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A Predictive Analytics Arsenal

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 claimswhere the vast majority of a carriers premium dollars are spent.

By Patrick Sugent | Updated on January 02, 2012
A typical auto claim process starts with a phone call from a policyholder who has just been in a car accident. The carrier representative collects details about the accident, and the claims processing system passes information to a claims adjuster’s queue. The claims adjuster then starts the investigation and may order incremental data that he or she thinks is most appropriate for each case. However, this process is labor-intensive. It may take 45 days or more to close the claim.

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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