Telematics

A behavior-based risk scoring framework that converts high-frequency telematics and IoT data into pricing-ready signals for usage-based auto insurance.

The Business Problem

Traditional auto pricing models rely primarily on static variables such as demographics, vehicle characteristics, and prior claims history. These factors do not fully reflect real-world driving behavior or dynamic risk exposure.

With the growth of telematics and connected vehicles, insurers needed a way to incorporate high-frequency IoT driving data into pricing models in a controlled, scalable, and regulator-aligned manner.

The goal was to translate raw trip-level data into stable, predictive signals that could meaningfully improve pricing accuracy.

Our Approach

We designed a machine learning–based driving behavior scoring framework that integrated telematics IoT data with policy and claims information.

Our work included:

Processing high-frequency GPS and telematics signals such as speed, harsh braking, rapid acceleration, cornering intensity, mileage, and time-of-day driving
Aggregating trip-level time-series data into structured behavioral risk features
Developing a composite driving behavior score using supervised learning methods
Integrating the telematics-derived score into existing auto pricing models
Applying validation, stability testing, and interpretability techniques to ensure regulatory alignment

The resulting framework provided a dynamic view of driver risk that complemented traditional rating variables.

Scale and Complexity

Millions of trip-level telematics records processed and aggregated
High-volume time-series IoT data requiring signal cleaning, normalization, and feature compression
Real-time or near-real-time scoring integration into pricing workflows
Cross-functional coordination across actuarial, product, data engineering, and compliance teams
Managing signal noise, behavioral variability, and state-level regulatory considerations significantly increased technical and operational complexity.

Impact Delivered

5 %

improvement in
auto pricing performance

8–12%

improvement in risk segmentation accuracy for high-risk driver cohorts

2–4 point

improvement in loss ratio within telematics enabled portfolios over 18 months

Why This
Was Hard

Telematics introduces high-velocity, high-variance data into traditionally stable pricing environments.

The challenge was to convert noisy behavioral signals into consistent, regulator-ready predictors while maintaining model stability across diverse geographies and driver populations.