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🔬 '''Experience study''' is an actuarial analysis that examines historical data on a defined population — such as a block of [[Definition:Life insurance | life insurance]] policyholders or a [[Definition:Pension | pension]] plan's participants — to measure how actual outcomes compare to the assumptions embedded in [[Definition:Reserve | reserves]], pricing, or plan funding. In insurance, these studies most commonly evaluate [[Definition:Mortality | mortality]], [[Definition:Morbidity | morbidity]], [[Definition:Lapse rate | lapse]], and [[Definition:Persistency | persistency]] rates, providing the empirical foundation that [[Definition:Actuary | actuaries]] rely on to calibrate their models.

📈 Conducting an experience study involves assembling exposure records and [[Definition:Claim | claim]] or decrement events over a multi-year observation window, then calculating actual rates of occurrence for the variable under review. Actuaries segment the data by dimensions such as age, gender, policy duration, [[Definition:Underwriting | underwriting]] class, or product type to uncover patterns that aggregate figures might mask. The observed rates are compared against the assumptions currently in use — or against published industry tables like the [[Definition:Commissioners Standard Ordinary (CSO) table | CSO mortality tables]] — and the gaps inform whether assumptions need updating. Large [[Definition:Insurance carrier | insurers]] with credible volumes of data may conduct proprietary studies annually, while smaller companies often supplement internal data with industry-wide studies published by organizations such as the [[Definition:Society of Actuaries (SOA) | Society of Actuaries]].

🎯 Without rigorous experience studies, an insurer risks mispricing its products or holding inadequate reserves — either of which can erode [[Definition:Solvency | solvency]] or competitiveness. Regulators and [[Definition:Rating agency | rating agencies]] expect companies to demonstrate that their assumptions reflect recent, credible experience rather than outdated benchmarks. For [[Definition:Insurtech | insurtech]] firms entering the life or health space, an early investment in structured data collection pays dividends down the line, because the depth and quality of experience data directly determine how quickly assumptions can be refined and how confidently new products can be launched.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Actuarial assumption]]
* [[Definition:Mortality table]]
* [[Definition:Lapse rate]]
* [[Definition:Reserve]]
* [[Definition:Persistency]]
* [[Definition:Society of Actuaries (SOA)]]
{{Div col end}}

Definition:Experience study - Revision history

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