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Using
BigDatatoC b t
To combat fraud,
more organizations
are thinking big —
employing new
approaches aroundBig Data to convert
the volumes of
information available
into useful insight
and real action.
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Fraud
Organizations around the world are look-
ing at new ways to combat fraud, as
they recognize that fraud is no longer a cost of
doing business but rather fraud is costing busi-nesses to do business. An estimated $3.5 trillion
in revenue annually is lost due to fraud, estimates the
Association of Certified Fraud Examiners (ACFE).
To combat the fraudsters, more organizations are thinking
big — employing new approaches around Big Data to convert
the volumes of information now available to them into useful
insight and that insight into real action.
With the explosion of social networks over the past few
years, Big Data has become a hot topic for business. But it’s
important to note that Big Data is much more than social
media. It is structured and unstructured data residing in data-
bases in multiple geographies. It’s text on Web-based formsand PDFs, and it is email and all forms of other documents.
Big Data has opened the door to a world of new capabilities
that, when deployed appropriately, can help organizations
tackle key business challenges, including fraud.
But how and where does an organization begin? Based on
the experience of hundreds of businesses — from banks to insur-
ance companies to government tax agencies — five best practices
for using Big Data to fight fraud have been identified: Establish a
Flexible and Open Central Data Environment; Identify the Knowns
and Unknowns; A Picture is Worth 1,000 Words; Not Every Anom-
aly is Fraud; and Develop Policies and Best Practices.
Establish a Flexible and Open Central Data Environment.
Data can only be valuable if the right people have access to it
at the right time. Many financial institutions have found the
most successful way to rapidly detect and effectively prevent
fraud is by creating a central data analysis environment and
applying advanced statistical, entity resolution — and link
analysis that can spot trends, patterns and anomalies that could
be potential fraud indicators.
With evolving threats, time is of the essence — and if datamust be extracted from numerous silos across an enterprise
and manually compared by an analyst, time becomes a major
inhibitor. Also, there is the undesirable and high probability
that key connections can be missed as a result of human error
or inadequate entity resolution caused by the high volume and
disparate types and formats of data.
By centralizing all their data — which may previously have
been stored in multiple locations across several geographies —
organizations can make connections and identify threats that
they would not otherwise be able to see if the data remained in
separate repositories. Instead of using a one-size-fits-all single
Enterprise
F dBy Robert Griffin
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data warehouse —
where all data needs to
be migrated to one loca-
tion — organizations
should seek out flexible,
scalable approaches
that enable them to use
existing resources and
bring the relevant data
from their original
sources into a single
repository for analysis.
This type of approach
allows them to generate
efficiencies, uncover
fraudulent activity more
quickly and ensure le-
gitimate transactions areprocessed without delay.
A good example of
this approach is an in-
surance company using
analytics to proactively
review new policies to
determine if it repre-
sented a potential for
fraud — primarily
through links/associa-
tions with known scam-
mers or suspicious priorclaims activity by the
policyholder.
Previously, an ana-
lyst would spend approx-
imately 40 hours to
manually evaluate more
than 50,000 new policies
each month. Because
of the manual nature of
the analysis, the analyst
was not identifying all
the potentially problem-atic policies and also
had no real way to dis-
cern if there was a case
of user error.
By creating a single
repository and using
sophisticated visual
analysis capabilities, the
analyst now can identify
and understand the links
between new policy-
holders and known
scammers, and identify prior suspicious or
problematic claims filed by the new poli-
cyholder. The analyst now completes the
work in about 12 hours and finds three
times as many problematic policies.
Identify the Knowns and Unknowns.
Financial organizations regularly deal
with billions of records that pass through
their networks and security intelligence
systems. Managing these records manu-
ally is time-consuming and doesn’t allow
for the level of scrutiny and analysis that
is necessary to detect anomalies and red
flags. As a result, many companies have
turned to analytics solutions to quickly
reduce volumes of records to the highest-
threat cases.Once data is consolidated into a cen-
tralized analysis environment, the next
step is performing statistical risk analysis
and entity resolution. This process defines
who is talking to who, identifies entities,
analyzes whether their activity is unusual
and poses a potential threat, and sends
up a red flag. This step is crucial to the
figurative “needle in a haystack” that rep-
resents the major critical threats.
