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Finance & compliance

Automate KYC document extraction with AI

AI automates KYC by reading onboarding documents (IDs, proofs of address, company registration) and extracting them into a structured record with risk flags and recommended checks, grounded in the source documents. The demo below takes onboarding text and returns structured KYC data plus the flags a reviewer needs.

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KYC Document Extractor

Onboarding documents in, structured KYC data and risk flags out.
10-15%
of headcount that banks put on KYC and AML work today
90%
of financial institutions already use AI in fraud and compliance
Self-hostable
documents can stay inside your environment for data residency

Why this matters now

Banks and fintechs put 10 to 15% of headcount on KYC and AML work, yet the industry catches only a small fraction of illicit flows. Manual onboarding is slow, inconsistent, and expensive, and finance operations is one of the most active areas of AI adoption because the pain and the regulatory pressure are both high.

How the reliable version works

  1. 1Read each onboarding document, digital or scanned, with OCR or a vision model.
  2. 2Extract identity, address, and entity fields into a typed, validated record.
  3. 3Populate risk flags and recommended checks instead of leaving them blank.
  4. 4Cross-check fields for consistency and flag mismatches for review.
  5. 5Route edge cases and low-confidence extractions to a human analyst.

Common questions

Is AI accurate and compliant enough for KYC?

For extraction and triage, yes, when paired with validation and human review of flagged cases. AI does the reading and structuring; your compliance team keeps the decision and the audit trail. The value is speed and consistency on the first pass.

Can we keep customer documents in-house?

Yes. The extraction can run on self-hosted models so sensitive onboarding documents never leave your environment, which is usually a hard requirement for regulated finance.

Does it flag risk, or just extract fields?

Both. Alongside the structured fields it populates risk flags and recommended checks, so a reviewer starts from a triaged case rather than a blank form.

Related demos, work and services

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