Synthetic Fraud Detection: An In-Depth Guide

Synthetic fraud detection is a method used by financial institutions to prevent fraudulent activities involving fictitious identities. It employs advanced algorithms and data analysis to identify anomalies in data and flag potential synthetic identities, helping to proactively prevent financial losses.

March 3, 2022
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Ronan Burke
Co-founder and CEO
Synthetic identity fraud is a type of identity theft in which a criminal combines both real and fake personal information to create a new, fictitious identity that can then be used for various identity-related schemes, such as credit card fraud, bank fraud, and more. While the fraudster may use an individual's SSN, they add fake personal details, such as a fictitious name and address. This combination of identifying information often fails to show up on credit reports or other traditional detection tools used by consumers.

About the author

Ronan Burke is the co-founder and CEO of Inscribe, where he has spent nearly a decade studying how fraud evolves and what it takes to stop it. His work sits at the intersection of generative AI and financial crime: specifically, how AI-powered reasoning can outpace the fraud tactics that have already outrun rules-based detection. He writes and speaks regularly on document fraud, agentic AI, and what it means to build fraud defenses for a world where fakes are cheap, fast, and convincing.

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