Identity Verification (Onboarding)
Onboarding is the default flow: take an unknown person, prove who they are, and enrol them as a verified Customer. It combines document verification, face matching, and liveness into a single session that typically completes in under a minute.
When to use it
Opening a bank account, activating a SIM, registering for a regulated marketplace — any moment a Company needs to know a new user is a real, unique person holding a genuine identity document.
How it works
- Document capture — the User photographs their identity document (both sides where applicable). Real-time guidance ensures the image is usable.
- Face capture & liveness — the User takes a selfie; passive or active liveness confirms a live person is present.
- Automated checks run in the background:
- The document is OCR-read and checked for authenticity and validity.
- The selfie is matched 1:1 against the document portrait.
- Liveness, deepfake, and injection-attack checks guard against spoofing.
- Optionally, duplicity check searches for an existing or blocked identity.
- Decision — the checks combine (via Trust Factors) into an outcome and a result is returned to the integrator.
Outcomes
| Outcome | Meaning |
|---|---|
| New customer | Trusted and unique — a new Customer record is created. |
| Merged / Updated | Trusted and matched an existing Customer — the attempt is linked to them, optionally refreshing their data. |
| Review | Ambiguous — sent to the back office for an operator decision. |
| Rejected | Identity not proven (poor capture or fraud). |
| Blocked | Matched the Blocklist, or rejected and added to it as fraud. |
| Incomplete | The User did not finish. |
Duplicity check during onboarding
When 1:N is enabled, onboarding disambiguates common situations by combining a text signal (name + date of birth) with a face signal:
| Situation | Text | Face | Typical outcome |
|---|---|---|---|
| Stolen ID used by someone else | Same | No match | Reject |
| Fabricated ID, same person | Different | Match | Reject |
| Name change or twin | Same DOB, different name | Match | Review |
| Namesake | Same name, different DOB | No match | New customer |
| Returning customer | Same | Match | Merge to existing |
| Different person | Same DOB, different name | No match | New customer |
See Face Duplicity check and Document Duplicity check for how these signals are computed.
See also
- Digital Identity Lifecycle
- Workflows — how the step sequence is defined and customized
- Trust Factors & Decisioning