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
空间金融资本与危害、风险暴露和转型的交汇之处
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 系统. 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.
处于覆盖范围内的投资组合每年预期损失,以及保险公司和贷款机构定价所依据的 1/100 和 1/250 水平下的预期损失,均来自每个 巨灾模型 所用的同样三个模块。教学模型,所用数值为注明的示意值,并非预测。
控制措施
读数
| 重现期(年) | 强度(强度指数) | 损失(占价值的 %) |
|---|---|---|
| 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:
贯穿六大支柱,由 Nexus Consortium 牵头
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 来源登记册.
| 数据 | 角色 | 标签 |
|---|---|---|
| Claims and exposure records | Model fitting | 已报告 |
| Industry benchmarks | Priors and comparison | 估算 |
| Household and enterprise surveys | Take-up and gap estimation | 观测 |
| Supervisory statistical returns | Market aggregates | 已发布统计 |
风险官、监管者或财政部会要求的风险暴露、损失、情景和资本视图,以及它们据以报告的框架。结构遵循监管和精算实务。数值为说明性:绝非价格、评级或建议。
How losses from probability and statistics in insurance grow with severity, gross and net of reinsurance, against capital.
| 重现期 | 年概率 | 总损失 | 净损失 | 净额,资本 = 100 |
|---|---|---|---|---|
| 1/10(10 年一遇) | 0.10 | 14 | 10 | 10 |
| 1/50(50 年一遇) | 0.02 | 33 | 21 | 21 |
| 1/100(100 年一遇) | 0.01 | 46 | 27 | 27 |
| 1/250(250 年一遇) | 0.004 | 68 | 39 | 39 |
| 年均损失 | 3.1 | 2.2 | 2.2 |
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
| 核查 | 如何开展 |
|---|---|
| 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.
系统科学与建模 stewards probability and statistics for the Consortium and reviews how it is applied across platforms.
报告 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.
切勿发送个人数据、敏感基础设施细节或您无权分享的任何内容。贡献在使用前会经过审查;发送贡献不赋予署名权或编辑权,赞助也绝不能换取研究结论。
本网站上的地图、数据和图表仅作为背景和一般信息展示。它们不是建议、预测或预警;信息的局限见免责声明和数据说明。
This site uses only strictly necessary cookies to work and to remember your privacy choices. Nothing loads from other companies, and we never sell or share your personal information.
This site uses only strictly necessary cookies to work and to remember your privacy choices. Nothing loads from other companies, and we never sell or share your personal information. We run no advertising. You can review your choices at any time through Privacy choices at the foot of every page.