GRA · Actuarial and Quantitative Risk
Spatial financeWhere capital meets hazard, exposure and transition
Actuarial and Quantitative Risk
The quantitative core of financial risk: actuarial and statistical methods that turn hazard, exposure and behaviour into loss distributions, capital and pricing questions, with their uncertainty stated. Actuarial and Quantitative Risk is an established platform of The Global Risks Alliance.
- STEM methods
- 6
- Canonical methods this field applies
- Topics
- 8
- Subject pages in the field
- Counterparts
- 4
- Platforms in shared fields across the network
- Report series
- 2
- Reports series it contributes to
Actuarial and Quantitative Risk in seven parts.
- 01What the platform doesShared methods, stated uncertainty, no sign-off
- 02FrameworksWhat the work is read against
- 03OverviewWhat Actuarial and Quantitative Risk does
- 04The problemWhy quantitative models fail when they matter most
- 05Foundation and counterpartsGrounded in mathematics, connected across three institutions
- 06CoverageThe areas of insurance and catastrophe this platform takes on
Shared methods, stated uncertainty, no sign-off.
The platform builds shared industry understanding of actuarial mathematics, extreme value theory, stochastic modelling, credibility and pricing theory and model validation. It is GRA’s STEM core, and its the Global Centre for Risk and Innovation (GCRI) counterpart is Systems Science and Modelling.
Method notes
Clear accounts of methods, assumptions and limits, written for practitioners and reviewers.
Model validation guidance
Research on back-testing, benchmarking and challenger models, without validating any firm’s model.
Quantitative annexes
Method annexes for GRA finance-readiness dossiers, and teaching material.
GRA is a nonprofit business league for the financial services industry. It does not lend, underwrite, rate, invest, advise on or arrange transactions, and readiness work is not approval. Competition safeguards are designed and set out in a written policy.
Reserving workbench.
The screen a team would use to follow this field: its measures, states and decisions. Names are examples and values are illustrative.
The questions this field works on.
Loss distributions
Frequency, severity and aggregate loss, and how tails are estimated.
TopicExtreme value methods
Tail estimation for hazards, markets and operational losses.
TopicClimate-adjusted pricing questions
How non-stationary hazard changes pricing assumptions.
TopicCredibility and sparse data
Working where loss history is short or missing.
TopicDependence and correlation
Copulas, contagion and why correlations break in stress.
TopicCapital and solvency models
Internal models, standard formulas and their assumptions.
TopicQuantitative model validation
Back-testing, benchmarking and challenger models.
TopicMortality and longevity
Health, heat and demographic change in life and pension risk.
Loss exceedance, average annual loss and probable maximum loss.
The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every catastrophe model uses. Teaching model with stated illustrative values, not a prediction.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
Methods
- Gumbel extreme-value hazard Gumbel (1958), Statistics of Extremes, Columbia University Press
- Lognormal fragility (vulnerability) FEMA (2003), HAZUS-MH Technical Manual
- Exceedance-probability curve and average annual loss Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Actuarial and Quantitative Risk in Foundry
- Tested at Actuarial and Quantitative Risk in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Actuarial and Quantitative Risk in Reports
- Taught in Faculty of Finance and Economics in Academy
What the work is read against.
The platform does not set standards. It reads its evidence against the frameworks that competent bodies have adopted, so a finding can be placed where a decision-maker already works.
| Framework or standard | Body | What it sets |
|---|---|---|
| International Standards of Actuarial Practice | International Actuarial Association (IAA) | Model standards for actuarial work, including general practice, IFRS 17, ERM, and models |
| Actuarial Standards of Practice | Actuarial Standards Board (United States) | Professional standards on topics including modelling, credibility, risk classification and catastrophe modelling |
| Insurance Capital Standard | International Association of Insurance Supervisors (IAIS) | A group-wide prescribed capital requirement for internationally active insurance groups, adopted after a monitoring period |
| Solvency II | European Union, with EIOPA technical standards | Risk-based capital calibrated to a one-in-two-hundred-year loss over one year, with standard formula and internal model routes |
| IFRS 17 Insurance Contracts | International Accounting Standards Board | Measurement of insurance liabilities, including the risk adjustment for non-financial risk |
| Actuarial Profession Standards and Technical Actuarial Standards | Institute and Faculty of Actuaries; Financial Reporting Council (United Kingdom) | Professional conduct and technical standards for actuarial work in the United Kingdom |
| CMI Mortality Projections Model | Continuous Mortality Investigation of the Institute and Faculty of Actuaries | A widely used model for projecting mortality improvements in the United Kingdom |
| Mortality improvement scales | Society of Actuaries (United States) | Published scales and tools for projecting mortality improvement for pension and insurance use |
Trace a shock in Actuarial and Quantitative Risk to the balance sheet.