This concept can be characterized as
“minimizing the zone of ignorance” —leveraging the tremendous volume of all
data available to an organization (internal
and external) against the volume of data
an organization is actually able to process.
This can help organizations take advan-
tage of and understand the nuances, senti-
ment and meaning of structured and
unstructured data for more informed risk-
based decision-making.
It is important to note that the best
solutions for this operation extract sub-
ject data on the fly, bringing it into thecentralized environment at the point of
analysis, leaving the subject data intact
and uncorrupted from its original state.
A Picture is Worth 1,000 Words.
Visualization is the next-generation tech-
nology being applied to a range of com-
plex fraud challenges, like Big Data, to
rapidly collapse them into a manageable
scope, identify and prioritize threats, de-
velop critical intelligence and make quick,
informed decisions. Differentiating be-
By centralizing all their data —
which may previously have
been stored in multiple locations
across several geographies —
organizations can make
connections and identify threats
that they would not otherwise be
able to see if the data remained
in separate repositories.
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tween fraudulent and legitimate activities
is difficult when vast amounts of informa-
tion are buried within rows of numbers.
Visual representation can illustrate
the story behind the data, and in cases of
potential fraud, can demonstrate link-
ages that are not obvious between peo-
ple, places and things — individuals,
fraudulent information, financial records,
phone calls and any number of items.
For example, a global credit card
company that manages more than 10
billion card transactions daily must be
able to understand the legacy threats that
are derived from billions of terabytes of
historical data and rapidly compare
them to evolving threats. Visualization of
this activity is critical for an organizationto quickly plug vulnerabilities.
It would be impossible to react to
such scenarios if analysts had to manu-
ally delve into a statistical report that
could comprise reams of paper.
In the aftermath of any fraudulent
activity, visualization also allows organi-
zations to rapidly communicate the
course of events to law enforcement au-
thorities so they can take action.
Not Every Anomaly is Fraud .Another step organizations can take to
distinguish between fraudulent and legit-
imate activities and proactively identify
and stop fraud before it happens, is to
recognize whether any anomalies they
uncover are from user error — such as
processing a claim more than once by
mistake — or from intentional misuse.
By applying advanced analytics, or-
ganizations can quickly identify fraudu-
lent activities while continuing to
process legitimate transactions and keepbusiness moving forward. This type of
analytic modeling can not only help to
differentiate between legitimate and
fraudulent transactions, but when fraud
is identified it can help to determine
whether the fraud is the work of an indi-
vidual fraudster or the collusion of any
organized crime effort.
Develop Policies and Best Practices.
Organizations succeed with policies
and best practices that enable processes
and technology investments to produce
the most meaningful and effective out-
put. This includes a policy for informa-
tion collection.
If a bank customer files for bankruptcy,
a lien may be issued against its property.
This type of activity is an important factor
in the risk profile of the customer, and the
bank must ensure it is capturing this infor-mation in a single credit risk score. Banks
can build automatic alerts into their
processes to automatically make changes
to a customer’s profile as events occur.
An important best practice in the
public safety world that can be applied to
any industry is the value of sharing intel-
ligence with others who need it, when
they need it. For a multinational com-
pany, this is of utmost importance when
talking about different lines of business.
In a financial services company, for
example, that may range from credit and
mortgage to traditional banking services.
For today’s organizations, the solu-
tions exist to help bring the relevant data
— structured and unstructured — from
oceans of existing data sources into a
single environment where analytics can
be applied to easily and effectively shareintelligence and prevent fraud.
As Big Data continues to grow and
the global marketplace becomes increas-
ingly complex, organizations both large
and small are embracing data analytics
and implementing appropriate policies
to combat and prevent fraud.
Robert Griffin is vice president, Industry
Solutions, for International Business
Machines Corp.
Instead of using a one-size-fits-all single data
warehouse — where all data needs to be
migrated to one location — organizations
should seek out flexible, scalable approaches
that enable them to use existing resources and
bring the relevant data from their original sources
into a single repository for analysis.
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