using big data to combat fraud

8/12/2019 Using Big Data to Combat Fraud

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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.



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



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.



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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