Illustrative: a chain of blocks each linked by hash to the one before, with a Merkle tree under the newest

Risk Academy, led by the Nexus Consortium · Course catalogue · Finance and Economics

Banking and Financial Stability

Financial crime and fraud

Financial crime and fraud, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Financial crime and fraud: foundations and practice

This area covers money laundering, terrorist financing, sanctions evasion, fraud and scams, identity fraud and cyber-enabled financial crime, and the risk-based controls used to detect and prevent them.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Points to crypto activities where money laundering and terrorist financing controls are currently weakest. EU Anti-Money Laundering Authority, AMLA identifies AML/CFT priorities for the review of MiCA, 7 October 2026

Learning outcomes

  1. Define money laundering and the other key terms of the discipline and use them correctly in a written case
  2. Explain money laundering and terrorist financing with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against sanctions compliance, stating evidence, date and limits

Modules

  1. Money laundering
    • Money laundering
    • Terrorist financing
    • Sanctions compliance
  2. Fraud and scams
    • Fraud and scams
    • Identity fraud
    • Cyber-enabled financial crime
  3. Suspicious activity monitoring
    • Suspicious activity monitoring
    • Beneficial ownership
  4. Lab: the discipline's method
    • Illicit transactions are rare, so even an accurate monitoring rule produces mostly false alerts; the false positive rate, not the detection rate, decides how much analyst time a bank spends and how many reports reach the financial intelligence unit.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-P9 Legal, compliance and regulatory risk
  • NRS-H118 Hazard family: Organised crime and illicit economies
  • NRS-M8 Method fabric: Financial risk

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C6 Systemic and cascading risk
  • NRS-C7 Early warning, triggers and anticipatory action

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. EU Anti-Money Laundering Authority, AMLA identifies AML/CFT priorities for the review of MiCA, 7 October 2026
  2. Sharman, Shopping for anonymous shell companies: an audit study of anonymity and crime in the international financial system, 2010
  3. Levi and Reuter, Money laundering, 2006
  4. Pol, Anti-money laundering: the world's least effective policy experiment? Together, we can fix it, 2020
02Live map

Activity and exposure in the Gulf of Mexico coast

Night-time lights and population, a proxy for economic activity, with the floods and fires that put assets at risk, over the Gulf of Mexico coast.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Activity and exposure

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Activity and exposure, framed on the Gulf of Mexico coast.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Optional layers (4 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Financial crime and fraud: frontier methods

The methods, data, tools and studies that changed financial crime and fraud between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Financial crime and fraud: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Points to crypto activities where money laundering and terrorist financing controls are currently weakest. EU Anti-Money Laundering Authority, AMLA identifies AML/CFT priorities for the review of MiCA, 7 October 2026

Learning outcomes

  1. Explain what changed in financial crime and fraud since 2024 and cite the studies, data or rules behind each change
  2. Reproduce AMLA flags crypto-asset AML gaps for MiCA review on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. AMLA flags crypto-asset AML gaps for MiCA review
    • AMLA asked that the MiCA review bring staking, lending and borrowing into scope, define control in decentralised finance, address unauthorised stablecoins and clarify cross-border supervision of service providers.
    • Points to crypto activities where money laundering and terrorist financing controls are currently weakest.
    • EU Anti-Money Laundering Authority, 7 October 2026
  2. AMLA finalises customer due diligence and group-wide standards
    • The EU Anti-Money Laundering Authority submitted three final draft technical standards to the Commission, covering business relationships and occasional transactions, customer due diligence including electronic identification and PEP screening, and group-wide AML/CFT arrangements.
    • Sets harmonised EU expectations for onboarding, verification and group controls that firms must build into compliance.
    • EU Anti-Money Laundering Authority, 1 October 2026
  3. UK APP scam reimbursement reaches 88% of losses
    • Eighteen months after mandatory reimbursement began in October 2024, the Payment Systems Regulator reports 88% of money lost to authorised push payment scams, £316m, returned to victims, with most claims closed within five business days.
    • Provides evidence on how shifting fraud liability to payment firms affects victim outcomes and fraud controls.
    • Payment Systems Regulator, 30 July 2026
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  5. Technology that helps and the risks it carries
    • AI models, tokenisation and regulatory technology.
    • Model risk, cyber accumulation, exposure to quantum attacks on cryptography, and concentration on third-party cloud providers.

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-P9 Legal, compliance and regulatory risk
  • NRS-H118 Hazard family: Organised crime and illicit economies
  • NRS-M8 Method fabric: Financial risk

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C6 Systemic and cascading risk
  • NRS-C7 Early warning, triggers and anticipatory action

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 5 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. EU Anti-Money Laundering Authority, AMLA identifies AML/CFT priorities for the review of MiCA, 7 October 2026
  2. EU Anti-Money Laundering Authority, Press Release: AMLA finalises key standards for the private sector, 1 October 2026
  3. Payment Systems Regulator, APP scams reimbursement dashboard for Q1 2026, 30 July 2026
  4. Sharman, Shopping for anonymous shell companies: an audit study of anonymity and crime in the international financial system, 2010
  5. Levi and Reuter, Money laundering, 2006
  6. Pol, Anti-money laundering: the world's least effective policy experiment? Together, we can fix it, 2020
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Money laundering

Disguising the criminal origin of funds so they appear legitimate.

Term

Know your customer

Verifying customers' identities and understanding their activities.

Term

Beneficial owner

The natural person who ultimately owns or controls a legal entity.

Term

Sanctions screening

Checking customers and transactions against sanctions lists.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

Risk Academy
Related

Around Financial crime and fraud: courses

Risk Academy

Study financial crime and fraud with us, or teach it

Every course is free and open. Institutions and experts can co-teach, host a cohort, contribute a lab or review a course before it opens.

Browse the course catalogue Partner with the Academy
Risk Academy

Courses · practice · credentials

Not for profit

Our articles, maps and twins are free to read and free to republish. Readers and partners keep it that way.

Support our work

Who funds us

Risk Academy is a not-for-profit institution. To ask who funds a piece of work, use the funding questions form.

Independence

Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Corrections policy

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.

Privacy choices