The Long Con: An Analysis of Synthetic Identities

A new study from ID:A Labs shows that the rate of synthetic identity fraud has increased more than 100 percent since 2010. This white paper the estimated size of the synthetic fraud issue, and possible solutions for reducing synthetic fraud.

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I See Fraud Rings

According to a new study by ID Analytics’ ID:A Labs there are more than 10,000 identity fraud rings in the U.S. This study is the first to systematically find many thousands of identity fraud rings, by developing an algorithm capable of automating the process of examining the interconnections between identity fraudsters.

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Child Identity Fraud Study

Minors’ identities are particularly appealing targets for fraudsters because their personal data is untainted, legitimate, less likely to be monitored for misuse, and few tools are available to protect children against attack. To provide greater insight into the scope of child identity fraud, ID Analytics conducted a study of more than 172,523 children enrolled in ID Analytics’ Consumer Notification Service at some point during the 12-month period.

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Address Discrepancy Data Study

Requesting a change of address on an account is a common tactic used by fraudsters to gain access to an account’s assets. Once the fraudulent address is in the system, the fraudster can then place orders for replacement credit cards, checks, passwords/PINs or new cell phones which will be mailed to the fraudulent address. Frauds linked to address changes are costing companies millions of dollars each year.

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The Trouble with Names /Dates of Birth Combinations as Identifiers

Direct marketing and many other consumer analytics frequently rely on name/dates-of-birth (DOB) combinations for personal identification. We will demonstrate that empirically, names/DOB combinations are woefully inadequate as a precise tool for personal identifiers; this is true for common, especially for cyclical and trendy names appended by a DOB.

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Scaling Properties of ID Analytics Networks

Despite the absence of apparent associations between individuals seeking credit, we find that the credit application process reveals a socio-economical network of credit customers. In the complex heterogeneous data captured by ID Analytics, we observe networks of people connected by their names, dates of birth, SSNs, phones and addresses.

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