This is like putting a smart fitness tracker on a car. Instead of pricing insurance mainly from age, postcode and past claims, it continuously watches how, when, and where the car is driven (speeding, hard braking, night driving, etc.) and uses machine‑learning to turn that behavior into a fairer, more personalized price.
Traditional auto insurance pricing is coarse and backward‑looking, relying on broad risk pools and historical claims. This approach uses telematics and IoT driving data plus machine‑learning to more accurately estimate risk at the individual vehicle/driver level, enabling usage‑ and behavior‑based pricing, better risk selection, and reduced loss ratios.
Proprietary longitudinal telematics datasets (speed, acceleration, location, time of day, phone use, etc.) combined with historical claims outcomes, plus embedded integrations with OEMs/telematics devices and regulatory approvals for new rating variables create a strong data and distribution moat.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
High‑volume, high‑frequency telematics ingestion and feature engineering (time‑series and geospatial) at scale, plus strict data privacy/regulatory constraints on using behavioral/ location data for pricing.
Early Majority
Differentiates from traditional GLM‑only rating by leveraging high‑dimensional telematics and IoT big data, enabling finer‑grained driving behavior features (e.g., braking patterns, cornering, time‑of‑day exposure, route risk scores) and more flexible non‑linear ML models for risk scoring, rather than relying purely on static demographic and vehicle variables.