud by Annual Household Income
Figure 50: Agencies Contacted by Familiar Fraud Victims and All Fraud Victims
Figure 51: Legal Actions Taken by Familiar Fraud Victims and All Fraud Victims
Figure 52: Responses to Fraud by Fraud Amount and Resolution Hours
Figure 53: Organizations Contacted by Severity of Fraud
Figure 54: Fraud Victims' Responses to Fraud by Age
Figure 55: Victims' Actions as a Result of Fraud by Year
Figure 56: Types of Merchants Fraud Victims Avoid

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- 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

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