Synthetic Identity Fraud: How to Define and DetectGreg Woolf of FiVerity Discusses a Federal Reserve Initiative to Better Define the Crime
Synthetic identity fraud (SIF) is a pervasive yet ill-defined crime. Greg Woolf of FiVerity discusses a recent initiative by the Federal Reserve to better define and therefore better manage SIF.
See Also: Playing A New Hand: How Digitalization Is Reshuffling The Cards For Banks Worldwide
“The Fed is concerned about this because, up until now, there was no standard industry definition around synthetic identity fraud. It's a very sophisticated cybercrime so it's hard to identify and hard for banks to detect. Banks look at it and say, 'Is this bad underwriting or fraud?'” says Woolf. “The Fed created an industry focus group that, for the last six months, has been trying to come up with a common definition around synthetic identity fraud. It wants to give the industry a utility to be able to define what SIF is and what it isn't because … if you can't measure it, you can't manage it.”
In a video interview with Information Security Media Group, Woolf discusses:
- The FRB Boston initiative to develop a common definition of SIF;
- Use of “human in the loop” machine learning to combat synthetic identity fraud;
- Development of banking consortia to share and refine counterattacks for SIF schemes.
Woolf is founder and CEO of FiVerity. He brings more than 20 years of experience founding and running FinTech companies. He moderates AI industry groups with more than 10,000 members. He is also the founder the Boston AI Think Tank - a group of senior executives from prominent global financial institutions who, with current and former government regulators, are exploring how AI can improve financial crime detection for the financial services industry.