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Socure, the leader in Day Zero digital identity verification, has published a new white paper on the growing issue of synthetic identity fraud, the fastest-growing financial crime in the U.S. According to the Federal Reserve, lenders lost six billion dollars to synthetic fraud in 2016 alone.

Synthetic fraud is a gradual process in which fraudsters compile a false identity to resemble a person, then use the false identity to commit financial crimes. The ultimate goal is to compile data elements or profile attributes—taken from different individuals—to present a “new” face that can get past the first line of defense with an appearance of legitimacy.

The paper, titled “The Identities Are Fake, but the Consequences Are Not,” examines the phenomenon of synthetic identity fraud, the methods fraudsters are employing, and why financial institutions are particularly vulnerable targets. The paper also addresses how advanced analytics and data science are helping institutions grapple with synthetic identity fraud.

“Synthetic identity fraud presents some unique challenges for financial institutions,” says Jeff Scheidel, Head of Training & Development at Socure. “Because a synthetic identity is composed of very real characteristics, even if the name or person is fabricated, it may have been so imbued with sufficient history that it can pass an identity and a Know Your Customer (KYC) check.”

The paper examines the primary methods used to synthesize an identity, which include:

  • Fabrication to create a completely fake identity containing no actual data related to an actual human being.
  • Identity manipulation modifying actual identity attributes of the fake person.
  • Identity compilation, the combination of modified and actual attributes, and the committing of the crime.

The paper underscores how difficult synthetic identity fraud can be to detect and deter, with no single real person to report the fraud. According to the Federal Reserve, between 85 and 95 percent of synthetic applicants are not picked up by standard fraud models.