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GRAFinancial Crime and Fraud RiskIn development
STEM foundation

STEM foundation of Financial Crime and Fraud Risk

The science, technology, engineering and mathematics Financial Crime and Fraud Risk rests on. Each method has a canonical page in the STEM Commons, hosted by GCRI, and a page here on how it is applied in this field.

Open Artificial intelligence and machine learning for financial crime and fraud risk Back to Financial Crime and Fraud Risk
Publisher
The Global Risks Alliance
Field
Financial stability, Cyber and digital resilience, Governance and institutions
Canvas · methods feeding this platformDesign, not a record
STEM foundation of Financial Crime and Fraud Risk: from customer due diligence to suspicious activity reportCustomer due diligence sets a risk rating; transaction monitoring scenarios raise alerts; analysts triage and investigate; the MLRO decides whether to report to the financial intelligence unit; feedback tunes the scenarios. Design of the process, not a record of a run.

Fig.The methods this platform draws on and what each one feeds. Design, not a record.

01In brief

The short answer.

The Financial Crime and Fraud Risk STEM foundation is the set of canonical methods its findings depend on, from artificial intelligence and machine learning to distributed ledgers and digital assets. Each method has a canonical STEM Commons page and a page here on its use in the financial stability field.

02Geography

Activity and exposure in Mexico.

Night-time lights and population, a proxy for economic activity, with the floods and fires that put assets at risk, framed on Mexico; each layer is a dated NASA product.

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) · 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.

Night-time lights and population, a proxy for economic activity, with the floods and fires that put assets at risk, framed on Mexico.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Natural EarthSources 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). 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). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.

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, last day: 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

03Methods

The methods behind the field.

  1. Artificial intelligence and machine learningReal-time fraud scoring · Alert prioritisation · Fairness in fraud controlsSteward · Artificial Intelligence and Autonomous SystemsApplied here
    Canonical · STEM Commons
  2. Network science and contagionMule network detection · Ownership chain analysis · Cross-institution patternsSteward · Systems Science and ModellingApplied here
    Canonical · STEM Commons
  3. Data engineering, interoperability and stewardshipScreening data quality · Monitoring coverage checks · Retention and minimisationSteward · Data and ComputeApplied here
    Canonical · STEM Commons
  4. Digital identity and public digital systemsAssurance levels for onboarding · Defending against deepfakes · Inclusion testingSteward · Digital Public InfrastructureApplied here
    Canonical · STEM Commons
  5. Distributed ledgers and digital assetsBlockchain analytics · Travel rule implementation · Obfuscation techniquesSteward · Fintech NexusApplied here
    Canonical · STEM Commons
04Methods

The STEM methods Financial Crime and Fraud Risk applies, and why.

Each Financial Crime and Fraud Risk method is described once in the STEM Commons at GCRI and applied here to financial crime under stress.

05Why it matters

Grounding makes claims checkable.

When a finding names the methods it depends on, a reader can see its assumptions and limits, and an expert in that method can challenge it.

06Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
STEM CommonsThe shared reference of science, technology, engineering and mathematics methods for the Nexus network, hosted by GCRI, with one canonical page per method and a named steward platform.
Steward platformThe platform that maintains the canonical description of a method in the STEM Commons and reviews how other platforms apply it.
Artificial intelligence and machine learningArtificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs.
Network science and contagionNetwork science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease.
07Sources

Where this page's references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

08Pages

Pages in STEM foundation of Financial Crime and Fraud Risk.

Method

Artificial intelligence and machine learning for financial crime and fraud risk

Method

Network science and contagion for financial crime and fraud risk

Method

Data engineering, interoperability and stewardship for financial crime and fraud risk

Method

Digital identity and public digital systems for financial crime and fraud risk

Method

Distributed ledgers and digital assets for financial crime and fraud risk

09Questions

Questions and answers.

What is a steward platform?
The platform that maintains the canonical description of a method and reviews how it is applied across the Consortium.
Related

Around Financial Crime and Fraud Risk.

Financial Crime and Fraud Risk

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