https://www.insurerbrain.com/w/index.php?action=history&feed=atom&title=Definition%3AActuarial_dataDefinition:Actuarial data - Revision history2026-04-29T11:58:46ZRevision history for this page on the wikiMediaWiki 1.43.8https://www.insurerbrain.com/w/index.php?title=Definition:Actuarial_data&diff=7204&oldid=prevPlumBot: Bot: Creating new article from JSON2026-03-10T12:39:54Z<p>Bot: Creating new article from JSON</p>
<p><b>New page</b></p><div>📊 '''Actuarial data''' refers to the quantitative and qualitative information that [[Definition:Actuary | actuaries]] collect, organize, and analyze to evaluate risk, price [[Definition:Insurance product | insurance products]], establish [[Definition:Loss reserve | reserves]], and assess the financial health of an [[Definition:Insurance carrier | insurance carrier]]. This encompasses [[Definition:Claims data | claims records]], [[Definition:Premium | premium]] volumes, [[Definition:Exposure | exposure]] counts, [[Definition:Policy | policy]] characteristics, demographic information, economic indicators, and any other dataset that feeds into [[Definition:Actuarial modeling | actuarial models]]. In the insurance context, data quality is not an abstract ideal — it directly determines the reliability of every number on which pricing, reserving, and [[Definition:Capital management | capital management]] decisions depend.<br />
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🔍 Gathering actuarial data involves pulling from an insurer's internal systems — [[Definition:Policy administration | policy administration]], [[Definition:Claims management | claims management]], [[Definition:Billing system | billing]] — and supplementing it with external sources such as [[Definition:Insurance Services Office (ISO) | ISO]] statistical filings, [[Definition:Bureau of Labor Statistics | government indices]], [[Definition:Catastrophe modeling | catastrophe model]] outputs, or [[Definition:Reinsurance | reinsurance]] bordereau. Actuaries spend considerable effort cleansing and reconciling these datasets: verifying that loss records tie to financial statements, that exposure measures are consistent over time, and that coding changes or system migrations have not introduced distortions. Under frameworks like [[Definition:Solvency II | Solvency II]], carriers must demonstrate formal [[Definition:Data governance | data governance]] standards, including documented data quality assessments, to satisfy regulators that the information underpinning technical provisions is accurate and complete.<br />
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💡 The consequences of poor actuarial data ripple far beyond the actuarial department. If historical [[Definition:Loss experience | loss experience]] is incomplete or miscoded, [[Definition:Ratemaking | rate indications]] can be systematically off, leading to [[Definition:Underpricing | underpriced]] business that erodes [[Definition:Underwriting profit | underwriting profit]] over time. Similarly, flawed data feeding into [[Definition:Reserving | reserve]] analyses may cause an insurer to under-report liabilities, creating a [[Definition:Reserve deficiency | reserve deficiency]] that surfaces years later. As [[Definition:Insurtech | insurtech]] platforms and advanced analytics tools become more prevalent, the volume and variety of available data are expanding rapidly — but the fundamental principle remains unchanged: actuarial conclusions are only as sound as the data on which they are built.<br />
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'''Related concepts'''<br />
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* [[Definition:Actuarial modeling]]<br />
* [[Definition:Data governance]]<br />
* [[Definition:Loss experience]]<br />
* [[Definition:Exposure]]<br />
* [[Definition:Claims data]]<br />
* [[Definition:Insurance Services Office (ISO)]]<br />
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