How AI accelerates insurance claims processing

The immediate aftermath of a vehicular crash is difficult for all parties involved. The drivers have to process the trauma, address injuries with medical attention, exchange information, and figure out how to get their damaged cars repaired or replaced. Insurance companies have to conduct damage assessments and figure out the most efficient and least expensive route to get the claims resolved. Claim adjusters, body shop workers, and various other parties all have a say in the process, which can often take weeks to wrap up.

In addition to the time and trauma involved, crashes generate a lot of data, whether they’re pictures of damaged parts or associated documentation from police reports. In addition, the frequency of crashes — 2019 saw nearly 6.8 million vehicle crashes in the United States alone — means a large volume of data to be processed constantly. Auto insurance claims result not just from crashes, but also from other kinds of damage, such as floods and trees falling on bumpers.

AI ramps up

These collective factors make for a particularly compelling argument for implementation of artificial intelligence in claims processing, says John Goodson, chief technology officer at CCC Intelligent Solutions, a technology solutions provider for the automotive and insurance industries. (CCC is itself not an insurance company.)

The use of AI in insurance claims processing has been steadily accelerating. CCC reported a 50% year-over-year increase in the application of advanced AI for claims processing in 2021. The company reports that more than 9 million unique claims have routed through its deep learning AI solution – a number that grew more than 80% in 2021.

When a crash claim comes through, the insurance company has to dispatch claim adjusters who attend to a laundry list of questions: is the car completely damaged or can it be fixed? How much will it cost? What’s the best way to fix the car? Where should replacement parts be sourced? Will the parties need a rental?. The same questions need to be asked every time, which makes them particularly suited to a deep learning model: understand the damage and solutions from previous crashes and apply that learned knowledge to future ones.[…]

AI ramps up

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