Assembled from the Nexus Standards area profile Insurance and reinsurance: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Wildfire
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
- Slope slide (set off downstream)
- Rockfall (set off downstream)
- Tsunami (set off downstream)
Systems and couplings
- Insurance and reinsurance
- Banking Coupling
- Development finance institutions Coupling
- Humanitarian and civil society Coupling
- Agriculture and land use Coupling
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-F3Insurance, catastrophe risk and protection gapsNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-U17Universe platform profile: Insurance NexusNRS-H71Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)NRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-F8Parametric and index-based risk transfer
Observation
- OECD Data Explorer (incl. Creditor Reporting System, insurance and pension statistics) (listed for reference)
- Insurance statistics (listed for reference)
- USGS earthquake GeoJSON summary feeds (listed for reference)
- Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)
- Direct economic loss attributed to disasters in relation to global gross domestic product
- (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
What Actuarial and Quantitative Risk does.
Actuarial and quantitative risk is the mathematics behind insurance and financial risk estimates, from loss distributions to capital models. Global Risks Alliance’s platform explains loss and extreme value methods, climate-adjusted pricing, credibility, dependence, solvency models, validation and mortality and longevity, under professional standards. It gives no actuarial opinions.
Its community
Actuaries; quantitative analysts; catastrophe modellers; risk modellers; model validators; academic statisticians.
Its methods
Actuarial mathematics; extreme value theory; stochastic modelling; credibility and pricing theory; model validation.
Its outputs
Method notes; model validation guidance; quantitative annexes to finance-readiness dossiers; teaching material.
What Actuarial and Quantitative Risk means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Exposure and loss at return periods
How losses from Actuarial and Quantitative Risk grow with severity, gross and net of reinsurance, against capital.
| Return period | Annual probability | Gross loss | Net loss | Net, capital = 100 |
|---|---|---|---|---|
| 1 in 10 | 0.10 | 14 | 10 | 10 |
| 1 in 50 | 0.02 | 33 | 21 | 21 |
| 1 in 100 | 0.01 | 46 | 27 | 27 |
| 1 in 250 | 0.004 | 68 | 39 | 39 |
| Average annual loss | 3.1 | 2.2 | 2.2 |
Transmission channels
How a shock in Actuarial and Quantitative Risk reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
Why quantitative models fail when they matter most.
Actuarial science and quantitative finance exist to put numbers on uncertainty: how often claims occur and how large they are, how long people live, how asset values move and how much capital is needed to survive bad years. These methods are powerful, and they underpin the solvency of insurers, pension funds and banks. Their failures are also well documented. Models calibrated on calm periods underestimated losses in financial crises; catastrophe models have been surprised by events outside their historical experience; correlations that looked stable broke down under stress; and longevity assumptions have repeatedly needed revision.
Three features make these failures systemic. First, the tail of the loss distribution, where rare but severe outcomes sit, is precisely where data are scarcest and estimates least certain. Second, dependence between risks tends to rise in a crisis, so diversification that looked reliable disappears when it is needed. Third, the world changes: climate change, medical advances, technology and new financial structures make the past an imperfect guide to the future. When many institutions use similar models and assumptions, their errors can be correlated too.
Good quantitative practice responds by stating uncertainty honestly, testing models against data they were not built on, challenging them with alternatives and being clear about what they cannot represent. That practice is shared across insurance, banking, pensions and financial stability, which is why it needs a common professional home.
Exposure, hazard and the exceedance curve.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
Grounded in mathematics, connected across three institutions.
The platform stewards actuarial mathematics and financial and risk engineering in the STEM Commons, and applies extreme value theory and tail risk, probability and statistics, uncertainty quantification and model validation and high-performance, cloud and edge computing. It sits in the fields of insurance and catastrophe, methods and modelling and capital markets and investment.
