AIAC-04 · Sector track
For risk, compliance, and financial-crime professionals at exchanges, custody providers, and digital-asset firms deploying AI into monitoring, screening, and onboarding.
§ 1 — Intended candidates
MLROs, heads of risk, and compliance leads at exchanges, custody providers, payment firms, and digital-asset businesses.
§ 2 — Examination domains
Domain weightings are published with the full syllabus ahead of the first cohort.
Where machine learning enters transaction monitoring, screening, and onboarding, and what changes about the control when it does.
Producing evidence that an alerting model works as claimed, in terms a supervisor examining the control will accept.
Tuning, threshold setting, and the asymmetry between a missed alert and an over-alerting system.
Assessing bought-in screening and analytics tools where the model is not yours and visibility is limited.
Retaining enough of a model decision to reconstruct why an alert fired, or did not, months later.
§ 3 — How to certify
Preparation is optional. Choose whichever route suits how you already work — the examination, the fee, and the marking standard are the same either way.
Register and sit the examination. Suitable for practitioners already working in assurance who want the credential without preparation.
The same examination, bundled with self-paced preparation mapped to the syllabus. Taking it confers no advantage in marking and is never required.
Preparation is optional. The examination is available at the same fee and marked to the same standard whether or not preparation was purchased, and assessors are not told which route a candidate took. How we manage this conflict.
§ 4 — Assessment
Scenario-based examination set in a digital-asset compliance context supplied at the start, requiring a risk profile for an AI-assisted control and a supervisory-facing justification.
Successful candidates receive a credential with a unique identifier that any employer can check against the public record. See credential verification.
Curriculum mapping indicates alignment of learning content with published frameworks. It does not constitute endorsement by, or certification under, any standards organization or regulator.
§ 5 — Further reading
Producing credible, independently reviewable evidence that an AI system works as intended — and how that differs from governance and compliance.
Three terms used interchangeably that mean different things, have different outputs, and require different independence. A practitioner-level distinction.