https://www.insurerbrain.com/w/index.php?action=history&feed=atom&title=Definition%3AFrequency_%28insurance%29 2026-06-16T10:09:17Z Revision history for this page on the wiki MediaWiki 1.43.8 https://www.insurerbrain.com/w/index.php?title=Definition:Frequency_(insurance)&diff=13070&oldid=prev 2026-03-13T12:30:41Z

<p>Bot: Creating new article from JSON</p> <p><b>New page</b></p><div>📊 &#039;&#039;&#039;Frequency (insurance)&#039;&#039;&#039; measures how often [[Definition:Insurance claim | claims]] or [[Definition:Loss | loss]] events occur within a defined exposure base over a given period — typically expressed as claims per policy, per unit of exposure, or per earned [[Definition:Insurance premium | premium]] dollar. It is one of the two foundational dimensions of loss analysis in [[Definition:Actuarial science | actuarial science]] and [[Definition:Underwriting | underwriting]], the other being [[Definition:Severity (insurance) | severity]], which measures the average cost per claim. Together, frequency and severity form the building blocks from which insurers project expected losses, set [[Definition:Insurance premium | premium]] rates, and establish [[Definition:Reserves | reserves]] across every line of business and every major insurance market worldwide.<br /> <br /> ⚙️ Actuaries calculate claim frequency by dividing the number of reported claims by the relevant exposure measure — earned car-years in [[Definition:Auto insurance | motor insurance]], employee hours in [[Definition:Workers&#039; compensation insurance | workers&#039; compensation]], or policy count in [[Definition:Homeowners insurance | homeowners]] coverage, for instance. The resulting frequency rate is then analyzed for trends over time, segmented by rating variables such as geography, policyholder demographics, or coverage type, and adjusted for known distortions like reporting lags or changes in [[Definition:Claims handling | claims handling]] practices. Statistical distributions commonly used to model claim frequency include the Poisson, negative binomial, and zero-inflated models, each suited to different patterns of claim occurrence. Regulators in most jurisdictions — from the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] in the United States to [[Definition:Solvency II | Solvency II]] supervisory authorities in Europe — expect insurers to demonstrate that their frequency assumptions are grounded in credible data and sound methodology when filing rates or calculating [[Definition:Technical provisions | technical provisions]].<br /> <br /> 📈 Shifts in claim frequency carry outsized strategic significance because even small percentage changes, applied across a large book of business, produce material impacts on [[Definition:Loss ratio | loss ratios]] and profitability. The COVID-19 pandemic provided a dramatic natural experiment: [[Definition:Auto insurance | motor]] claim frequency plummeted as driving declined, generating windfall profits for auto insurers, while certain [[Definition:Liability insurance | liability]] and [[Definition:Health insurance | health]] lines saw frequency spike. Longer-term frequency trends — such as the decline in auto accident frequency driven by advanced driver-assistance systems, or the rise in [[Definition:Cyber insurance | cyber]] claim frequency as ransomware proliferates — shape product pricing, [[Definition:Reinsurance | reinsurance]] purchasing strategies, and capital allocation decisions. Understanding and accurately forecasting frequency is therefore not merely a technical exercise but a core competitive capability for insurers operating in any market.<br /> <br /> &#039;&#039;&#039;Related concepts:&#039;&#039;&#039;<br /> {{Div col|colwidth=20em}}<br /> * [[Definition:Severity (insurance)]]<br /> * [[Definition:Frequency and severity]]<br /> * [[Definition:Loss ratio]]<br /> * [[Definition:Actuarial science]]<br /> * [[Definition:Claims handling]]<br /> * [[Definition:Exposure (insurance)]]<br /> {{Div col end}}</div>

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