The Global Risks Alliance (GRA)
Property and casualty insurance covers damage to physical assets and liability to others. Its largest risks are accumulations: many policies hit by one storm, earthquake, flood or wildfire, or many liability claims driven by one product, practice or legal change. This page sets out how catastrophe and portfolio exposure develop, how they are modelled and measured, and which institutions use that evidence for capital, reinsurance and public policy.
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Property and casualty insurance covers damage to physical assets and liability to others. Its largest risks are accumulations: many policies hit by one storm, earthquake, flood or wildfire, or many liability claims driven by one product, practice or legal change. Catastrophe models and exposure data estimate those accumulations for capital and reinsurance decisions.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Property and casualty · Insurer Alpha · Quarter 3
| Property, commercial | 216 | 76 | ▲ 7 | Above |
| Property, household | 261 | 66 | ▲ 8 | Within |
| Motor | 212 | 66 | ▲ 0 | Within |
| Health | 140 | 84 | ▲ 5 | Watch |
Catastrophe and portfolio exposure. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Loss arises where a hazard footprint overlaps insured values whose vulnerability depends on construction, occupancy, age and local building codes. Exposure data quality often drives model error as much as the hazard does.
Policies that look independent share one event. Accumulation control tracks sums insured by zone and peril, and catastrophe models convert them into event loss tables and exceedance probabilities.
After a large event, demand surge in labour and materials, prolonged business interruption, contingent supply chain losses and coverage disputes raise the loss beyond the physical damage alone.
Liability claims can emerge years after the policy period through latent injuries, new legal theories, litigation funding and rising awards. Reserves set today absorb that development later.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Average annual loss | The expected insured loss per year from a peril, averaged over all simulated years of a catastrophe model. |
| Probable maximum loss | The loss a portfolio could suffer at a stated return period or exceedance probability, used to size reinsurance and capital. |
| Exceedance probability curve | A curve giving the probability that losses in a year exceed each amount, the main output of a catastrophe model. |
| Loss amplification | The rise in claims costs after a large event when demand for labour and materials drives up repair prices and settlement times. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Average annual loss | currency per year | annual | Catastrophe model output | MODELLED |
| Probable maximum loss at stated return period | currency | annual | Catastrophe model, occurrence and aggregate exceedance | MODELLED |
| Total insured value by peril zone | currency | quarterly | Insurer exposure data | REPORTED |
| Industry insured loss per event | currency | per event | Industry loss estimation services | ESTIMATED |
| Prior-year reserve development | change in prior-year reserves, currency | annual | Statutory loss development triangles | OFFICIAL |
| Non-modelled peril share of catastrophe losses | percent of catastrophe losses | annual | Insurer and broker loss analysis | ESTIMATED |
| Exposure data completeness | percent of locations with key attributes coded | per portfolio | Exposure data audit | OBSERVED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Verisk Property Claim Services (PCS) | Industry insured loss estimates for catastrophe events, used in index triggers | Proprietary, licensed |
| Munich Re NatCatSERVICE | Natural catastrophe event records with economic and insured losses | Proprietary; summary statistics published |
| Swiss Re Institute sigma natural catastrophe reports | Insured and economic catastrophe losses by region and peril | Proprietary research, reports free to read |
| Oasis Loss Modelling Framework | Open framework for running catastrophe models with exchangeable hazard, vulnerability and exposure | Open source, BSD licence |
| NOAA IBTrACS and Storm Events Database | Tropical cyclone tracks and severe weather event records | Public domain |
| Copernicus ERA5 and Emergency Management Service | Reanalysis hazard fields and rapid event footprints | Free and open, attribution |
| Global Earthquake Model (GEM) hazard and risk maps | Open seismic hazard and risk models | Creative Commons, terms per product |
| OpenFEMA National Flood Insurance Program data | United States flood policies and claims | Public domain |
Insurance Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Insurer and reinsurer chief risk officers | How much catastrophe exposure to accept by peril and region, and how much reinsurance to buy | Exceedance probability curves with model, vendor and data uncertainty shown |
| Insurance supervisors | Whether internal catastrophe models and accumulation controls are fit for capital purposes | Model validation evidence, exposure data quality and scenario losses |
| Reserving actuaries and audit committees | Whether casualty reserves are adequate given emerging claims trends | Loss development, trend analysis and scenario tests for latent claims |
| Building code and land-use authorities | Where construction standards or zoning would reduce insured and uninsured loss | Vulnerability evidence and loss differences by construction class |
Property insurance faces accumulation risk: a single event can produce claims from thousands of policies in the same area. Losses depend on hazard intensity, the location and value of exposures and the vulnerability of buildings. Growth in population and asset values in exposed areas has increased potential losses, independent of changes in hazard. Secondary perils, which individually cause smaller losses, have become significant contributors to annual insured losses.
Casualty and liability risks accumulate differently: through legal trends, latent injuries that emerge years later, and systemic events such as large cyber incidents affecting many firms at once. These risks are harder to model because their drivers include legal and social change as well as physical events.
Simulating hazard, exposure and vulnerability, drawing on extreme value theory and tail risk and climate and atmospheric science.
Measuring concentrations of insured value in areas exposed to the same event.
Studying past events to calibrate models and understand loss drivers.
Models carry substantial uncertainty for rare events, exposure data is often incomplete, and secondary perils and liability trends are difficult to model.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.