Compliance in the age of AI.
Practitioner-led courses for financial crime, risk and compliance teams. Learn where AI helps, where it fails, and how to keep every decision evidenced and accountable.
AI is already inside your controls. The judgment still belongs to you.
Screening ranks hits. Monitoring scores alerts. Assistants draft case summaries. Each output looks finished. None of them carries accountability on its own.
These courses are vendor-neutral. They teach the discipline: how AI produces outputs, where it sits in a control, how it fails, and what evidence makes a decision defensible to an examiner.
Five rules every course returns to.
They start in the foundation course and carry through to model assurance and board oversight.
Enter at the level that matches your role.
Foundations
Vocabulary, safe daily use and governance. The common ground for every team.
Oversight
A parallel route for boards and executives: what to ask and what to challenge.
Data to act early
Data, signals, monitoring, KYC, networks, MI, assurance and safe labs.
Connected risks
AI-enabled crime, deepfakes, cyber and the dark web, translated into controls.
Fourteen courses. One standard of evidence.
Foundations
Courses 1 to 3. The shared vocabulary, safe working practice and governance every role needs.
AI in Compliance: Foundations
What AI is, what it is not, and where it fits in financial crime compliance.
Practical AI for Compliance Teams
Use AI for research, drafting and summaries, with verification and data boundaries.
AI Governance and Accountability
Ownership, approvals, validation and monitoring for AI used in compliance.
Board and executive oversight
Course 4. A parallel route for leaders who oversee AI without operating it.
Data to act early
Courses 5 to 13. Specialist courses that turn data and AI into earlier, evidenced control decisions.
Data and AI for Early Financial Crime Action
Move controls from retrospective detection towards earlier, governed action.
Financial Crime Data Architecture and Lineage
Trace data from source to decision and close the breaks in ownership.
Signal Design: Turning Typologies into Data
Turn a typology into observable signals, thresholds and a test plan.
Transaction Monitoring: Design, Alerts and Continuous Improvement
Design, triage, tune and test monitoring as one feedback loop.
KYC/CDD and Adverse Media: Detecting Material Change
Detect material change in customer risk and decide what follows.
Entity Resolution and Network Risk
Match records to real entities and expose the networks behind them.
Control Tower MI: From Metrics to Decisions
Turn metrics into thresholds, accountable owners and intervention.
AI Assurance for Financial Crime
Validate, approve and monitor AI used in financial crime controls.
Synthetic Data and Safe Financial Crime Labs
Test controls and practise investigations without exposing customer data.
Connected risks
Course 14. How criminals use AI, and what that changes in your controls.
Concept, case, practice, checkpoint, check.
Key concepts
Plain-language definitions. No background in data science required.
Worked example
A realistic compliance case with inputs, decision points and evidence retained.
Practical exercise
A short task with a clear output and review criteria.
Human-review checkpoint
Where evidence, control and escalation sit. Who owns the call.
Knowledge check
Quick questions that confirm the decision rule landed.
Every course ends in a work product.
Something you can take back to your team, your committee or your examiner.
- Proceed, pilot, pause or reject recommendationCourse 1
- Use-case canvas with human decision pointsCourse 5
- Data lineage map and ownership modelCourse 6
- Signal specification and test planCourse 7
- Entity and network case map with confidence assessmentCourse 10
- AI assurance fileCourse 12
- Threat-to-control action briefCourse 14
Built for every seat in the control.
Compliance and financial crime teams
Officers, analysts, advisory and QA staff who work alongside AI every day.
Investigators and intelligence analysts
The people who turn an alert, a match or a network into evidence that holds.
Data, model-risk and assurance teams
Architects, data scientists, validators and internal audit.
MLROs, executives and boards
Leaders accountable for AI they do not operate themselves.
AI in Compliance: Foundations is free and open now.
Five modules. 25 to 30 minutes with activities. For anyone in compliance, risk or financial crime, no technical background needed.
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