Nearmap’s AI-powered models approved in 27 markets
Insurers can leverage the predictive scores to help price risk.
Now consider this scenario: an agent receives a phone call from a person who has just been in a car accident. The representative immediately sees all of the relevant data about all involved parties as it fills the screen through a data prefill product for quick validation and customer confirmation. The agent instantly confirms the person’s name, address, and vehicle identification number (VIN), as he or she collects details about the accident. Once the accident details are captured, the data is evaluated against an external database that indicates the claimant actually has coverage with multiple carriers. The claim is automatically directed to the carrier’s subrogation unit for further investigation.
Scenarios like this one are surprisingly uncommon. In areas of the business like personal lines, quoting, and underwriting, carriers have embraced data and analytics to improve profitability and reduce costs. Yet, they have not applied the same approach to claims—where the vast majority of a carrier’s premium dollars are spent.
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Insurers can leverage the predictive scores to help price risk.
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