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Identity & Fraud
Detecting deepfakes and doctored documents
A financial institution benchmarked biometric identity and document-verification tools against synthetic deepfake and injection attacks, and reached a confident decision in weeks.
6 weeks
vs 12–18 months traditionally
The challenge
The rise of generative AI made remote identity verification hard to trust. Fraudsters submitted synthetic videos, voices and documents during onboarding, while manual reviewers were overwhelmed – driving up cost, delay and the risk of human error.
How it ran on NayaOne
1
Isolated evaluation sandbox
A secure, repeatable environment to test deepfake-detection, biometric and document-verification tools without exposing any sensitive data.
2
Synthetic attack generation
Face swaps, voice clones, replays and injection attacks generated on demand to stress every vendor under identical conditions.
3
Evidence-backed scoring
Every test logged for full traceability, giving risk and compliance an auditable record instead of a vendor questionnaire.
Outcomes
>95%
detection precision against deepfakes and spoof attempts
<800ms
average detection time, real-time onboarding preserved
<15%
manual review rate after automation