structured flow, typos, and abbreviations
in adjuster notes, the eventual business
results are well worth the effort. Furthermore, once a core text mining engine is
established, it can be quickly and easily
extended to produce insights across a
variety of claims concepts, from identifying the at-fault party to fraud red flags,
dissatisfied claimants and insureds, and
details about the location and the cause
of a given loss.
Mining for fraud Mitigation
Fraud is a challenge that pervades all
industries, especially P&C insurance. Estimates from the Insurance Information Institute (I.I.I.) indicate that fraud accounts
for approximately 10 percent of the P&C
insurance industry’s incurred losses and
loss adjustment expenses (LAE)—or about
$30 billion each year. Recent indications
also show that fraud is a growing menace
in personal injury protection (PIP) states,
with direct impact on company results and
insured premiums. Recognizing, investigating, and denying suspicious claims are
thus key priorities for carriers.
A common example of automobile insurance fraud is the “swoop and squat.”
This refers to a scheme in which several
potential fraudsters use an older car to
cause an accident with a newer luxury
vehicle, based on the assumption that the
owner has high insurance coverage limits. The scammers pull in front of their
intended target vehicle—which is usually
near a highway exit—and stop suddenly,
thereby causing a collision. They then report damage to the car and fake numerous soft-tissue injuries, leading to a big
payday for them and a big loss to the victim’s insurance carrier.
While experienced, alert adjusters can
recognize such schemes and get the Special
Investigative Unit Services (SIU) involved
in a timely manner, workflow pressures,
lack of experience, and inadequate training can often lead to missed opportunities.
Furthermore, fraud schemes are constantly
evolving, and patterns may not be as easy
to detect as in the above example.
Text mining can be leveraged to distill
insights from adjuster notes to systematically create a multitude of fraud concepts,
such as questionable injury, excessive
treatment, low-speed accident, sudden
stop, and an accident near a highway exit.
Predictive models can then be established
using these concepts—along with other
structured data from the claims system—
to generate highly accurate and actionable
referrals for IME or SIU intervention.
improving Workflow Routing
In addition to alleviating flaws in the
claims process, text mining can help reduce blemishes in workflow. Many insurers continue to use legacy claims-handling
systems. An adjuster may identify subrogation opportunity on a claim, but to route it
to the recovery unit, the adjuster may have
to navigate through a number of screens
or pages to the recovery referral screen.
Worse yet, at some carriers, the adjuster
may have to open a separate database and
cut-and-paste information into it to route
the claim to the recovery unit. Owing to
this obviously burdensome process, many
valid referrals are often missed.
Text mining, coupled with strategic