The Global Risks Alliance (GRA)
Every catastrophe model, price and parametric trigger depends on loss data: industry loss estimates, insurer claims, economic loss records and event footprints. These data are incomplete, revised over time and often proprietary. This page sets out how catastrophe loss data are produced and where they mislead, the sources that exist with their licences, and who decides how they are used.
Catastrophe loss data · Cedent C-07 · Peril: tropical cyclone
| Retention | 0 | 47 | n/a | |
| Layer 1 | 79 | 101 | 12 | 1-in-9 |
| Layer 2 | 170 | 107 | 2 | 1-in-53 |
| Layer 3 | 274 | 294 | 1.4 | 1-in-248 |
Fig.Catastrophe model results. Design wireframe with placeholder names and illustrative values, not a record or a live system.
Catastrophe loss data are the records of insured, economic and claims losses from disasters that calibrate models, price contracts and trigger payouts. They differ in definition, are revised over time and are often proprietary. Modellers, contract parties, public authorities and supervisors decide how they are used.
How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.
Loss reporting and data quality. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Industry loss estimates are revised for months or years after an event as claims develop. Contracts triggered on early estimates may settle on different numbers.
Economic, insured, total and normalised losses are measured differently by each provider. Comparing them without reconciliation produces false conclusions.
Detailed claims and industry loss data are held by insurers and private aggregators. Public access is limited, which constrains research and public models.
Loss data are rich where insurance penetration is high and thin elsewhere. Models for low-income regions lack calibration data.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Industry loss estimate | A reporting agency's estimate of total insured losses from an event across the market. |
| Economic loss | Total direct losses from an event to property, infrastructure and income, whether insured or not. |
| Loss development | The change in estimated loss for an event as claims are reported and settled. |
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 |
|---|---|---|---|---|
| Industry insured loss per event | currency | per event | Industry loss estimation services | ESTIMATED |
| Revision of industry loss estimates | percent change from first to final estimate | per event | Loss estimation services | ESTIMATED |
| Economic loss per event | currency | per event | Disaster databases | ESTIMATED |
| Events recorded in public disaster databases | count by peril and region | annual | EM-DAT | REPORTED |
| Open claims datasets available | count | on change | Public data portals | OBSERVED |
| Time to final industry loss estimate | months | per event | Loss estimation services | 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 |
|---|---|---|
| EM-DAT international disaster database | Disaster events with deaths, affected people and economic losses | Free for non-commercial use, CRED terms |
| Verisk Property Claim Services (PCS) | United States and international industry insured loss estimates | Proprietary, licensed |
| PERILS AG | Industry exposure and loss data for selected markets | Proprietary, licensed |
| OpenFEMA National Flood Insurance Program claims | United States flood claims | Public domain |
| NOAA Storm Events Database | Severe weather events and damage estimates | Public domain |
| DesInventar Sendai | National disaster loss databases | Free, UNDRR terms |
Reinsurance and Catastrophe 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 |
|---|---|---|
| Catastrophe modellers | Which loss data to use for calibration | Data sources, definitions and coverage |
| Contract parties and calculation agents | Which loss reporting agent and timeline apply to a trigger | Reporting practice and revision history |
| Public authorities | Whether to publish claims and loss data | Comparable open data practice and privacy safeguards |
| Researchers and supervisors | How to compare losses across sources | Definition reconciliation |
Insured losses are compiled from claims and industry surveys, while economic losses include uninsured damage and are estimated by various methods. Different sources may report different totals for the same event, and estimates change as claims develop.
Coverage is uneven. Records are more complete for large events, recent decades and high-income countries. Smaller events, which can add up to large totals, and losses in regions with low insurance penetration are under-recorded.
Trends in raw losses reflect growth in population, property values and prices as well as hazards. Normalisation adjusts for these factors, but methods differ and can lead to different conclusions.
Compiled records of events and losses. Definitions and thresholds differ between databases.
Adjusting historical losses for exposure and price changes. Assumptions affect results.
Using detailed claims to calibrate vulnerability, using actuarial methods. Access is restricted.
Fitting distributions to loss records, using extreme value theory. Short records limit tail estimates.
Loss figures from different sources should not be combined without checking definitions. Analyses state sources and their vintages.
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.