Follow the Digital Breadcrumbs: Stopping Synthetic Identity Fraud with Proof of Life Data Sources

Synthetic identity fraud, in which criminals combine real and fabricated personal information to obtain credit, benefits, or move illicit funds, is projected by Deloitte to cause at least $23 billion in losses by 2030. Socure’s latest Sigma Synthetic Fraud model uses machine learning, consortium data, transaction signals, and “proof of life” records—such as long-term address and email histories, school enrollment, driver’s licenses, property records, and legal or death records—to assess whether an identity reflects a genuine person’s accumulated life history. While fraudsters may create short-term credit or utility records, they often cannot convincingly reproduce years of consistent personal data, making thin or recently established files potential warning signs. The approach also aims to reduce false positives for younger people, immigrants, and gig-economy workers who may lack traditional credit histories by evaluating multiple forms of evidence rather than relying on any single record.

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