This is about using data from cars (how, when, and where people drive) so insurers can price policies more fairly and design new products, instead of just relying on traditional factors like age, postcode, or past claims.
Traditional auto insurance pricing and risk assessment are blunt instruments that miss real driving behavior, leading to mispriced risk, limited product innovation, and weaker customer engagement. Telematics data promises more accurate risk scoring and new usage‑based insurance models.
Access to large‑scale telematics data, historical loss data, and insurer integrations can create proprietary risk scores, actuarial models, and long‑term carrier relationships that are hard for new entrants to replicate.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Ingesting, cleaning, and featurizing large‑volume telematics time‑series data from many vehicles and devices, and linking it reliably to policy and claims systems while staying within data privacy and regulatory constraints.
Early Majority
Focus on combining telematics data with existing insurance risk and claims data to create deployable scores and tools for insurers, rather than just raw device or app data collection.