investigation. Paying legitimate claims
quickly increases adjuster performance
and customer satisfaction, while simultaneously decreasing costly churn and driving down fraud risk.
Strong analytic processes offer significant
benefits to insurers beyond simply identifying connected individuals and preventing the payment of fraudulent claims.
In most states, insurance carriers are
required to complete annual documents
which detail their anti-fraud efforts. Aside
from having a more proactive, holistic
cost saving claims operation, through the
use of different tools, insurance carriers
have a more robust story to tell regulators
about their anti-fraud programs.
If senior insurance executives were asked
how much organized fraud is committed
against their organization and what the
corresponding losses associated with that
fraud type are, they likely wouldn’t know.
Effective fraud prevention, however,
dictates that management does know
what’s going on inside the network and
who is committing fraud against their
organizations. The brilliant Chinese military strategist Sun Tzu said it best with
his “know your enemies” philosophy, and
that definitely holds true when it comes
to fighting organized ring fraud.
The more you know about groups,
their methodologies and attack strategies, the better positioned your company defenses will be. SNA provides the
investigator with a “top down” visualization which establishes the commonality between shared data points. These
connected data points often signify organized criminal activity, which if undetected, generates large scale losses;
diminishing return on investment (ROI)
and bottom line profitability for insurance carriers over time.
The technology allows investigative
teams to expose sophisticated bad actors
and groups that were previously undetected, and identify high probability information which is relevant to the claim.
Better data yields better results
An important consideration in identifying patterns of nefarious activity and
organized crime rings operating in a network is the volume and quality of data
gathered by anti-fraud detection tools.
While SNA connections are created
from existing network data and real time
or batch data fed into the system, the
amount and quality of the data insurers
have access to has a definite impact on the
ability to successfully identify anomalies
and connections within the claims data.
While adding third-party data sources
doesn’t solve everything, data enhances
the identification of anomalies and generating SNA connectivity.
In 2015, an Association of Certified
Fraud Examiners (ACFE) article by Clop-
ton, Morrow and Heitger stated: “The
proliferation of e-mail, messaging, elec-
tronically stored documents and social
media not only provides a rich repository
of data for social scientists, but also pro-
vides a platform for analysis of relation-
ships in fraud examinations.”
Frequently, carriers operate multiple legacy claims systems, which compound the
challenges of investigation and impede
SNA automation. Multiple systems decrease adjuster efficiency since they add
additional steps to effectively adjudicate
and pay claims.
Streamlining the investigative process
and reducing operational ineffectiveness
are important. Automation is critical to
achieving SNA and effective fraud detection. Not only does it present a clearer
picture, but it reduces the time spent on
identifying social networks, increases
ROI and frees up staff to perform other
claims related and investigative functions.
Despite the move to more technology-based anti-fraud programs, the Coalition
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Strong analytic processes offer
significant benefits to insurers
beyond simply identifying
connected individuals and
preventing the payment
of fraudulent claims.