P&C insurers need to embrace predictive and advanced analytics -- as well as analytics as a service -- to combat the growing complexity and sophistication of claims fraud.

1. • Cognizant Reports
Using Advanced Analytics to Combat
P&C Claims Fraud
Combating the growing complexity and sophistication of claims fraud
requires P&C insurers to embrace predictive and advanced analytics, such as
text, social media, link and geospatial analysis. By partnering with firms that
can deliver analytics as a service, insurers can improve their bottom lines,
enhance claims processing efficiencies and boost customer satisfaction.
Executive Summary analytics, insurers can establish relation-
Insurance fraud is the second biggest white-collar ships among various parties involved in a claim
crime in the U.S. after tax evasion, according to based on geographic location. This can help
the National Insurance Crime Bureau.1 As insur- detect highly sophisticated fraud, as well as
ers deal with an uncertain economic climate and organized crime rings.
intense competition, they must also grapple with
the increasing incidence and sophistication of Achieving this level of sophistication requires
fraud, not to mention the resulting losses. The an efficient model and approach to enterprise-
traditional methods of identifying fraud are no wide data management. Insurers must focus on
longer sufficient. breaking down data silos and ensuring a con-
tinuous flow of quality data across various func-
Advanced analytics can help insurers identify and tional areas of the organization to enable a more
reduce fraud-related losses, as well as condense systematic use of advanced analytics that detect
the claims cycle, resulting in improved customer and prevent fraud. Getting there requires a
satisfaction. Historical claims data, combined cultural shift toward fact-based decision-making,
with industry data, can be a starting point for which demands a major commitment from senior
insurers to identify common types of fraud early leadership.
in the claims process. Advanced analytics, such
as social media analytics and text mining, can However, many insurers still run their business
help insurers sift through and draw inferences on traditional database systems that operate
from unstructured data more quickly and con- in silos, resulting in data inconsistencies. One
vert it into insights that quickly aid in identifica- way to quickly and effectively extract value from
tion and avoidance of fraudulent claims. By using these vast data pools is to pursue analytics as a
link analytics in combination with geospatial service (AaaS), a new delivery model that enables
cognizant reports | december 2012

2. insurers to work closely with specialists who increased by 16% (see Figure 2, next page).
provide analytical insights on a pay-per-use The prolonged weak economy is inflicting
basis. This model shifts the cost of owning tech- significant economic hardships for consumers
nology infrastructure, processes and talent to the and businesses, which is further increasing the
chosen partner. cost of fraud, especially in personal lines, accord-
ing to 54% of the 143 insurers surveyed in August
Fraud: A Growing Menance 2012 by FICO and the Property Casualty Insurers
On average, insurers lose $30 billion annually Association of America (PCI).5
to fraudulent claims, representing 10% of their
claims expenses, according to the Insurance Infor- Fraud negatively impacts insurers’ bottom lines
mation Institute (see Figure 1).2 Insurance fraud (reduced profitability due to the cost of fraudulent
can be divided into two categories: opportunistic/ claims that would otherwise not be incurred) and
soft fraud and professional/hard fraud. Oppor- competitiveness (delays in claims processing). It
tunistic fraud is committed by individuals who increases premiums for customers, as insurers
inflate damages or repairs in a legitimate claim or charge them more to make up for the increase
provide false information to reduce the premium in payouts. NICB estimates that fraud increases
amount. About half of P&C insurers lose 11 cents premiums by $200 to $300 per family, annually.
to 30 cents or more per premium dollar to soft
fraud alone, according to the Insurance Research The Need for Analytics
Council-Insurance Services Office.3 The P&C insurance industry continues to
operate in an uncertain economic climate, with
Professional, or hard, types of fraud are low interest rates hampering investment income.
committed by organized groups that steal The direct loss ratio rose by 2.7 points from
vehicles, deliberately damage property and stage 2010, to 67.5% in 2011,6 while the combined ratio
accidents. These gangs are well acquainted with in the first half of 2012 was 102%.7 Fraud, along
fraud detection systems and collude with doctors, with long-tail liabilities such as incurred but not
attorneys, insiders in insurance companies, body reported (IBNR) liabilities, produce uncertainty,
shops, etc. to lodge fraudulent claims. making it much tougher to assess accurate claims
reserves and pricing of premiums.
A growing concern for insurers is the increas-
ing number of questionable claims4 referred to Compliance with the Dodd-Frank Wall Street
the National Insurance Crime Bureau (NICB) by Reform and Consumer Protection Act8 and
its member insurance companies. Between 2010 the expected impact of Solvency II9 beyond
and 2011, property-related questionable claims EU borders requires U.S. insurers to invest
increased by 2%, while casualty-related claims in enterprise risk management and related
Estimated Annual Loss* Due to Fraud
($B)
40
34.3 34.8
35
30.1 31.2 30.2 30.9 31.3
30 28.0 28.7 28.7
27.2
25
20
15
10
5
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
*Assuming 10% of P&C claim expense
Source: Insurance Information Institute, July 2012
Figure 1
cognizant reports 2