At The Global Centre for Risk and Innovation (GCRI), its counterparts are Systems Science and Modelling, Systemic Risk Management, Labs and Foundry, where reproducible compute is designed to run. Within GRA, the platform supplies methods to Reinsurance and Catastrophe, Insurance Nexus and AI and Financial Model Risk, and actuarial methods remain a chapter topic of Reinsurance and Catastrophe. Research feeds the Methods and Technical and Finance and Insurance Readiness series, and the Universe theme shared quantitative methods for an edition tests them in an exercise setting.
The areas of insurance and catastrophe this platform takes on.
Actuarial and Quantitative Risk studies the mathematics behind insurance and financial risk estimates: loss distributions, extreme value methods, climate-adjusted pricing, credibility when data are sparse, dependence between risks, capital and solvency models, model validation and mortality and longevity. Its pages set out how each method works, where it fails, which data and professional standards apply, and who signs off the results, with every source labelled. Global Risks Alliance shares professional understanding and does not give actuarial opinions or advice.
Loss distributions
Frequency and severity models, aggregate loss distributions and the choices that shape their tails.
Extreme value methods
Modelling the largest losses and hazard extremes beyond the observed record, with their uncertainty.
Climate-adjusted pricing
Adjusting premiums and loss estimates for changing hazard rather than relying on past averages alone.
Credibility and sparse data
Blending limited own experience with wider data, and estimating risks with few observations.
Dependence and correlation
How risks move together, especially in the tail, and how diversification credit is justified.
Capital and solvency models
Standard formulas and internal models that set the capital insurers and banks must hold.
Model validation
Testing, back-testing and independent review of quantitative models against professional and supervisory standards.
Mortality and longevity
Mortality tables, improvement projections and the risk that people live longer than assumed.
Built on named science, technology, engineering and mathematics.
Every claim on Actuarial and Quantitative Risk rests on methods with a canonical page in the STEM Commons, so readers can see what a finding depends on and where its limits lie.
How work moves in this field.
A question becomes evidence, a governed build, an organised community, a controlled test, a reviewed publication and, finally, a decision taken by the competent authority under its own rules.
- Question Universe National, regional or institutional priority. Question in Actuarial and Quantitative Risk
- Evidence Foundry STEM foundation of the platform. Evidence in Actuarial and Quantitative Risk
- Build Foundry Foundry. Build in Actuarial and Quantitative Risk
- Organise Universe Nexus ecosystem. Organise in Actuarial and Quantitative Risk
- Test Universe Universe (independent site). Test in Actuarial and Quantitative Risk
- Publish Reports Reports. Publish in Actuarial and Quantitative Risk
- Decide Competent authority Competent authorities and institutions, through NXS-RLS (participants, agreements and handoffs). Decide in Actuarial and Quantitative Risk
- Renew Universe Corrections and the next annual agenda. Renew in Actuarial and Quantitative Risk
Each step of the cycle in the pillar it runs in; the decision stays with the competent authority, outside the programme.
- Labs
- Foundry
- Universe
- Nexus Standards
- Reports
- Risk Academy
- Competent authority (outside the programme)
How this works across the six pillars.
Actuarial and Quantitative Risk works through six pillars of one annual programme, led by the Nexus Consortium, with GCRI, the Global Risks Forum (GRF) and GRA each contributing in its role: explore, experiment and prototype in Labs, build, simulate and prepare in Foundry, convene and test in Universe, codify, correct and apply in Nexus Standards, publish in every format in Reports, teach in the open in Risk Academy.
handoff in playrecorded handoffno direct handoff
Programme yearAll phases
Route
The whole programme
Every pillar hands its work to another; together they close the year’s feedback loops.
L · F · U · S · R · A
- Pillars
- 6 of 6
- Handoffs
- 18 of 18
- Loops
- 17 closed
Handoffs in play
- F01Universe → Foundry · Admitted mission becomes a cycle project with a decision caseReadiness preparation
- F02Foundry → Universe · Core Build Report and go or no-go evidenceControlled build
- F03Foundry → Reports · Sealed observations and indicator series behind a profileCloseout
- F04Universe → Reports · After-action and scenario findings for chapters 10 and 13After-action review
and 14 more
- LabsWhere questions become experiments and prototypes: new methods, data and tools are tried in the open, with their limits on record, before the Foundry builds them, under the Nexus Consortium.Labs
- FoundryWhere exposure and scenario evidence is built so supervisors, insurers and investors can read it.Actuarial and Quantitative Risk in FoundryFoundry
- UniverseWhere scenarios are tested in controlled exercises, and finance-readiness dialogues read what they mean for capital and cover.Actuarial and Quantitative Risk in Universeuniverse.nexus
- Nexus StandardsWhere disclosure and data rules are codified so risk evidence is comparable for finance and insurance.Finance, Insurance and the Economy in Standardsnexustandards.com
- ReportsWhere sector analysis and the finance and insurance readiness series are published, with exposure and limits stated.Actuarial and Quantitative Risk in ReportsReports
- Risk AcademyWhere finance, insurance and public-finance professionals learn to read risk evidence, on Risk Academy.Faculty of Finance and Economics in Academyrisksacademy.com
The same field, seen three ways.
