are becoming more robust. Carriers also
understand that aggregating the right insights and information ultimately makes
them more efficient.
Detection models to ferret out questionable claims and networks for expert review
are available through multiple techniques,
including industry-accepted indicators,
advanced analytics, complex algorithms
and mathematically driven anomalies.
One of the biggest efficiency gains is
the ability to identify matters in real time
instead of catching fraud on the back
end. Additionally, having instantaneous
insight into how people, providers and
other entities are connected throughout claims data is powerful. This is a key
ROI from technology investments. Tangible impact opportunities are increased
through early detection versus being contingent on the future.
A secondary benefit to this move is that
it helps insurers become more familiar
with their own data by recognizing trends
and gaining greater insight into the actual
book of business they write. This type of
information will create a new landscape
of red flags that is more meaningful to the
company. How the output is used is the
responsibility of the carrier, but having a
consistent approach to assessing referrals
identified through analytics helps safeguard the discovery process.
Within this realm, plaintiff attorneys
will look to exploit this process if an opportunity should arise, and they do not
need a solid technology background for
simple inquiries that can develop into
more complex questions. An industry
professional may need to articulate how
a specific claim landed on his or her desk.
That singular question could lead to discovery of other internal individuals who
would never have been on a deposition
list prior to data questioning.
With this question, everyone who
touches the process may be subject to de-
position, and carriers should be prepared
to answer questions such as:
• Who is in charge of the
carrier’s data analytic unit?
• Do you use an outside vendor?
• How does the process work?
• Where is the data stored?
• Who has access to the data
and the findings?
• How were the models created?
From a litigation perspective, this type
of discovery is not new, but these types
of questions may grant wider access into
the internal SIU process. They could open
doors that permit discovery of personnel
who would otherwise be irrelevant. These
discovery requests will likely be decided
via motion, and the outcome may hinge on
relevancy, purpose, scope and the prejudicial effect of not producing the discovery.
The true purpose of the plaintiff’s mo-
tion may be to simply shake the tree in
an effort to obtain a quicker settlement or
more favorable resolution. In the worst
case scenario, the plaintiff’s end game
may be to scrutinize the investigation
process and probe the investigation for
the sake of creating a bad faith scenario
to increase damages.
The bottom line for insurance carriers
and claims professionals is to be prepared
for this scenario in the claims and litiga-
tion space. Utilizing technology-driven
fraud detection provides consistency
in the detection approach for the initial
identification of all claims. Ultimately, our
professionals are still responsible for tri-
aging and determining a future course of
action. As such, we cannot underestimate
the value of training and preparation.
Discovery is a fact of litigation. Technology and analytics level the playing
field for the carrier, optimizing opportunities for more effective and efficient
fraud detection so that the investigator’s
skills are fully leveraged. The key is to be
aware of the technological evolution and
Jeffrey G. Rapattoni, Esq., co-chairs the
fraud/special investigation practice group
at civil defense litigation law firm, Marshall
Dennehey Warner Coleman & Goggin.
James Hulett is a security and intelligence
business consultant at SAS, and Kim Kuster
is a manager, intelligence unit, special
investigation unit, at Grange Insurance.
One of the biggest
efficiency gains is the ability
to identify matters in real
time instead of catching
fraud on the back end.