Corgi and Trucker Path Insurance recently launched a trucking insurance program built around highly granular route-planning data as a key underwriting input.
It is an interesting approach because routing gives a detailed view of one important part of fleet exposure. But commercial auto risk is shaped by more than where a fleet operates.
So how far can one highly granular input take you toward a complete view of fleet risk?
Granularity gives you depth
Route data can give insurers a much more precise view of geographic exposure: where vehicles actually operate, how far they travel, and how their activity is distributed.
That depth matters.
The same principle applies elsewhere. Telematics can reveal fleet mix, operating hours, speed profiles, mileage, and safety events. Mapping data can give you context – traffic, weather, road conditions, and much more.
Each source can describe its part of the risk with increasing precision. The challenge is seeing those parts together.
Fleet risk is complex and constantly changing
A fleet’s risk profile reflects a combination of operational characteristics, risk signals, regulatory data and other aspects of exposure. But no single data set captures the whole picture.
So – coming back to Corgi’s case – two fleets can operate in similar geographies and still present very different risks.
The same fleet can also change substantially during the policy term.
It may expand, change where and how it operates, or develop other patterns that alter its exposure. Even a highly detailed view at bind is still only a snapshot. Its value increases when that picture stays current as the fleet changes.
Draivn: bringing the signals together
Commercial auto already has access to more data than ever.
But to make the data useful, inputs from different sources need to be brought together, validated, translated into insurance-ready outputs, and kept current throughout the policy term.
Draivn combines fleet data from diverse sources to give insurers and MGAs this continuously validated view of fleet risk.
Today, that includes:
Base exposure, including fleet composition, technology adoption (GPS, cameras, ADAS, etc.), and mileage;
Geo exposure, with homebase, radius, rate territories, urban and rural exposure, and road types;
Driving and behavior data, including operating hours and night driving; speed profile and speedings, speed vs. traffic, harsh and safety events;
Frequency and severity signals;
Claims intelligence including accident detection, reconstruction, and triage.
A highly granular dataset can make one part of the picture much clearer. Bringing those parts together gives insurers a more complete view of the risk they are actually carrying.
Want to see the full risk picture of your fleet customers? Talk to our team via draivn.com.
