prove the automated identification of
fraudulent or genuine claims.
2 Multiple lines of business.
A number of insurers have demonstrated that building networks of customers and activities across multiple lines of
business provides even further customer
insight and intelligence. A customer with
a repeated incidence of suspect home
claims is more likely to make a suspect
vehicle claim or travel insurance claim.
At a larger scale a number of fraudulent
vehicle insurance claims have identified
large fraudulent commercial insurance
3 Capture intelligence and learn.
All investigations provide invaluable
intelligence, it is important to capture ev-
ery outcome or interaction with your cus-
tomer, whether a claim is denied, marked
for additional attention or cleared. This
insight is added to the networks and al-
lows the system to learn and adapt scor-
ing to improve accuracy. A networked
database also allows third-party fraud
flags to add far more value as they can be
used to enhance risk scores even if they
are not directly matched with the claim
4 Use automated scoring at every step.
Cost can be reduced if you can confidently identify at the earliest possible
opportunity which claims need further
investigation and which should be paid.
The system should re-score the claim
every time any additional information
is obtained throughout the claims handling process. This ensures that time is
not wasted in unnecessary investigation
and customers are not kept waiting for
5 Eliminate organized crime.
Insurers who implement networked
analytics solutions find that organized
crime represents a disproportionally
large volume of claims fraud. It becomes
very apparent that what initially appear
to be opportunistic frauds are frequently
connected and part of organized crime.
A networked analytics approach is the
ideal way to identify and deal with the
conscious, pre-meditated activities asso-
ciated with organized fraud.
6 Leverage free text documents.
Many insurers fail to make use of free
text associated with claims, policies,
customer interactions or contracts.
This text can be mined and used in
multiple ways for example, words and
phrases can be used in risk scoring to
better predict fraud and entities can be
extracted from the text automatically to
enhance networked data. Investigators
can search huge volumes of text documents to find evidence, historic behavior or commonalities in seconds. Text
analysis, categorization and heat mapping by claim types and geography can
help to identify fraud or risk hot spots.
The ability to process and integrate text
is especially important in areas such as
commercial, medical and liability related fraud.