3. support systems, adding to already strained a shortage of qualified employees. Claims staff
operating costs. There has also been an increase at many organizations must devote at least half
in the frequency of natural catastrophes (see of their time to routine administrative tasks.
Figure 3, next page) and, consequently, in the Identifying fraudulent claims early improves
cost of serving customers. Superstorm Sandy claims processing. It reduces cycle time as
cost insurers between $20 billion and $25 billion, suspicious claims are weeded out and sent for
according to disaster-modeling company Risk further investigation, while legitimate claims
Management Solutions Inc.10 Insurers are there- are prioritized. This, in turn, results in improved
fore looking to significantly reduce costs and customer service, as well as significant savings
improve process efficiencies. for the organization.
Claims are at the heart of P&C insurer Insurers have always had systems in place to
operations and account for about 80% of their identify fraudulent claims and special teams
costs. An efficient claims service is crucial for to investigate suspicious claims. However, the
creating a sustainable customer relationship. growing complexity of fraud and well-executed
Further, with long-tail liabilities looming, timely fraud schemes have exposed the limitations
management of claims becomes very important. of traditional fraud-detection systems, such as
internal audits, whistleblower hotlines to report
However, the claims departments at many fraud and software that flags anomalies based on
insurers are hamstrung by outdated tools and a pre-defined set of rules.
Questionable Property Claims
8,000
6,000
4,000
2,000
0
Flood/water Suspicious Inflated Suspicious Fire/arson Hail damage
disappearance/ damage theft/loss
loss of jewelry (excluding vehicles)
2009 2010 2011
Questionable Casualty Claims
20,000
16,000
12,000
8,000
4,000
0
Duplicate
billing
Excessive
treatment
Billing for
services not
rendered
Unbundling/
Faked/
exaggerated
injury
Prior
injuries
Jump-in
Inflated
billing
Slip and fall
Solicitation
Provider/facility
improperly licensed
/incorporated
Staged/caused
accident
upcoding
2009 2010 2011
Source: NICB, February 2012
Figure 2
cognizant reports 3

4. Such systems can detect only some types of fraud Analytics for Improved Fraud Detection
(usually soft fraud) and not early enough for Insurers generate large volumes of customer
preventive action. They have also been known data, such as policy details, previous claims
to cause major embarrassment by flagging and information gathered from adjusters. This
legitimate claims as fraudulent. Additionally, in data can be used in combination with data from
a bid to retain customers in a highly competitive industry sources such as NICB to run predictive
environment, insurers have refrained from analytics to identify fraudulent claims early in the
making a serious effort to investigate suspi- claims process.
cious cases. Not surprisingly, fewer than 20% of
fraudulent claims are detected.11 For example, NICB’s ForeWARN database allows
member companies to search and identify
Insurers have large amounts of data that can whether a party had committed fraud in the past
help identify fraud. However, the data is usually and obtain additional information to develop
scattered across organiza- fraudulent patterns and trends. NICB also pro-
Predictive analytics tional silos and exists as vides analytics support to member companies
helps to quickly and unstructured impossible to
it practically
data, making to identify fraud patterns and exposure, helping
organizations in fraud investigations.12
more accurately use it for gaining meaning-
determine ful insights. To deal with Predictive analytics, which involves the use of
whether a claim this, enterprise-wide adopt
an
insurers must
data-
regression models and advanced techniques such
as neural networks, helps to quickly and more
needs further centric approach, clean and accurately determine whether a claim needs fur-
investigation integrate the historic claims ther investigation and to determine the complex-
and to determine data and stored overdispa-
years
collected
in
the ity of the claim. This speeds up the processing of
legitimate claims, resulting in improved customer
the complexity of rate databases, and develop satisfaction, as well as preventing payouts for
the claim. predictive models to gain a fraudulent claims. It also aids in assigning staff
complete view of custom- with the appropriate level of experience based on
ers and their transactions. This can help identify the severity of a claim. Insurers have also begun
a variety of fraud types quickly and effectively, deploying expert systems that apply artificial
reducing losses significantly. intelligence algorithms to proactively identify
fraudulent activities.
Catastrophe Insured Losses
($B)
80
71.7
60
40 36.9 33.9 32.9 32.6
25.8 28.5
25*
20 13.7 15.9
8.6
12.3 10.7 14 11.3
10.3 7.3 11.2 14.1
7.8 6 7.4
4.7 3.7
0
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
U.S. CAT losses in 2011 were the fifth highest in U.S. history on an inflation adjusted basis.
* Only Sandy-related losses, as estimated by Risk Management Solutions, Inc.
Note: 2001 figure includes $20.3B for 9/11 loss reported through 12/31/01. Includes only business and personal
property claims, business interruption and auto claims. Non-property/BI losses = $12.2B ($15.6B in 2011 dollars).
Source: Property Claims Service/ISO and Insurance Information Institute
Figure 3
cognizant reports 4

