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Telematics Data

Driving behavior data from in-vehicle devices or apps (speed, braking, mileage) used to price auto insurance based on actual usage and risk.

technicalPublished 2026/06/07Last verified 2026/06/07

FAQs

Are telematics scoring algorithms subject to rate filing requirements?
Yes, in most states. If telematics scores influence rates or tier placement, the scoring algorithm — or at least the variables and their rating impact — must be filed and approved in prior-approval states. Many states also require disclosure to policyholders of how telematics data affects their rate.
How do we handle the privacy concerns of collecting continuous driving data?
Required disclosures, opt-in consent, data retention limits, and clearly stated policies on what data is shared with third parties are the baseline requirements. Several states have enacted specific telematics data privacy rules, and the NAIC has issued guidance on data privacy in insurance. Consult legal counsel on state-specific requirements before program launch.
What happens to a telematics-rated policy when a policyholder stops sharing data?
Most carriers apply a default tier or a penalty rate when a policyholder disables data collection. The policy must disclose this treatment at enrollment. Some programs allow a grace period before the default rate applies. The filed rating plan must define the treatment of non-participating policies.

Related Terms

  • IoT Risk Data

    Sensor data from smart home, commercial property, or industrial devices used to monitor risk and enable loss prevention or dynamic pricing.

  • Feature Engineering

    Selecting, transforming, and constructing input variables from raw data to improve predictive accuracy of machine learning models in insurance.

  • Real-Time Scoring

    Running a predictive model instantly at a transaction point (quote, bind, FNOL), returning a risk score or decision within milliseconds.

  • Gradient Boosting Insurance

    An ensemble machine learning technique building sequential decision trees widely used in insurance pricing, fraud detection, and churn prediction.

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Telematics data refers to the continuous stream of vehicle operation and driving behavior information collected through onboard diagnostic (OBD-II) devices, factory-embedded telematics systems, or smartphone-based apps, and used by auto insurers to assess individual driving risk, price policies based on actual usage and behavior, and improve the accuracy of claims assessment.

How it works / Why it matters

Traditional personal auto rating relies on proxy variables — driver age, vehicle type, credit score, prior claims — that correlate with risk at a population level but may poorly reflect an individual's actual driving habits. A 25-year-old rated as high-risk based on demographics may be a cautious, low-mileage driver. Telematics addresses this by capturing direct behavioral evidence.

Typical telematics signals collected include:

  • Mileage: Annual and per-trip miles driven, the single strongest predictor of exposure frequency.
  • Hard braking and acceleration events: Frequency and severity of sudden braking and rapid acceleration, associated with following distance and aggression.
  • Speed and speeding: Distribution of speeds and frequency of exceeding posted limits.
  • Time of day and day of week: Night driving and rush-hour driving carry elevated accident rates.
  • Phone distraction: Smartphone-based programs can detect handheld phone use while driving.
  • Location and road type: Highway vs. urban driving, exposure in high-risk geographic corridors.

Raw telematics signals require substantial feature-engineering before model consumption. Trip-level events must be aggregated into behavioral metrics over meaningful time windows (30, 90, 180 days). Scores must be normalized for differences in collection methodology between OBD devices and smartphone apps. Missing data periods must be handled carefully to avoid penalizing drivers for app inactivity.

Telematics pricing models use gradient-boosting-insurance or GLM architectures trained on linked telematics and claims datasets to estimate the relationship between behavioral signals and accident frequency and severity.

In practice

Usage-based insurance (UBI) programs range from pay-as-you-drive (PAYD), which prices primarily on mileage, to pay-how-you-drive (PHYD), which incorporates the full behavioral profile. Renewal repricing based on telematics scores has become a significant competitive differentiator in personal auto.

Verisk provides telematics data exchange and scoring services. Carriers using the insurance-data-lake architecture can store raw trip-level data for retrospective model development and claims reconstruction.

Related concepts

See iot-risk-data for the analogous application of connected-device data in commercial and property lines, and real-time-scoring for how telematics-based scores are computed and surfaced at policy transactions.