In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning-trained recognition and analysis capabilities can be added to onboarding and authentication workflows to verify users' identities and detect fraudulent and duplicate accounts in real-time. As a result, user experience is improved, fraud is reduced, and verification costs are lowered.

Guidance

Prescriptive architectural diagrams, sample code, and technical content

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