5. In addition, by combining social network and and accident descriptions, which usually consist
social media analytics, link analysis and geospa- of short or incomplete sentences, misspelled
tial analysis, insurers can identify fraud that is words and abbreviations. Based on the key words
hard to detect using traditional methods. used to describe an incident, text analytics helps
insurers detect attempted fraud by flagging
Social Network and Social Media Analytics questionable incidents, exaggerated injuries and
Customers share varying degrees of relationships treatment costs, reckless driving, etc. and recom-
with other individuals with whom they share mends actions.
group membership. Social network analytics,
for example, helps to identify proximities and For example, an adjuster’s notes of an injured
relationships among people, groups, organiza- customer that contains key phrases such as “car
tions and related systems. It reveals the strength moving slowly,” “head-on collision with another
of the relationships and how information flows slow-moving car,” “complains of severe neck
within the groups and, most importantly, group pain,” “reports excessive treatment costs,” etc.
influencers. This provides valuable input on can help insurers determine whether the claim
whether a customer is affiliated with any fraudu- needs to be probed further. This ensures that only
lent group and helps to predict the chances of a cases with strong fraud patterns are forwarded
particular customer committing fraud. to investigation units and improves an adjuster’s
ability to quickly process genuine claims.
Insurance companies are also increasingly min-
ing social media to detect and investigate fraud. Link Analysis and Geospatial Analysis
With two out of three people in the U.S. using An individual claim may not appear false at first
social networking sites, tracking customers’ glance. Often, it is only when it is seen in the
social media updates can help insurers investi- context of previous fraudulent claims, or claims
gate suspicious claims. By tracking social media with a high fraud score, that those anomalies
accounts and applying social network analytics become apparent. Link analysis provides that
to the information on social media, insurers can larger picture for a claim. In the case of a car
gain information about claimants, medical provid- accident involving multiple claimants, link analy-
ers, body shops, etc., as well as a claimant’s con- sis can use claimants’ addresses, phone numbers,
nection with organized crime networks. Investiga- vehicle numbers, etc. to unearth links among the
tors in California recently used Facebook to find claimants, the clinics where the claimants were
that four women, who staged an auto accident to treated and the body shop they used, thus leading
defraud insurers of about $40,000 and denied investigators to rings of professional fraudsters.
knowing each other, were in fact friends.
While link analysis allows investigators to under-
A majority of social media users are either stand whether the parties involved in a large
ignorant of the security settings that hide their group of injury claims are interrelated, geospatial
information from others or do not bother to analysis can provide location-based information
enable them, thus providing claims’ investigators related to a claim, as well as the physical prox-
with clues. For instance, Facebook’s location ser- imity of the claimants and others involved in a
vice, which allows users to update their locations, claim. In the case of a staged accident, geospatial
offers investigators insights into whether a car analysis provides information about the loca-
driver visited a bar before hitting a tree. tion of the accident, the distance between the
various claimants’ residences and their proximity
Text Mining to resources such as a lawyer, a body shop and
Text mining and predictive modeling will be the a medical provider. This provides investigators
primary tools that insurers will deploy to com- with evidence to pursue a hunch and to identify
bat fraud in the next two years, according to a potential fraud rings.
2012 study by SAS Institute and Coalition Against
Insurance Fraud (CAIF) of 74 U.S. insurance exec- Geospatial analysis can also be used to identify
utives.13 the exact area affected by a natural disaster or
an explosion, which helps determine the amount
Text analytics helps companies gain critical of risk to insured properties and weed out claims
insights from large volumes of unstructured data, that are filed from areas that are not located in
such as adjuster notes, first notice of loss, e-mail the affected zone.
cognizant reports 5

