https://www.insurerbrain.com/w/index.php?action=history&feed=atom&title=Definition%3AFrequency-severity_model 2026-06-17T13:00:08Z Revision history for this page on the wiki MediaWiki 1.43.8 https://www.insurerbrain.com/w/index.php?title=Definition:Frequency-severity_model&diff=6869&oldid=prev 2026-03-10T04:54:02Z

<p>Bot: Creating new article from JSON</p> <p><b>New page</b></p><div>📐 &#039;&#039;&#039;Frequency-severity model&#039;&#039;&#039; is an [[Definition:Actuarial analysis | actuarial]] framework that separately estimates how often [[Definition:Claim | claims]] occur ([[Definition:Frequency | frequency]]) and how large each claim tends to be ([[Definition:Severity | severity]]), then combines the two components to project total expected [[Definition:Loss | losses]] for a book of business. By decomposing loss experience into these two distinct dimensions, the model gives [[Definition:Insurance carrier | insurers]] a far more nuanced view than simply looking at aggregate loss totals, revealing whether a deteriorating [[Definition:Loss ratio (L/R) | loss ratio]] stems from more claims, costlier claims, or both.<br /> <br /> ⚙️ In practice, [[Definition:Actuary | actuaries]] fit statistical distributions to each component independently. Frequency is often modeled using Poisson or negative binomial distributions, reflecting the count nature of claim occurrences, while severity draws on lognormal, Pareto, or gamma distributions to capture the right-skewed nature of claim sizes. Once parameterized, the two distributions are convolved — often through simulation — to produce an aggregate loss distribution. This output drives [[Definition:Ratemaking | ratemaking]], [[Definition:Reserving | reserve]] setting, and [[Definition:Reinsurance | reinsurance]] purchasing decisions. For instance, an [[Definition:Excess of loss reinsurance | excess-of-loss]] treaty attaching at $1 million per occurrence is priced primarily off the severity tail, while a [[Definition:Quota share reinsurance | quota share]] arrangement responds proportionally to both components.<br /> <br /> 🎯 The model&#039;s real power lies in its ability to isolate and respond to trends. If [[Definition:Auto insurance | auto]] claim frequency drops due to advanced driver-assistance systems but severity climbs because of higher repair costs for sensor-laden vehicles, a carrier using a frequency-severity model can adjust pricing for each driver accordingly, rather than applying a blunt across-the-board change. [[Definition:Insurtech | Insurtech]] platforms and modern [[Definition:Predictive analytics | predictive analytics]] tools have made it easier to update frequency and severity assumptions in near-real time, feeding granular data from [[Definition:Telematics | telematics]], [[Definition:Internet of Things (IoT) | IoT sensors]], and [[Definition:Claims management system | claims systems]] directly into model recalibration. This granularity supports more responsive [[Definition:Underwriting | underwriting]] and more stable portfolio performance over time.<br /> <br /> &#039;&#039;&#039;Related concepts&#039;&#039;&#039;<br /> {{Div col|colwidth=20em}}<br /> * [[Definition:Frequency]]<br /> * [[Definition:Severity]]<br /> * [[Definition:Actuarial analysis]]<br /> * [[Definition:Ratemaking]]<br /> * [[Definition:Aggregate loss distribution]]<br /> * [[Definition:Predictive analytics]]<br /> {{Div col end}}</div>

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