Merchant identity verification combines Thai national ID OCR, a government DOPA registry check, and a liveness face match into a single camera-based session. For juristic entities, director verification runs as a separate, asynchronous flow.
Before granting access to accept payments, a platform has a legal and regulatory obligation to confirm the identity of every person applying. eKYC replaces in-person verification with a camera-based flow, adding a liveness check to confirm that a real person, physically present, is the one submitting.
The process is the same whether it is the applicant or a director. For juristic applicants, the person filing is the person of attorney (POA), who completes eKYC as part of the main flow. Each listed director completes theirs separately afterward via a dedicated link.
Each submission is evaluated across three dimensions: card photo quality, whether OCR-extracted fields were edited, and how closely the selfie matches the ID.
All checks pass with no field edits. Application proceeds automatically without requiring manual review of eKYC data.
One or more checks returned below-threshold results, or the applicant edited OCR-extracted fields. Surfaced for manual review in the reviewer workspace.
Camera system error or face match could not be completed. Requires a re-capture or follow-up by the operations team.
The applicant positions the card within the frame and captures. The system then performs OCR extraction and document verification.
Each failed capture returns a specific error code mapped to a targeted retake prompt.
The applicant taps to shoot when ready, with real-time guidance to frame the shot correctly.
Each director completes their own eKYC through a separate link after the application is submitted. The POA coordinates delivery.
Early placement filters intent but risks abandonment before any contact data is captured. Late placement preserves lead quality.
eKYC is positioned at steps 12–13 because the primary risk is lead loss, not intent filtering. Contact details and business information are already captured before this point. The friction spike is mitigated by listing camera scan as a requirement on the intro page, so it reads as the final step rather than an unexpected gate.
Testing covered 8 participants (2 moderated, 6 unmoderated). Each session included a forced fail followed by a pass for both ID document capture and liveness, testing whether the system communicated what went wrong and guided recovery clearly.
No participant struggled to understand what the AI model was evaluating. Error messages were specific enough that retakes felt instructional rather than punitive. The session completion rate was 100%, though this reflects both motivated participants and a prototype that had been iterated to address known issues before testing began.
This case study covers registration and identity verification. Each module was designed independently and built to hand off cleanly to the next.