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📊 '''Personalized pricing''' is the practice within [[Definition:Insurance | insurance]] of tailoring [[Definition:Premium | premiums]] to an individual policyholder's unique risk profile using granular data, advanced [[Definition:Predictive analytics | predictive analytics]], and [[Definition:Machine learning | machine learning]] models rather than relying solely on broad rating classes. While insurers have always segmented risk — grouping drivers by age and geography, for example — personalized pricing pushes segmentation to a far more granular level, incorporating real-time behavioral data from [[Definition:Telematics | telematics]] devices, [[Definition:Wearable technology | wearables]], smart-home sensors, and digital footprints. The approach represents a fundamental shift in how [[Definition:Underwriting | underwriting]] and [[Definition:Rating | rating]] operate, enabled by the data infrastructure and algorithmic capabilities that [[Definition:Insurtech | insurtech]] firms have pioneered.

⚙️ The mechanics hinge on feeding richer and more diverse data sets into [[Definition:Rating algorithm | rating algorithms]]. In [[Definition:Auto insurance | auto insurance]], a [[Definition:Usage-based insurance (UBI) | usage-based program]] might track braking patterns, cornering speed, time of driving, and miles traveled to generate a risk score unique to each driver. In [[Definition:Health insurance | health]] and [[Definition:Life insurance | life insurance]], data from wearable fitness trackers or electronic health records can inform pricing. Actuaries build [[Definition:Generalized linear model (GLM) | generalized linear models]] or deploy [[Definition:Artificial intelligence (AI) | AI]]-driven approaches that identify risk factors invisible in traditional rating plans. The resulting [[Definition:Premium | premium]] more closely reflects each individual's expected [[Definition:Loss cost | loss cost]], rewarding lower-risk policyholders with lower prices and charging higher-risk individuals more — a process sometimes called risk-adequate pricing at the individual level.

⚖️ The promise of personalized pricing — greater accuracy, fairer premiums, and reduced [[Definition:Adverse selection | adverse selection]] — comes with significant regulatory and ethical scrutiny. [[Definition:Insurance regulator | Regulators]] in many jurisdictions are examining whether hyper-granular pricing could result in unfair discrimination, particularly if [[Definition:Proxy discrimination | proxy variables]] correlate with protected characteristics like race, income, or disability status. The European Union's evolving stance on [[Definition:Algorithmic fairness | algorithmic transparency]] and various U.S. state-level initiatives on [[Definition:Insurance regulation | rate regulation]] reflect growing concern about where the line falls between actuarially justified differentiation and socially unacceptable discrimination. Insurers pursuing personalized pricing must invest not only in data science talent and technology but also in robust [[Definition:Model governance | model governance]] frameworks that ensure their pricing models are explainable, auditable, and compliant with applicable laws.

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

Definition:Personalized pricing - Revision history

PlumBot: Bot: Creating new article from JSON