GRA · Actuarial and Quantitative Risk
Spatial financeWhere capital meets hazard, exposure and transition
Dependencies and consequences
Quantitative risk work connects insurance, banking, pensions and public finance, because the same loss distributions and dependence assumptions drive pricing, capital and fiscal planning. This page maps those links.
- 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
What models depend on.
Hazard and climate
Catastrophe and pricing models depend on hazard science, which is changing as the climate warms.
Health and demography
Mortality, morbidity and longevity models depend on medicine, behaviour, heat exposure and population change.
Markets and inflation
Asset values, interest rates and claims inflation drive both sides of the balance sheet.
Exposure, hazard and the exceedance curve.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
How shared quantitative assumptions transmit risk.
- Common calibrations: standard formulas and widely used vendor models make firms move together.
- Correlation breakdown: diversification assumed in calm periods disappears in stress.
- Model change: a vendor update can shift capital across a market at once.
- Longevity: shared mortality tables link insurers and pension funds.
Field overview.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Who checks the quants.
Independent validation, actuarial review and supervisory model approval are the checks. Governance of model use in public decisions at GRF algorithmic public decisions; topic depth at mortality and longevity.
How model results become consequences.
Model outputs set premiums, reserves and capital. If models understate risk, institutions are under-capitalised and may fail when losses arrive, shifting costs to policyholders, policyholder protection schemes funded by industry, or the public. If models overstate risk or react sharply to new information, cover can become unaffordable or be withdrawn, widening protection gaps for households, firms and governments.
Model homogeneity creates its own dependency. When many institutions use similar vendor models, data and assumptions, their errors are correlated, and a single model revision can shift prices or capital across a market at once. This is one reason supervisors and validators value independent challenger models.
Where time horizons collide.
Many quantitative frameworks use one-year horizons for capital, while climate, ecological and demographic change unfold over decades. Pension and life liabilities, by contrast, span generations. Reconciling these horizons is a central methodological challenge, and nature-related risks, such as ecosystem degradation affecting flood exposure, are only beginning to enter models.
- Systemic and cascade analysis at The Global Centre for Risk and Innovation (GCRI): networks and cascades.
- Insurance market effects: insurance protection gaps at GRA.
- Loss data foundations: catastrophe loss data at GRA.
How dependencies are analysed.
Dependency analysis uses uncertainty quantification, sensitivity analysis, scenario design and dependence modelling. Every analysis states its assumptions, data and uncertainty; see methods. A dependency map describes how stress could travel; it is not a forecast. This is shared industry research on methods, not actuarial, investment or regulatory advice to any firm, and GRA does not validate, approve or sign off any model.
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.
Questions and answers.
Why does model homogeneity matter?
Do models include climate change?
Is this analysis firm-specific?
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