AI for AML/KYC: From Onboarding to STR/SAR

Designed for :

• AML KYC analysts and approving officers,
• Onboarding and client lifecycle staff,
• Transaction monitoring teams,
• Compliance officers and MLROs who review and approve their work.

The course uses employer-approved chatbot accounts and synthetic data generated in the room.
No programming, no installation or product demonstration is involved.

This one-day course rebuilds four manual AML tasks, maximising trusted chatbot assistance while keeping every answer tied to a named document or data field and an accountable sign-off.
Participants document each manual process, request the complete chatbot deliverable in one go, then apply the Four-colors method:

• Green, verified by a repeatable test
• Red, refused with reasons when evidence fails or sources conflict
• Orange, a judgement signed by a named person
• Grey, explicitly incomplete, with nothing established

Confidence means traceability, not guaranteed correctness: unsupported claims never appear supported.
AI can support and partly automate customer due diligence, screening and suspicious-activity analysis; regulatory responsibility remains with the institution. Material screening decisions and suspicious transaction/activity report submissions require institutional governance and accountable human approval.
Recommended prerequisites:

• AI Driving Licence in Companies (ref 012301) or AI Driving Licence for Financial Sector Employees (ref 012859)
• How to Navigate the EU Artificial Intelligence Act (ref 011838)
• Generative AI for Back-Office Operations (ref 012303) or Generative AI for Finance and Insurance Professionals (ref 012302)
• Open a customer file a reviewer can check instead of redo: the as-of date and the governing version fixed first, a document and page on every field, quotes confirmed by exact search, and what the document says kept apart from what was concluded
• Close a list, show the arithmetic and judge an absence: the sweep behind a beneficial-owner list, the multiplication chain behind every ownership percentage, and an absence recorded as resting on a search rather than as a fact
• Keep the chase list and stop the queue turning into answers: every open item carries what was already tried, and an answer moving between the four tasks travels with its state attached
• Make the rulebook the source of the rule: thresholds, designations and typology names quoted

Contents (8 hours), built around one synthetic customer carried through four tasks:
1. The AML file as a checklist, and the four-step method:

• the manual process
• the far extreme
• what it would take to trust it
• and where the line falls

Regulatory context includes the Luxembourg AML/CFT framework and the transition to the EU AML Single Rulebook (AMLR/AMLA).
2. The manual process written down, then the far extreme: the beneficial ownership walk through a layered structure, asked for in one go (Exercise).
3. What it would take to trust it: the four-colours method, and grading the output green, red, orange or grey (Exercise).
4. Building to the requirements: finding separated from writing, a source for every field, absence reportable, visible working for every figure (Exercise).
5. The line and the supervision protocol: what the AI hands over, what the human owns, and the three checks that keep supervision bounded, with relevant AI Act and financial-sector governance requirements mapped onto them, including human oversight, traceability and record-keeping where applicable (Exercise).
6. Screening hit adjudication: the named discriminating field, and the comparison table as the supervising check (Exercise).
7. Alert review and the disposition narrative: computed figures, and measured facts kept apart from inferred ones (Exercise).
8. Preparing an STR/SAR for reporting to the Luxembourg FIU (CRF): completeness against the applicable reporting schema, provenance back to the earlier cases, and the human decision gate (Exercise).
9. Afterwards: approved enterprise GenAI, access controls, logging, model/vendor governance, testing, human approval gates and periodic review.

After the course, participants can download their House of Training certificate of attendance from the learner portal.

• Short taught blocks alternating with hands-on work in the participants' own chatbot
• One case built with the group, then three run independently

Packs are exchanged and reviewed, so every participant both builds and approves.