nexustandards.com/digital-ai · 2 taught
Digital, Cyber and AI
NRS-C9 Risk models, methods and validation; NRS-I3 Model and data cards for risk
2standards1discipline
Illustrative: a joint probability density whose dependence shifts over time
Risk Academy, led by the Nexus Consortium · Risk Science and Methods · Department of Risk Management
Models that are wrong or misused, and the controls that manage them
Model risk and validation
A model is a decision tool with an error term, and model risk is what happens when the error is ignored.
- Department
- Department of Risk Management
- Courses
- In preparation
- Levels
- L3 Advanced
- Labs
- In development
- Model risk governance
- Model validation
- Model monitoring and drift
- Explainability and fairness
- Third-party models
- Generative AI in finance
- Human-in-the-loop controls
- Model inventories
02Maps
Build a twin of any place
Search any place or open the world; the layers and generators follow model risk and validation on the planet map, the globe and in 3D.
- Terrain 24 to 2,805 m
- 3 reference earthquakes, M6.2 to M6.6
- Earthquakes M4.5 and above, past week: read live when the 3D twin opens
- USGS ShakeMap intensity contours: read live when the 3D twin opens
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and – zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices that can draw it, once its data and code have been checked.
The boundaries, names and designations shown do not imply any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. Countries: Natural Earth (public domain).
03Live map
Connected activity in the Nordic and Baltic region
Night-time lights and population density, the footprint of electrified, connected activity, over the Nordic and Baltic region.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 2
Connected activity
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
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.
Connected activity, framed on the Nordic and Baltic region.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
- 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 (3 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
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
04The subject
The subject, methods and measures and debates and bodies
The field and its scope
For model validators, model owners, data scientists and risk committees who must decide whether a model is fit for the decision it supports and keep it that way in use.
Quantities and units
Methods and their limits
Each method is listed with what it assumes and where it fails.
What the field debates
Bodies that set practice, and the data they publish
Bodies and standards are named so learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
05The method
From a model's claim to a back-test verdict: expected exceptions, observed exceptions and the zone the count falls in
A model that claims a confidence level makes a testable promise about how often outcomes will exceed its figure; counting those exceptions over a test window and comparing the count with the expected distribution gives the verdict, and the verdict feeds the model's tier and its permitted use.
Sources: Board of Governors of the Federal Reserve System, SR 11-7: Guidance on Model Risk Management (2011); Basel Committee on Banking Supervision, Supervisory framework for the use of backtesting in conjunction with the internal models approach (1996); Bank of England PRA, SS1/23 Model risk management principles for banks (2023); Box, Science and statistics, Journal of the American Statistical Association (1976); Kupiec, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives (1995).
06At a glance
At a glance, with 5 related topics
Model risk and validation at a glance
risksacademy.comrisk-sciencediscipline
- Open, 0
- In preparation, 1
- Also taught here, 2
- Subtopics, 8
Nexus Standards it teaches and relies on
The Nexus Standards the discipline teaches toward, grouped by master standard, with the disciplines that teach each.
nexustandards.com/data · 1 taught
Data and Evidence
NRS-M23 Scientific assurance
1standard1discipline
Nexus Standards at the AcademyThe Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review.
Research and tools
Research and preprints as they are published, by discipline, with the faculty's other tools.
The hub lists the newest research and preprints for Model risk and validation, each with its source and date. Open the research hub
The methods it teaches
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
Methods and practice
- Uncertainty and confidence1 course
Hazard families
- AI and algorithmic failure1 course
What is changing now
Recent developments in model risk and validation, each with its source; sources checked on 16 October 2026.
Courses in this discipline
Courses for this discipline are in development; until they open, study it through the connected courses and open lessons below.
Courses with this primary disciplineIn preparation
- RAT-217Model Risk Management and ValidationIn preparationTechnical, L3 Advanced, 10 hours (estimate)
Courses from connected disciplines that also teach this field
Each course has its primary discipline elsewhere and teaches this field as part of its scope.
From connected disciplinesIn preparation
- RAT-207Quantitative Risk ModellingIn preparationTechnical, L2 Intermediate, 12 hours (estimate), primary discipline: Quantitative risk analysis and statistics
- RAT-215Credit Risk ModellingIn preparationTechnical, L3 Advanced, 12 hours (estimate), primary discipline: Banking and credit risk
07Console
How Model risk and validation is taught, as a process you can run
Press Play to run this process from this page's own data.
08Learning
Learning, practice and connections
Achievements in this discipline
Pathways that teach it
Where the subject is practised
- The Global Risks Alliance (GRA)AI and Financial Model RiskCredit and pricing models, Explainability and fairness, Generative AI in finance, Human in the loop controls, Model monitoring and drift, Model risk governance, Stress and scenario models, Third party models
Field platforms of the ecosystem where the subject is studied and applied.
The three Foundry systems where it is practised
- FoundryNXS-NXQ systemFitness of models for a taskCompetence room
- NXS-LAB systemModel comparisonBenchmarks room
- NXS-GRX systemValidation studies behind risk productsReports room
Foundry system pages describe what each system and its rooms are designed to do; the courses teach the methods these systems apply.
Across the ecosystem
09Take part
Take part in Risk Science and Methods
Start from who you are.
10References
Core references and where to begin
Start with Board of Governors of the Federal Reserve System, then Bank of England Prudential Regulation Authority, then Box.
11Questions
Questions, answers and key terms
Is study here 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 courses in model risk and validation?
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
Model risk
The potential for loss or poor decisions from errors in models or their misuse.
Term
Validation
Independent review that a model is sound and fit for its purpose.
Term
Back-testing
Comparing model predictions with what happened.
Term
Drift
Change in data or relationships that degrades a model over time.
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 Model risk and validation
Model risk and validation
Prepare with a related course
Open courses in connected disciplines teach the methods this discipline will build on.