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	<title>Definition:Experience table - Revision history</title>
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	<updated>2026-06-23T20:12:39Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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		<id>https://www.insurerbrain.com/w/index.php?title=Definition:Experience_table&amp;diff=15551&amp;oldid=prev</id>
		<title>PlumBot: Bot: Creating new article from JSON</title>
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		<updated>2026-03-14T17:36:49Z</updated>

		<summary type="html">&lt;p&gt;Bot: Creating new article from JSON&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;📋 &amp;#039;&amp;#039;&amp;#039;Experience table&amp;#039;&amp;#039;&amp;#039; is an actuarial tool that presents statistical data on the frequency and severity of specific events — most commonly [[Definition:Mortality rate | mortality]], [[Definition:Morbidity | morbidity]], [[Definition:Lapse rate | lapse]], or [[Definition:Disability | disability]] rates — organized by age, gender, duration, or other relevant risk factors, and used by [[Definition:Insurance carrier | insurers]] to price products, calculate [[Definition:Insurance reserves | reserves]], and assess the adequacy of their assumptions. In [[Definition:Life insurance | life insurance]] and [[Definition:Health insurance | health insurance]], experience tables form the empirical backbone of [[Definition:Actuarial science | actuarial work]], translating raw claims and policy data into structured rates that can be applied prospectively to new or in-force business. Prominent examples include national [[Definition:Mortality table | mortality tables]] (such as the CSO tables in the United States, the CMI tables in the United Kingdom, and the standard mortality tables published by the Institute of Actuaries of Japan), as well as industry-specific morbidity and disability tables maintained by professional actuarial bodies and [[Definition:Insurance regulator | regulators]] across different jurisdictions.&lt;br /&gt;
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⚙️ Constructing an experience table involves collecting large volumes of historical data — typically spanning years or decades — on a defined population of insured lives or risks, then applying statistical techniques such as graduation (smoothing) to eliminate random fluctuation while preserving the underlying trend. The raw data is segmented by key rating variables: for a [[Definition:Mortality table | mortality table]], this usually means age, gender, smoker status, and sometimes underwriting class or policy duration since issue. Insurers often develop their own proprietary experience tables reflecting the characteristics of their particular book of business, since population-level tables published by government agencies or actuarial societies may not capture the effects of an insurer&amp;#039;s [[Definition:Underwriting | underwriting]] selection, distribution channel, or target demographic. In regulatory contexts, standard experience tables serve as prescribed or recommended benchmarks: for example, reserving regulations in many jurisdictions mandate minimum [[Definition:Mortality table | mortality tables]] for statutory [[Definition:Insurance reserves | reserve]] calculations, while [[Definition:Solvency II | Solvency II]] and other risk-based frameworks require insurers to demonstrate that their best-estimate assumptions are supported by credible experience data.&lt;br /&gt;
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💡 An experience table that accurately reflects the risk profile of a given insurance portfolio is one of the most consequential inputs in an insurer&amp;#039;s operations — small deviations in assumed mortality or morbidity rates, compounded over millions of policies and decades of coverage, can translate into enormous differences in [[Definition:Profitability | profitability]] and [[Definition:Solvency | solvency]] outcomes. Regular experience studies, in which an insurer compares actual claims outcomes against the rates predicted by its tables, are therefore a core discipline of actuarial practice and a frequent focus of [[Definition:Insurance regulator | regulatory]] review. The COVID-19 pandemic underscored how rapidly real-world experience can diverge from historical tables, prompting actuaries globally to reassess the assumptions embedded in their [[Definition:Mortality table | mortality]] and [[Definition:Morbidity | morbidity]] projections. As data availability improves — through [[Definition:Insurtech | insurtech]] innovations, wearable devices, and electronic health records — experience tables are becoming more granular and dynamic, enabling insurers to refine [[Definition:Risk segmentation | risk segmentation]] and move toward more personalized [[Definition:Pricing | pricing]] models.&lt;br /&gt;
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&amp;#039;&amp;#039;&amp;#039;Related concepts:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
{{Div col|colwidth=20em}}&lt;br /&gt;
* [[Definition:Mortality table]]&lt;br /&gt;
* [[Definition:Actuarial assumption]]&lt;br /&gt;
* [[Definition:Morbidity]]&lt;br /&gt;
* [[Definition:Lapse rate]]&lt;br /&gt;
* [[Definition:Life insurance]]&lt;br /&gt;
* [[Definition:Experience study]]&lt;br /&gt;
{{Div col end}}&lt;/div&gt;</summary>
		<author><name>PlumBot</name></author>
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