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🧮 '''Rating model''' is the quantitative framework an [[Definition:Insurance carrier | insurer]] uses to convert risk characteristics into a specific [[Definition:Insurance premium | premium]] for a given policy. It translates [[Definition:Rating variable | rating variables]]—such as a driver's age, a building's construction type, or a business's revenue—into numerical factors that, when applied to a [[Definition:Base rate | base rate]], produce the price a [[Definition:Policyholder | policyholder]] pays. Rating models sit at the heart of the [[Definition:Underwriting | underwriting]] process and represent one of the most competitively sensitive assets a carrier owns.

⚙️ Traditionally, rating models relied on [[Definition:Generalized linear model (GLM) | generalized linear models (GLMs)]] fitted to years of [[Definition:Loss experience | loss data]], producing multiplicative or additive factor tables that underwriters could apply in a structured, auditable way. The rise of [[Definition:Insurtech | insurtech]] has expanded the toolkit to include [[Definition:Machine learning (ML) | machine learning]] algorithms—gradient-boosted trees, neural networks, and ensemble methods—that can capture nonlinear interactions among dozens or even hundreds of variables. However, deploying these models in production requires navigating [[Definition:Rating law | rating laws]] that demand rates be adequate, not excessive, and not unfairly discriminatory, which means carriers must often pair complex models with interpretability layers that satisfy regulatory [[Definition:Rate filing | filing]] requirements.

🔍 The caliber of a carrier's rating model has outsized consequences for portfolio quality and profitability. A model that segments risk more accurately attracts better accounts at competitive prices while avoiding adverse selection on poorly priced segments—an edge that compounds over multiple [[Definition:Underwriting cycle | underwriting cycles]]. For [[Definition:Managing general agent (MGA) | MGAs]] and [[Definition:Program administrator | program administrators]] operating under [[Definition:Delegated underwriting authority (DUA) | delegated authority]], the rating model often doubles as the primary control mechanism their capacity partners rely on to ensure disciplined [[Definition:Risk selection | risk selection]]. As [[Definition:Telematics | telematics]], [[Definition:Internet of Things (IoT) | IoT]] sensor data, and alternative data sources proliferate, the competitive gap between carriers with sophisticated rating models and those relying on legacy approaches continues to widen.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Rating variable]]
* [[Definition:Rating methodology]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Predictive analytics]]
* [[Definition:Base rate]]
* [[Definition:Rate filing]]
{{Div col end}}

Definition:Rating model - Revision history

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