Telematics and Usage-Based Insurance Modeling

Telematics Data in Insurance

Telematics devices and smartphone apps collect driving behavior data including speed, braking patterns, cornering, time of day, and miles driven. This granular data enables usage-based insurance (UBI) where premiums reflect actual driving habits rather than broad demographic categories. MAS-II expects you to understand how this data feeds into predictive models.

Modeling Techniques

Generalized linear models (GLMs) and gradient boosting machines (GBMs) are commonly applied to telematics features. Feature engineering is critical: raw accelerometer readings must be transformed into meaningful risk indicators like hard braking events per mile or percentage of nighttime driving. Mixed models handle the panel data structure where each driver has multiple observation periods. On the exam, be prepared to discuss variable selection with high-dimensional telematics data, model interpretation challenges, and how to integrate telematics scores with traditional rating variables.