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STEM foundation

STEM foundation of Insurance Nexus

The science, technology, engineering and mathematics Insurance Nexus 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 Actuarial mathematics in insurance Back to Insurance Nexus
Publisher
The Global Risks Alliance
Field
Insurance and catastrophe, Climate and hazards, Health and biosecurity
Canvas · methods feeding this platformDesign, not a record
STEM foundation of Insurance Nexus: the risk management process, scope to reviewScope, context and criteria set the frame; risks are identified, analysed and evaluated against criteria; treatment follows; monitoring and review and communication run across every step, and the process is recorded and reported. 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 Insurance Nexus STEM foundation is the set of canonical methods its findings depend on, from actuarial mathematics to artificial intelligence and machine learning. Each method has a canonical STEM Commons page and a page here on its use in the insurance and catastrophe field.

02Geography

Activity and exposure in the Gulf of Mexico coast.

For STEM foundation of insurance nexus, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on the Gulf of Mexico coast; every other NASA layer, live feed and forecast waits in the layer list.

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

Layers 2

Activity and exposure, for this page

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.

For STEM foundation of insurance nexus, in GRA's spatial-finance reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on the Gulf of Mexico coast; every other NASA layer, live feed and forecast waits in the layer list.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 · Blue Marble · © OpenStreetMap contributors · Natural Earth · GDACS · CIESIN/Columbia, NASA SEDAC HBASESources 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
  • 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 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
  • 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
  • GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); 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). 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: GDACS (EC JRC / UN OCHA) Events are indicative, not official warnings.
  • Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
  • Optional layers (35 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

  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
  • Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026

03Methods

The methods behind the field.

  1. Actuarial mathematicsLoss reserving with development triangles · Experience rating and credibilitySteward · Actuarial and Quantitative RiskApplied here
    Canonical · STEM Commons
  2. Extreme value theory and tail riskLarge-loss pricing for excess layers · Tail measures for solvency capitalSteward · Reinsurance and CatastropheApplied here
    Canonical · STEM Commons
  3. Probability and statisticsFrequency and severity modelling · Bayesian updating of assumptionsSteward · Systems Science and ModellingApplied here
    Canonical · STEM Commons
  4. Climate and atmospheric scienceClimate-conditioned hazard catalogues · Supervisory climate stress scenariosSteward · Climate and HazardsApplied here
    Canonical · STEM Commons
  5. Artificial intelligence and machine learningExposure enrichment from imagery · Fraud and claims triage · Fairness testing of pricing modelsSteward · Artificial Intelligence and Autonomous SystemsApplied here
    Canonical · STEM Commons
04Methods

The STEM methods Insurance Nexus applies, and why.

Each Insurance Nexus method is described once in the STEM Commons at GCRI and applied here to insured and uninsured loss, insurability and the capital view of catastrophe risk.

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.
Actuarial mathematicsActuarial mathematics applies probability, statistics and financial theory to measure and price contingent risks, particularly in insurance and pensions.
Extreme value theory and tail riskExtreme value theory and tail risk study the behaviour of rare, high-impact events.
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 Insurance Nexus.

Method

Actuarial mathematics in insurance

Method

Extreme value theory for insurance tails

Method

Probability and statistics in insurance

Method

Climate science for insurance hazard

Method

Machine learning in insurance

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 Insurance Nexus.

Insurance Nexus

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