GCRI studies a field as science, GRF governs it and GRA builds shared financial understanding of it. Actuarial and Quantitative Risk links to its counterparts so that work on one side is visible on the others.
Pages in Actuarial and Quantitative Risk.
Mandate and publisher
PageField overview
PageResearch and evidence
PageInnovation and practical technology
PageProgrammes and projects
PageData and technical methods
PageSpecialist community
PageLearning and professional development
PageEvents and technical dialogues
PageResearch and publications
PageParticipation paths
PageContact the specialist team
PageTrust, rights and status
PageSTEM foundation of Actuarial and Quantitative Risk
PageHow work moves in this field
PageBuild in Foundry
PageTest in Universe
PagePublish in Reports
PageThe same field across the Consortium
PageTopics
Questions and answers.
Who publishes Actuarial and Quantitative Risk?
Is Actuarial and Quantitative Risk active?
How can I contribute?
Where are findings published?
Which values on this platform are real?
How do I report an error?
Does the platform validate or approve models?
Is this a professional actuarial body?
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.
- EmailGateway GW-GRASend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGlobal Risks AllianceSee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
How Actuarial and Quantitative Risk works.
Choose a view and press Play to follow the platform’s process, its programme year, the standards it applies and how its findings are published. The outline shows how the work is designed to run, not the status of any decision.
Actuarial and Quantitative Risk
How Actuarial and Quantitative Risk works
Platforms · Programme year · Standards · Reports
A platform joins a field to named methods, labelled evidence and the outputs it publishes, without becoming the authority it serves.
01 Scope
- 01Field and familiesStart · The platform leadThe home states the field, its families and who it serves.
- 02Topic dossiersRecord · Platform authorsTopics explain the subject with labelled evidence.
02 Evidence
- 03Evidence labelsRecord · GCRIEach value is labelled: observed, published statistics, estimated, modelled, scenario or unknown.
- 04MethodsStep · Method ownersMethods come from the STEM Commons, each with one canonical page.
03 Work
- 05Scenarios on the systemsModel · Foundry pillarScenarios run on the Nexus systems, labelled as scenario.
- 06Standards that applyRecord · Nexus StandardsThe platform names the standards that apply. A validator report is evidence for review.
04 Publish
- 07EditionsRecord · ReportsFindings are published through Reports with their limits.
- 08Authority decidesOutcome · The competent authorityDecisions and execution stay with the government, operator or licensed institution.
Actuarial and Quantitative Risk home · Platforms · Shared methodsAn outline of how the work is designed to run; it does not report the status of any decision or record.
Take part in Actuarial and Quantitative Risk.
Start from who you are. Answer an open call. Or form and lead a competence cell on the Actuarial and Quantitative Risk platform.
National institutions
Ministries, statistics offices and agencies: a national desk and chapter, and a seat in the working groups. National chapters
Sectors and professions
Operators, regulators, banks, insurers and professional bodies whose work touches Actuarial and Quantitative Risk. Participate
Learning
Courses and practice, with a record of every course completed. Free to study. Open learning
Standards
Draft, review and adopt the methods and data standards with the working groups. Working groups
Communities and research
Universities, civil society and individual experts: projects, data and review. Write to the team
| Open call | Seeks | Closes |
|---|---|---|
| Founding call for experts to the working groups CALL-FOUNDING-EXPERTS | expert, editor, chair | 2026-12-15 |
| Open nomination for the three independent expert seats of the Global Standards Council CALL-GSC-IND | council-member | 2026-12-01 |
| Nominations of interim regional members (applies only if the transitional provision is decided) CALL-GSC-REG | council-member | 2026-12-15 |
| Review of the wave-1 risk core outlines (C1, C2, C3, C4, C8, C10, C11) CALL-REVIEW-WAVE1 | contributor | 2027-01-31 |
| Experts for the core standards of risk science: uncertainty and evidence grading, scales, structured expert judgement, tolerability, extremes and compound events, catastrophic and existential risk, ethics and equity, units and measurement (NRS-C17 to NRS-C24) CALL-CORE-SCIENCE | expert, editor, chair | 2027-01-31 |
| Independent reviewers for the families of the hazard atlas, three from at least two regions for each family (NHS2-R19), priority families first CALL-ATLAS-REVIEW | expert | 2027-03-31 |
| Members of the interim Frontier Safety Review Panel and red-team reviewers (biosafety and biosecurity, AI safety and security, critical-infrastructure security); applications treated in confidence CALL-FRONTIER-SAFETY-REVIEW | expert | 2026-11-30 |
| Experts in ontology, metrology, software verification and conformity assessment for the requirement register, the NHS-2 machine representation, the conformance test suites and the conformity assessment scheme CALL-CONFORMANCE-ONTOLOGY | expert, editor | 2027-01-31 |
Calls past their closing date are hidden automatically. Every call, proposal and route: open areas and calls.
