Atypical 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
realize the value of
external data to the
claims process, but
they typically use it in
a reactive manner.
the carrier’s subrogation unit for further
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.
Data and analytics can help carriers
create a more efficient claims process:
one that is both cost- and time-efficient,
and that minimizes losses related to fraud
while enhancing customer service. To
reap the full benefits, insurance carriers
must proactively supplement their internal policy-level data with external data.
Intuitively, carriers know the value of