Learning Module 06 · Week 6 · Individual mission
Design an Accountable Compliance Alert
Improve an anti-money-laundering alert without treating automation as a legal conclusion.
- 1BriefingCurrent
- 2Worked exampleNext
- 3Case boardNext
- 4CheckpointsNext
- 5ReflectionNext
- 6CompleteNext
Start here
Briefing
An AML model produces risk indicators, not a legal verdict. Effective RegTech combines detection with governance, investigation, escalation, and review.
Use a fictional control case. The approved teaching spine names the HSBC AML case as classroom context, but this mission copies no protected case facts, assessment content, or conclusions, and the invented banks in this mission are not a retelling of it. Any laws or regulators named here are classroom context, not legal advice for a specific jurisdiction.
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Game economy
Build Evidence Momentum without risking Decision XP
You begin with 3 Evidence Momentum. Any held points return when you solve that checkpoint. Evidence Momentum never falls below zero and never changes assessment marks. Complete the mission to open one transparent reward draw.
What you will practise
- Distinguish a risk alert from a verified compliance finding.LO1
- Design monitoring, escalation, documentation, and accountability around a RegTech control.LO2, LO3
6 terms for this mission
- Anti-money laundering
- Controls intended to detect, assess, and respond to money-laundering risk.
- False positive
- An alert that is investigated but does not support the suspected condition.
- Model drift
- A change in data or behaviour that weakens a model over time.
- Accountability
- Clear ownership of decisions, controls, review, and remedy.
- Three Lines Model
- A governance model (IIA Three Lines) in which first-line management owns risk, second-line functions oversee it, and internal audit independently assures both.
- FATF Recommendations
- Global anti-money-laundering standards from the Financial Action Task Force that each country enacts as its own binding law and supervision.
Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards are named only as general context.
Sources and further reading
Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards named in this mission, including in the sources below, appear only as general context, and the sources support the concepts and methods, not the events in the scenario.
Financial Action Task Force (2012, as amended). International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation: The FATF Recommendations. FATF, Paris.
The primary standard-setter text behind the risk-based approach, customer due diligence, transaction monitoring and suspicious-transaction reporting that national law implements.
Financial Action Task Force (2021). Opportunities and Challenges of New Technologies for AML/CFT. FATF, Paris
The standard-setter's own assessment of what automated monitoring can and cannot establish, and the conditions of responsible use: explainability, data quality, human review and supervision.
The Institute of Internal Auditors (2020). The IIA's Three Lines Model: An Update of the Three Lines of Defense (July 2020). The Institute of Internal Auditors
The source text for the Three Lines Model, defining first-line ownership of risks and controls, second-line expertise and challenge, and independent third-line assurance.
Koprivec, F., Kržmanc, G., Škrjanc, M., Kenda, K., & Novak, E. (2022). Screening tool for anti-money laundering supervision. In J. Soldatos & D. Kyriazis (Eds.), Big Data and Artificial Intelligence in Digital Finance: Increasing Personalization and Trust in Digital Finance using Big Data and AI (pp. 233-251). Springer. Open access. doi:10.1007/978-3-030-94590-9_13
A worked account of an AML screening pipeline in which automatically flagged patterns are ranked by risk and passed to domain experts, illustrating why a model output is a prioritisation signal rather than a conclusion.