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💲 '''Algorithmic pricing''' is the use of automated, data-driven models — often powered by [[Definition:Machine learning | machine learning]], [[Definition:Generalized linear model (GLM) | generalized linear models]], or [[Definition:Deep learning | deep-learning]] architectures — to determine [[Definition:Premium | premium]] rates for [[Definition:Insurance policy | insurance policies]] in real time or near-real time. Rather than relying solely on traditional [[Definition:Rating manual | rating manuals]] and broad classification factors, algorithmic pricing ingests hundreds or even thousands of variables — from telematics driving behavior and property-sensor data to credit attributes and third-party enrichment feeds — to produce individualized rates that more precisely reflect each applicant's expected [[Definition:Loss cost | loss cost]].

⚙️ The workflow typically begins with [[Definition:Actuarial analysis | actuarial]] and data-science teams training models on historical [[Definition:Loss experience | loss-experience]] data, then validating them against hold-out samples and regulatory fairness criteria. Once approved, the model is embedded in the [[Definition:Rating engine | rating engine]] of a [[Definition:Policy administration system | policy-administration system]] or [[Definition:Application programming interface (API) | API]] layer, allowing it to score quotes in milliseconds during the [[Definition:Quote-to-bind | quote-to-bind]] process. Continuous feedback loops feed new claims and exposure data back into the model, enabling periodic recalibration. Carriers operating in [[Definition:Personal lines | personal lines]] — particularly [[Definition:Auto insurance | auto]] and [[Definition:Homeowners insurance | homeowners]] — have been early adopters, but commercial-lines [[Definition:Underwriting | underwriters]] are increasingly leveraging algorithmic pricing for [[Definition:Small commercial insurance | small-commercial]] and [[Definition:Cyber insurance | cyber]] portfolios where speed and granularity create competitive advantages.

📋 The appeal of algorithmic pricing is clear — better risk segmentation leads to more competitive rates for good risks and more adequate rates for adverse ones, improving the overall [[Definition:Loss ratio (L/R) | loss ratio]]. Yet the approach raises important questions around [[Definition:Algorithmic transparency | transparency]], [[Definition:Unfair discrimination | unfair discrimination]], and [[Definition:Regulatory compliance | regulatory approval]]. State [[Definition:Insurance regulator | regulators]] may require that carriers file the logic behind their models and demonstrate that no protected-class proxy variables drive rate differences. The tension between proprietary model sophistication and regulatory demands for explainability is one of the defining challenges of modern [[Definition:Insurtech | insurtech]], prompting carriers to invest heavily in [[Definition:Explainable AI (XAI) | explainable-AI]] tooling and [[Definition:Algorithmic audit | algorithmic auditing]] programs.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Rating engine]]
* [[Definition:Predictive model]]
* [[Definition:Algorithmic transparency]]
* [[Definition:Telematics]]
* [[Definition:Rate filing]]
{{Div col end}}

Definition:Algorithmic pricing - Revision history

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