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📐 '''Pricing model''' is the quantitative framework an [[Definition:Insurance carrier | insurance carrier]] uses to determine the [[Definition:Premium | premium]] it charges for a given [[Definition:Insurance policy | policy]] or [[Definition:Class of business | class of business]]. At its foundation, the model estimates the expected cost of [[Definition:Claim | claims]] — incorporating [[Definition:Frequency | frequency]], [[Definition:Severity | severity]], and trend factors — and layers on provisions for [[Definition:Expense ratio | expenses]], [[Definition:Profit margin | profit]], and [[Definition:Contingency loading | contingency]]. Whether built by an in-house [[Definition:Actuary | actuarial]] team or adapted from bureau rates filed by organizations such as the [[Definition:Insurance Services Office (ISO) | Insurance Services Office]], the pricing model translates [[Definition:Risk | risk]] into a dollar figure that must be both competitive and financially sustainable.

🔧 Traditional approaches rely heavily on historical [[Definition:Loss ratio | loss experience]] and [[Definition:Experience rating | experience rating]], segmenting risks by characteristics like geography, industry, size, and [[Definition:Claims history | claims history]]. More advanced models incorporate [[Definition:Predictive analytics | predictive analytics]] and [[Definition:Machine learning (ML) | machine learning]], drawing on granular data — credit scores, [[Definition:Telematics | telematics]] feeds, satellite imagery — to refine risk segmentation far beyond what manual classification permits. [[Definition:Generalized linear model (GLM) | Generalized linear models]] remain the industry workhorse, but carriers increasingly experiment with ensemble methods and neural networks, particularly in [[Definition:Personal lines | personal lines]] where data volumes are large enough to support complex algorithms.

💡 A well-calibrated pricing model is the engine of [[Definition:Underwriting profit | underwriting profitability]]. Underprice a segment, and the [[Definition:Combined ratio | combined ratio]] deteriorates as losses outpace premium; overprice it, and business migrates to competitors with sharper analytics. [[Definition:Regulatory compliance | Regulators]] add another dimension, requiring that rates be adequate, not excessive, and not unfairly discriminatory — constraints that demand transparency in how variables are selected and weighted. As [[Definition:Insurtech | insurtech]] companies push the boundaries of data-driven pricing, the tension between granularity and fairness continues to shape one of the most consequential disciplines in the [[Definition:Insurance value chain | insurance value chain]].

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Actuarial science]]
* [[Definition:Predictive analytics]]
* [[Definition:Experience rating]]
* [[Definition:Rate filing]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Loss ratio]]
{{Div col end}}

Definition:Pricing model - Revision history

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