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📊 '''Experience analysis''' is the systematic study of actual insurance outcomes — such as [[Definition:Mortality | mortality]] rates, [[Definition:Morbidity | morbidity]] incidence, [[Definition:Lapse rate | lapse rates]], expense levels, and [[Definition:Investment return | investment returns]] — compared against the assumptions that were used to price products, set [[Definition:Reserves | reserves]], or calculate [[Definition:Embedded value | embedded value]]. It sits at the heart of [[Definition:Actuarial science | actuarial practice]] in both life and non-life insurance, providing the empirical feedback loop that keeps assumptions grounded in reality. Every major insurance market relies on experience analysis, though the specific regulatory and reporting frameworks — from [[Definition:IFRS 17 | IFRS 17]]'s requirement to update fulfilment cash flows to the actuarial opinion mandates of the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] in the United States — shape how frequently and rigorously the exercise is performed.

🔍 The process begins with extracting historical policy and claims data, segmenting it by relevant risk factors such as age, gender, product type, distribution channel, or geography. Actuaries then measure actual experience over a defined observation period and compare it to the expected experience implied by the prevailing assumption set. Statistical techniques — ranging from straightforward actual-to-expected (A/E) ratios to more sophisticated [[Definition:Generalized linear model (GLM) | generalized linear models]] and survival analysis — are used to identify trends, detect emerging risks, and quantify deviations. In life insurance, a company might discover that mortality among a particular cohort is improving faster than assumed, signaling the need to strengthen longevity reserves for [[Definition:Annuity | annuity]] business while potentially releasing mortality reserves on term life portfolios. In general insurance, experience analysis on [[Definition:Claims frequency | claims frequency]] and [[Definition:Claims severity | severity]] feeds directly into [[Definition:Ratemaking | ratemaking]] and [[Definition:Reserving | reserving]] decisions.

💡 Robust experience analysis underpins nearly every consequential decision an insurer makes. Pricing that drifts too far from actual experience erodes [[Definition:Underwriting profit | underwriting margins]]; reserving assumptions that lag behind real-world trends can mask deterioration until it surfaces as a sudden earnings hit. Regulators and [[Definition:External auditor | external auditors]] increasingly expect documented, transparent experience studies as part of the [[Definition:Appointed actuary | appointed actuary]]'s work. Under IFRS 17, for example, changes in assumptions driven by experience analysis flow directly into the [[Definition:Contractual service margin (CSM) | contractual service margin]] or the [[Definition:Profit and loss | profit and loss statement]], making the quality of experience studies a first-order financial reporting concern. For [[Definition:Insurtech | insurtech]] firms leveraging new data sources, experience analysis also serves as the proving ground for whether novel [[Definition:Predictive model | predictive models]] and alternative risk factors deliver genuinely better outcomes than traditional approaches.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Actuarial assumption]]
* [[Definition:Reserving]]
* [[Definition:Mortality]]
* [[Definition:Lapse rate]]
* [[Definition:IFRS 17]]
* [[Definition:Ratemaking]]
{{Div col end}}

Definition:Experience analysis - Revision history

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