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
Keuangan spasialTempat modal bertemu bahaya, keterpaparan, dan transisi
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 Sistem. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on STEM Commons.
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.
Kerugian yang dapat diperkirakan per tahun oleh portofolio di wilayah terdampak, serta pada tingkat 1 dalam 100 dan 1 dalam 250 tahun yang dihargai perusahaan asuransi dan pemberi pinjaman, dari tiga blok yang sama yang digunakan setiap model katastrofe . Model pengajaran dengan nilai ilustratif yang dinyatakan, bukan prediksi.
Kendali
Pembacaan
| Periode ulang (tahun) | Intensitas (indeks intensitas) | Kerugian (% dari nilai) |
|---|---|---|
| 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:
Di seluruh enam pilar, dipimpin oleh 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 register sumber.
| Data | Peran | Label |
|---|---|---|
| Claims and exposure records | Model fitting | DILAPORKAN |
| Industry benchmarks | Priors and comparison | ESTIMASI |
| Household and enterprise surveys | Take-up and gap estimation | TERAMATI |
| Supervisory statistical returns | Market aggregates | Statistik yang diterbitkan |
Tampilan eksposur, kerugian, skenario, dan modal yang akan diminta oleh pejabat risiko, otoritas pengawas, atau kementerian keuangan, beserta kerangka pelaporan yang mereka gunakan. Strukturnya mengikuti praktik pengawasan dan aktuaria. Nilai bersifat ilustratif: tidak pernah merupakan harga, peringkat, atau saran.
How losses from probability and statistics in insurance grow with severity, gross and net of reinsurance, against capital.
| Periode ulang | Probabilitas tahunan | Kerugian bruto | Kerugian neto | Neto, modal = 100 |
|---|---|---|---|---|
| 1 dalam 10 tahun | 0.10 | 14 | 10 | 10 |
| 1 dalam 50 tahun | 0.02 | 33 | 21 | 21 |
| 1 dalam 100 tahun | 0.01 | 46 | 27 | 27 |
| 1 dalam 250 tahun | 0.004 | 68 | 39 | 39 |
| Rata-rata kerugian tahunan | 3.1 | 2.2 | 2.2 |
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
| Pemeriksaan | Cara pelaksanaannya |
|---|---|
| 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.
Ilmu Sistem dan Pemodelan stewards probability and statistics for the Consortium and reviews how it is applied across platforms.
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