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💹 '''Dynamic pricing''' is a [[Definition:Rating | rating]] approach in which [[Definition:Insurance carrier | insurance carriers]] continuously adjust [[Definition:Premium | premiums]] in response to real-time or near-real-time data signals rather than relying solely on fixed rate tables filed at the start of a policy period. In the insurance industry, the concept goes beyond the simple supply-and-demand mechanics familiar in airline or hotel pricing; it incorporates live [[Definition:Risk assessment | risk-assessment]] variables — such as driving behavior captured by [[Definition:Telematics | telematics]], IoT sensor readings from commercial properties, or shifting [[Definition:Catastrophe model | catastrophe-model]] outputs — to produce prices that more accurately reflect the current state of the insured risk.

⚙️ Implementation typically involves a [[Definition:Predictive model | predictive modeling]] layer that ingests streaming data, re-scores the risk, and feeds updated pricing into the [[Definition:Policy administration system | policy administration system]] or [[Definition:Quoting | quoting engine]]. In [[Definition:Usage-based insurance (UBI) | usage-based auto insurance]], for example, a driver's premium may decrease month over month as telematics data confirms safe behavior, or increase if patterns suggest higher exposure. [[Definition:Insurtech | Insurtech]] platforms have been at the forefront of operationalizing dynamic pricing because their cloud-native architectures can process high-volume data feeds and recalculate rates without the batch-processing constraints of [[Definition:Legacy system | legacy systems]]. Parametric products — where [[Definition:Payout | payouts]] trigger automatically from measurable indices — also rely on dynamic pricing logic tied to real-time data such as weather station readings or seismic monitors.

🎯 The appeal of dynamic pricing is clear: it aligns [[Definition:Premium | premium]] more tightly with actual risk, which can reduce [[Definition:Adverse selection | adverse selection]], reward lower-risk behavior, and improve [[Definition:Loss ratio (L/R) | loss ratios]]. Yet the approach raises important [[Definition:Regulatory compliance | regulatory]] questions. Most U.S. states require insurers to file rates and obtain approval before use, and [[Definition:Insurance regulator | regulators]] are wary of pricing algorithms that could produce [[Definition:Unfair discrimination | unfairly discriminatory]] outcomes or lack [[Definition:Algorithmic transparency | transparency]]. Carriers pursuing dynamic pricing strategies must therefore balance actuarial innovation with robust [[Definition:Model governance | model governance]], clear documentation of rating factors, and ongoing compliance monitoring to satisfy both regulators and consumer-protection standards.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Usage-based insurance (UBI)]]
* [[Definition:Telematics]]
* [[Definition:Predictive analytics]]
* [[Definition:Rate filing]]
* [[Definition:Adverse selection]]
* [[Definition:Algorithmic underwriting]]
{{Div col end}}

Definition:Dynamic pricing - Revision history

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