Topic starter
11/07/2026 8:29 pm
TL;DR: 15 identity verification providers in Forrester’s 2025 Wave show deepfake detection, document verification, and government data integrations as key differentiators, based on Incode’s summary of the report and its survey evidence. Identity verification now has to judge evidence quality against AI-generated deception, not just routine onboarding checks.
Editorial analysis by NHI Mgmt Group, based on content published by Incode: “Incode Named a Strong Performer in The Forrester Wave™: Identity Verification Solutions, Q3 2025”.
By the numbers:
- Incode says its government data integrations support deterministic verification across 42+ U.S. state DMVs and 10+ countries globally.
Key questions
Q: When does document verification become too weak on its own?
A: Document verification becomes too weak when the decision depends mainly on image quality or OCR output.
Q: What are the warning signs that an IDV programme is being outpaced by fraud?
A: Warning signs include rising manual review queues, more mismatches between presented identity data and authoritative records, and increasing dependence on exception handling.
Q: What should teams do first when deepfake fraud is targeting identity verification?
A: Start with the highest-risk recovery and escalation paths, because those flows often accept weaker proof than initial login.
Practitioner guidance
- Tighten verification policy for synthetic media Define which checks must be present before an identity is accepted, including liveness, deepfake detection, document validation, and authoritative record matching.
- Separate evidence quality from user experience metrics Track false acceptances, manual review overrides, and document mismatch rates alongside conversion so fraud risk is visible in the same programme.
- Prioritise authoritative source integrations Where jurisdiction and coverage allow, connect IDV decisions to DMV or equivalent records instead of relying only on uploaded documents and selfies.
Bottom line: Deepfake-enabled fraud changes identity verification from simple onboarding validation into adversarial trust evaluation.
Glossary terms: Identity verification, Forensic Deepfake Detection, Document Verification, Deterministic Verification
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 8 hours ago by NHI Mgmt Group
11/07/2026 8:58 pm
Deepfake detection has become an identity assurance control, not a fraud add-on. Once synthetic media can convincingly mimic real users, identity verification is no longer just comparing a person to a document. The field has moved into adversarial proofing, where the verification system must test whether the evidence is human-origin, consistent, and correlated. Practitioners should treat this as a core IDV design requirement, not an optional enhancement.
A few things that frame the scale:
- Gartner predicts that by 2026, 30% of enterprises will consider identity verification solutions unreliable in isolation because of AI-driven attacks.
A question worth separating out:
Q: Should organisations prioritise deepfake detection or government data matching?
A: Organisations should prioritise both, but the balance depends on the risk model and available coverage. Deepfake detection addresses manipulated presentation, while government data matching strengthens the evidence behind the decision. The strongest programmes use both because they solve different parts of the same assurance problem.
👉 Read our full editorial: Forrester’s IDV findings underscore the rise of deepfake fraud