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.
GRA · Insurance Nexus
Finanças espaciaisOnde o capital encontra ameaça, exposição e transição
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.
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 sistemas. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on os bens comuns CTEM.
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.
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.
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.
A perda que uma carteira na área abrangida pode esperar por ano e nos níveis de 1 em 100 e 1 em 250 que seguradoras e credores avaliam, a partir dos mesmos três blocos que qualquer modelo de catástrofes utiliza. Modelo didático com valores ilustrativos declarados, não uma previsão.
Controles
Leituras
| Período de retorno (anos) | Intensidade (índice de intensidade) | Perda (% do valor) |
|---|---|---|
| 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 |
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:
Nos seis pilares, sob a liderança do Nexus Consortium
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 registro de fontes.
| Dados | Função | Rótulo |
|---|---|---|
| Claims and exposure records | Model fitting | REPORTADO |
| Industry benchmarks | Priors and comparison | ESTIMADO |
| Household and enterprise surveys | Take-up and gap estimation | OBSERVADO |
| Supervisory statistical returns | Market aggregates | Estatísticas publicadas |
As vistas de exposição, perda, cenários e capital que um responsável pelo risco, um supervisor ou um ministério das finanças pediria, com os quadros de referência perante os quais reportam. As estruturas seguem a prática de supervisão e atuarial. Os valores são ilustrativos: nunca preços, notações ou aconselhamento.
How losses from probability and statistics in insurance grow with severity, gross and net of reinsurance, against capital.
| Período de retorno | Probabilidade anual | Perda bruta | Perda líquida | Líquido, capital = 100 |
|---|---|---|---|---|
| 1 em 10 | 0.10 | 14 | 10 | 10 |
| 1 em 50 | 0.02 | 33 | 21 | 21 |
| 1 em 100 | 0.01 | 46 | 27 | 27 |
| 1 em 250 | 0.004 | 68 | 39 | 39 |
| Perda anual média | 3.1 | 2.2 | 2.2 |
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
| Verificar | Como é feito |
|---|---|
| Out-of-sample validation | Models are fitted on one period or segment and tested on another; only test performance is reported |
| Residual diagnostics | Residuals are inspected for structure the model has missed, such as trends or clustering |
| Sensitivity to assumptions | Independence, 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.
Ciência e modelagem de sistemas stewards probability and statistics for the Consortium and reviews how it is applied across platforms.
Relatórios 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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