Key takeaway: When the same forged document or device turns up across companies within minutes, no single merchant’s fraud check catches it alone, which is exactly why cross-institution device and identity signal-sharing is becoming a bigger part of onboarding fraud prevention.
Shufti’s 2026 Identity Fraud Report finds deepfake documents accounted for 80.1% of AI-enabled identity fraud attempts in the first half of 2026, as organized rings increasingly reuse forged IDs, devices, and IP addresses across multiple companies.
The identity verification vendor published the report September 8, analyzing confirmed fraudulent verification attempts across eleven industries from January through June 2026, then cross-referencing shared documents, devices, and IP addresses to trace fraud rings operating across institutions.
Shufti found 65.68% of linked fraud attempts reused a forged document already seen at another company, while 17.67% shared a controlled IP address and 16.64% shared a device. The largest cluster linked 70 identities across 13 devices, with one device anchoring 16 verification attempts alone. Within AI-generated fraud, deepfake documents made up 80.1% of attempts, ahead of synthetic identities (12.31%), injected videos (4.01%), and face swaps (3.58%). By industry, digital-asset platforms had the highest confirmed fraud rate at 22.49%, ahead of fintech (18.36%) and forex (17.18%); banking was lowest at 4.24%. Cross-border fraud was rare (2.01% of network cases) but fast, averaging a 9-minute, 33-second gap between verification attempts in different countries, with the fastest at 38 seconds.
“Deepfakes are no longer just an individual fraudster’s tool. Organised crime networks are using the same AI-generated documents and identities across borders,” said Faryam Asif, Shufti’s chief technology officer.
Why it matters: If your onboarding or age/identity verification relies on document checks alone, a single forged ID or device can quietly power sign-ups at dozens of platforms before anyone connects the dots — Shufti’s data shows that pattern concentrated in iGaming, forex, and lending, where bonus abuse and self-exclusion evasion are common motives.