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GRA · Insurance Nexus

Spatial financeWhere capital meets hazard, exposure and transition

Probability and statistics in insurance

Probability and statistics provide the language of insurance: frequency and severity, dependence, credibility and uncertainty. They make assumptions explicit, test them against experience, and show how much confidence any estimate of loss deserves, from a single portfolio’s claim frequency to a national estimate of how many households are uninsured.

01Qeyd

Probability and statistics in insurance on the record.

Method recordSTEM method
  1. RêbazÎhtîmal û statîstîk
  2. Applied inInsurance Nexus
  3. Canonical pageSTEM Commons
02Serlêdan

What probability and statistics does here.

Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In insurance and catastrophe, climate and hazards and health and biosecurity, statistical methods quantify the likelihood and uncertainty of losses across interconnected pergalan. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on the STEM Commons.

Frequency and severity modelling

Generalised linear models relate claim frequency and severity to risk factors, with offsets for exposure, so the contribution of each factor and its uncertainty can be read and reviewed.

Bayesian updating of assumptions

Prior assumptions from industry or benchmark data are updated with an insurer’s own experience, giving posterior estimates that state how far the data have moved the assumption.

Survey design for protection gaps

Household surveys on insurance take-up are designed and weighted so that estimates of uninsured shares represent the population, and their sampling error is reported with them.

03Loss model

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.

Kontrol

Typical annual hazard intensity (location)
1.00 intensity index
Belavbûna nirxên tund (pîvan)
0.80 intensity index
Tundiya ku nîvê nirxê tê de winda dibe
4.00 intensity index
Belavbûna lawazî
0.55
Para nirxê di nav şopa metirsî de
40%
Standarda parastinê (heyama vegerê)
50 sal

Xwendin

Windahiya salane ya navînî
0.51% of value
Windahî di 1 ji 100 salan de
24.5% of value
Windahî di 1 ji 250 salan de
28.4% of value
Tundî di 1 ji 100 salan de
4.68 intensity index
Jimar, rêbaz û çavkanî
Heyama vegerê (sal)Intensity (intensity index)Windahî (% ya nirxê)
10.090.00
21.270.00
41.910.00
72.470.00
133.000.00
243.510.00
444.020.00
824.5323.55
1545.0326.44
2875.5328.87
5366.0330.88
1,0006.5332.53

Rêbaz

  1. Metirsî: nirxên dawîn ên Gumbel Gumbel (1958), Statistics of Extremes, Columbia University Press
  2. Nazikiya lognormal (lawazî) FEMA (2003), HAZUS-MH Technical Manual
  3. Qada îhtîmala derbasbûnê û windahiya salane ya navînî Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
  4. Bandora bendavê Di Baldassarre and others (2013), Socio-hydrology, HESS 17
  5. Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. Hesasiyeta yek-yek (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Dane

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:

  • Çem Natural Earth
  • Dirêjahiyên bajarî Natural Earth
  • Bajar Natural Earth
  • Erdhejên M4.5+, hefteya borî (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
  • Rêjeya baranê, nîv saeta dawîn (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
  • Pêşbînî: herikîna çemê, 30 roj bi navber (GloFAS bi riya Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
  • Berfirehiya avakirî û niştecihbûnê, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Loss exceedance, average annual loss and probable maximum loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).

Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium

04Dane

What the method consumes.

Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DaneRolNîşan
Claims and exposure recordsModel fittingREPORTED
Industry benchmarksPriors and comparisonTEXMÎNKIRÎ
Household and enterprise surveysTake-up and gap estimationÇAVDÊRÎKIRÎ
Supervisory statistical returnsMarket aggregatesStatîstîkên weşandî
05Exposure, loss and capital

What probability and statistics in insurance 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 probability and statistics in insurance grow with severity, gross and net of reinsurance, against capital.

1 in 101 in 501 in 1001 in 250grossnet of reinsurance800
Return periodAnnual probabilityGross lossNet lossNet, capital = 100
1 in 100.10141010
1 in 500.02332121
1 in 1000.01462727
1 in 2500.004683939
Windahiya salane ya navînî3.12.22.2
Illustrative loss index for a example portfolio (capital = 100): the exceedance-probability form insurers and supervisors read. Not data on any institution, not a price or a rating.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
06Erêkirin

How results are checked.

Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.

Kontrol bikinHow it is done
Out-of-sample validationModels are fitted on one period or segment and tested on another; only test performance is reported
Residual diagnosticsResiduals are inspected for structure the model has missed, such as trends or clustering
Sensitivity to assumptionsIndependence, distribution and sampling assumptions are varied and the effect on the result is stated

Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.

08Stewardship

Who maintains the method.

Zanista pergalan û modelkirin stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

09Girêdayî

Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.

10Pirs

Pirs û bersiv.

What are the limits of probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
Çavdêrxaneya canonical method page lists every platform that applies it.
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rêzeya Pergalên Stratejîk 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.

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