6. Challenges systems, insurers can build real-time analytical
Insurers generally use a combination of anti- capabilities that help in creating a just-in-time
fraud technologies. Older technologies, such as understanding of opera-
red flags/business rules and scoring capability, tional issues, effective A large U.S.
still dominate the scene, according to the CAIF fraud identification and
and SAS survey. Fewer than 50% of respondents more meaningful and
insurance company
use more advanced techniques, such as workflow timely decisions. A large that deployed
routing, text mining, predictive modeling and U.S. insurance company real-time analytics
geographic data mapping, while 12% do not use that deployed real-time
any anti-fraud technologies, the survey found. analytics to sift through
to sift through
unstructured claims data unstructured claims
A major obstacle to embracing analytics is the from two fraud-prone states data from two
lack of enterprise-wide data management at found that more than 1,000
many insurers. While insurance companies are insured customers were
fraud-prone states
data-rich, not many have made progress on actually high-risk custom- found that more
the data management front. Much of their data ers. Another insurer identi- than 1,000 insured
resides in numerous independent legacy systems, fied actionable claims worth
often resulting in data inconsistency. It is, there- $20 million within the first
customers were
fore, important that data structures across the three months of deploying actually high-risk
organization be standardized and inconsistencies fraud analytics.15 customers.
resolved to realize the full benefits of analytics.
Benefits
Other major challenges in deploying analytics While there is no denying that deployment of
include the lack of IT resources and concerns advanced analytics requires significant invest-
about return on investment, according to the CAIF ment, the benefits far outweigh the costs. Some
and SAS study (see Figure 4).14 Some insurers also examples:
cite legal and compliance issues that can arise
from using social media data for investigations. • Efficient fraud detection reduces annual claims
payouts.
Overcoming Obstacles • The number of false positives identified and
To leverage the benefits of advanced analytics, pursued is minimized. This boosts employee
insurers need to focus on fresh approaches to productivity, minimizes loss adjustment
data management that can integrate disparate expenses and avoides customer ire and legal
systems and effectively deal with data overflow. hassles. Advanced analytics helped a U.S.
By integrating predictive analytics with enterprise insurer improve its false-positive detection
rate by 17%.16
Challenges in Deploying Analytics
5%
7% Cost/benefit analysis (ROI)
Lack of IT resources
14%
36%
Proof of concept and unknown effectiveness
Acquisition and integration of data
38%
Legal and compliance issues
Source: The State of Insurance Fraud Technology, Coalition Against Insurance Fraud and SAS, September 2012
Figure 4
cognizant reports 6

7. • Claims processing cycle time can be reduced, to increase outlays by Insurers
resulting in faster processing of claims and more than 10% per year
acknowledge
increased customer satisfaction. (see Figure 5).
• Losses through payouts can be minimized, that deploying
thus eliminating the need to increase premi- Major insurers have predictive analytics
ums and thereby helping to build strong cus- employed statisticians and
is the most
tomer relationships. Santam, a South African predictive modelers and are
short-term insurer, saved $2.4 million on fraud- capable of building efficient effective way to
ulent claims within four months of deploying fraud detection models. combat fraud,
analytics. It also improved fraud detection However, many insurers
according to the
capabilities and unearthed a motor fraud ring are revisiting their decision
within one month of deploying analytics.17 to build in-house capabili- FICO and PCI study.
• Insurers committed to fighting fraud will be ties due to the complexities
able to send a strong message to fraudsters involved in handling analytics and the expertise
and enhance their image in the eyes of required for text mining, using social media and
customers. geospatial analysis. While in-house solutions offer
greater control over development, “operational-
Embracing Analytics as a Service izing” a fraud detection model and the infrastruc-
The growing complexity of fraud requires ture required to implement and run an analytical
organizations to move beyond rules-based and solution can be expensive.18 Vendor solutions,
judgmental approaches toward more fact-based on the other hand, offer lower total cost of
and self-learning analytical models. We believe an ownership.
ideal fraud detection approach must combine the
best of analytics and rules-based approaches. Open source projects, such as R and Apache
Hadoop, are being used by organizations to do
Insurers acknowledge that deploying predictive more with big data. While Apache Hadoop helps
analytics is the most effective way to combat fraud, to efficiently store and manage huge volumes
according to the FICO and PCI study. The increas- of data, R is widely used for data manipula-
ing confidence in analytics is reflected in the rise in tion, calculation and graphical display. Further,
data and analytics budgets. According to a recent by combining R and Hadoop, organizations can
survey by Strategy Meets Action of 165 insur- overcome the complexity of processing large
ers, three quarters of the respondents plan to volumes of unstructured data and analyzing
increase their annual data and analytics spend- social media networks in short periods.
ing between 2012 and 2014, with 19% planning
Insurers' IT Spending Plans for Data and Analytics (2012-14)
2%
Increase by more than 10% per year
19%
23%
Increase by 6%-10% per year
Increase by 1%-5% per year
21%
Spending will remain flat
35%
Decrease
n=165
Source: SMA Research, Data and Analysis, 2012
Figure 5
cognizant reports 7

