Using ID Screening to Fight COVID-19 Economic Relief FraudDr. Gary Shiffman of Giant Oak on the Role of Artificial Intelligence
High-speed identity screening can play a critical role in cracking down on fraud tied to COVID-19 economic relief efforts without impeding legitimate access to funds, says Dr. Gary Shiffman, CEO of Giant Oak, which offers artificial intelligence technology.
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"We've got to get beyond this idea that to get money out quickly, we can't do screening - we absolutely can," he says. "Technology exists today - machine learning, artificial intelligence technologies - which enables high-speed, large-scale screening and monitoring for purposes of deterring fraud. Remember, the purpose of law enforcement is not to catch fraudsters. The purpose is to raise the bar so that people say, 'Look, I don't even want to engage in fraud. It's just not worth it.' That's the goal."
In a video interview with Information Security Media Group, Shiffman discusses:
- The pervasiveness of economic stimulus benefits fraud last year and the need to prevent it in new programs this year;
- How money launderers have adapted to working from home;
- The use of federated learning in collaborative bank fraud prevention.
Shiffman is CEO of Giant Oak Inc. and Consilient, both of which are machine learning and artificial intelligence technology companies. He's an applied microeconomist and business executive working to combat organized violence, corruption and coercion. He received his doctorate in economics from George Mason University. His global operational experience includes service as a U.S. Navy surface warfare officer in the Pacific Fleet with tours in the Gulf War, as an official in the Pentagon and a senior executive in the U.S. Department of Homeland Security. He also served as a national security adviser in the U.S. Senate and as a business leader at a publicly traded corporation. Shiffman recently published "The Economics of Violence: How Behavioral Science Can Transform Our View of Crime, Insurgency, and Terrorism" with Cambridge University Press.