Gather the cell
Bring together three to seven people with complementary skills: data, field practice, policy and communication.
Register it
Write to the team with the cell’s name, members, area and region. You receive a reference and a contact. Write to the team
Choose a charter
Pick one focus: research and evidence, data and maps, standards review, or community preparedness.
Learn together
Follow shared study paths at Risk Academy, with practice and a record of every course completed. Hazards and resilience
Lead and report
Hold regular sessions. Publish what the cell learns through Reports. Bring findings to the working groups. Working groups
What a national team or a local community can do on The Global Risks Index (NXS-GRX) today.
Your country’s profile
Open risk profiles for 195 countries, with sources and licences on every value. Open the Index
Correct or reply
Correct a value or reply for your country. The change is logged with its effect. Open the Index
Offer data
Offer a dataset. It enters through the licence gate, with your credit. Open the Index
Every figure on this page is indexed: hazards, climate, conflict, the world.
The Index is the Consortium’s indexing system for risk data. Every indicator, hazard, series and country profile carries its Nexus Standards identifier, its source and its licence, so what this page shows can be traced, corrected and reused.
- 01 · Sources205 registered sourcesEvery value keeps its source and licence; withheld values are counted.Data and licences
- 02 · Nexus StandardsNIS-1 and NHS-1451 indicators, 905 hazards; every record carries a UNI.Nexus Standards
- 03 · The Index54 series, 195 country profilesComputed on the standards; no value is published until an edition is released.The Global Risks Index
- 04 · This pageActuarial and Quantitative RiskMaps, figures and feeds read the Index in the context of hazards, climate, conflict.Public API
Series in this context hazards, climate, conflict
- GLBGlobal SystemsThe state of interdependent global risk: the twenty domain indices aggregated to the world, the five core systems (water, energy, food, health, biodiversity) and their couplings, the global tail register and the gap…NXS-GRX-AR-000002Hazards · Climate · Conflict
- THDisaster Risk Intelligence (DRI)Signals, observations, event streams, geospatial layers and scenario packs turned into structured risk intelligence for preparedness and planning.NXS-GRX-AR-000111Hazards · Climate · Conflict
- GSGlobal CitiesUrban exposure, heat, flooding, informal settlement, service dependency and the economic weight of cities at risk.NXS-GRX-AR-000068Hazards · Infrastructure · Climate
- GSGlobal ClimateChronic and acute physical climate hazard, transition exposure, adaptation gaps and scenario pathways to 2030, 2050 and 2100.NXS-GRX-AR-000065Climate · Hazards · Finance
Country profiles the world
Every country profile carries the source and licence of each value. Correct a value or reply
Hazard domains NHS-1
- Atmosphere and weatherNXS-GRX-AR-000135
- Solid EarthNXS-GRX-AR-000138
- Systemic and compound eventsNXS-GRX-AR-000148
Geodata and API open
Open layers on an equal-area hexagon grid, shown as densities per km² or as symbols: People · GHSL 2025People · WorldPop 2025Built-up areaPortsAirportsRoadsRailwaysRivers and lake centrelines
/api/v1/public/grix · https://nexustandards.com/uni/{uni}
Developers · Data · Made with Natural Earth, Public domain; GHSL and WorldPop, CC BY 4.0
Snapshot of the Index registries pinned 2026-10-23 · 22c6eed. Research and risk-analytics information: not a rating, an official classification, a warning, a benchmark or advice.
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