Data science is bringing innovation to underwriting trucking insurance, says Ian White, chief executive officer of Koffie Labs.
The underwriting process in trucking insurance is relatively antiquated, not having changed for the past 50 years, White says.
Understanding drivers is one of the major considerations that incumbents, or underwriters, have. “You're looking at their motor vehicle record for any kind of disqualifying events,” White says. “It could be a crash or violations. And they consider the operations of the motor carrier itself, which would include such things as financial stability, the type of goods they're hauling, where they’re based, and whether they’re long haul. That's what incumbents have done for about 50 years or more.”
Truck insurance is hardly a small business. White estimates underwriting “in a moderate way” comes in around $100 billion in premiums. “We could define it more broadly and could get closer to maybe $150 billion, maybe $200 billion. For reference, personal automotive insurance is $250 billion in premiums per year.”
Continuing with his critique that the industry is a laggard in innovation, White says incumbents don’t actually consider the equipment when they underwrite a fleet. A fleet of Western Star trucks from 1999 will get the same quote as one with top-of-the-line 2022 Volvos.
Data should be parsed to truly understand how these fleets perform differently. That means not just looking at crashes and violations, but also at the equipment on the vehicle, the manufacturer of the engine and whether operators use technology like telematics. “This gives us a much more granular and dynamic view into the risk,” says White, “not just today but going forward, when technology will play an increasingly large role in operating a vehicle as the role of the driver begins to decrease.”
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