8. However, deploying analytics is no easy The partner must have expertise in
task. Unstructured data accounts for about extracting meaningful insights from insurance-
80%19 of organizational data and is bound related social networks and
to grow at 60% annually,20 with the increas- social media and perform Organizations
ing chatter created on social media. The complex analyses on the should seek a
traditional IT infrastructure deployed by most data. The technology com-
insurers is insufficient to analyze such large ponent includes the part-
partner that can
volumes of data and requires organizations ner’s ability to integrate seamlessly marry
to invest in people, processes, IT tools and advanced analytics with analytics with
infrastructure. insurers’ claims systems,
and create new claims effi-
technology rather
Choosing the Right Partner ciencies and improve over- than a pure-play
With process virtualization and cloud comput- all claims effectiveness. analytics player
ing, opportunities now exist for cost-cutting
through global sourcing via As analytics processes
that may not have
The traditional the business process as a become standardized and industry domain
service (BPaaS) model. This can uniformly be applied expertise.
IT infrastructure can save precious Cap-Ex via cloud-enabled models
deployed by by transferring the cost of (harnessing the growing clout of utility comput-
most insurers acquiring expensive hard- ing architectures), we believe that insurers stand
ware, software and key talent to benefit greatly by associating themselves with
is insufficient through consumption-based partners that have invested in such capabilities.
to analyze large pricing models.
volumes of data Looking Forward
A subset of BPaaS, analytics To experience the potential of analytics, we
and requires as a service combines tradi- believe that insurers should:
organizations to tional knowledge process out-
invest in people, sourcing (KPO) and business • Develop an enterprise-wide data architecture.
processes, IT tools
process outsourcing (BPO) • Identify key areas for deploying analytics.
capabilities with more effi- • Design a comprehensive strategy for adoption
and infrastructure. cient, cloud-enabled ways and implementation of analytics, including
of delivering analytical insights. This approach information technology.
allows organizations to deploy analytics solu- • Develop a fact-based decision-making culture
tions tailored to their needs. The service can be focused on achieving specific goals.
increased or decreased as business requirements • Formulate customized strategies to capitalize
dictate, providing more Op-Ex flexibility. on unique data.
• Continuously innovate and renew analytics
Organizations should seek a partner that can implementation.
seamlessly marry analytics with technology • Enter into relationships with partners capable
rather than a pure-play analytics player that of providing AaaS to advance competitive
may not have industry domain expertise. The key advantage.
analytical component is derived from the
ability to understand various forms of insurance
fraud — ranging from early payment defaults to
more complex types of malfeasance — and devel-
oping predictive models capable of understand-
ing complex relationships and learning from his-
torical data.
cognizant reports 8

9. Footnotes
“Insurance Fraud: Understanding The Basics,” NICB, April 21, 2011, https://www.nicb.org/File%
1
20Library/Theft%20and%20Fraud%20Prevention/Fact%20Sheets/Public/insurancefraudpublic.pdf.
2
“Insurance Fraud,” Insurance Information Institute, June 2012, http://www.iii.org/assets/docs/pdf/
InsuranceFraud-072112.pdf.
3
“Fraud Stats,” Coalition Against Insurance Fraud, http://www.insurancefraud.org/statistics.htm.
4
According to NICB, a questionable claim is one that NICB member insurance companies refer to NICB
for closer review and investigation based upon one or more indicators of possible fraud. A single
questionable claim can contain up to seven different referral reasons.
5
“FICO PCI Insurance Fraud Survey,” FICO, October 4, 2012, http://www.fico.com/en/Company/News/
Pages/10-04-2012.aspx.
6
“Written Premium, Rising Loss Ratios Point to Continued Rate Increases,” PropertyCasualty360,
March 27, 2012, http://www.propertycasualty360.com/2012/03/27/written-premium-rising-loss-ratios-
point-to-contin.
7
“Property Casualty Insurers Benefit From Drop In Catastrophe Losses,” Property Casualty Insurers
Association of America, October 4, 2012, http://www.pciaa.net/LegTrack/web/NAIIPublications.nsf/
lookupwebcontent/4003FCCDBDDDB35186257A8E006C244?opendocument.
8
The Dodd-Frank Wall Street Reform and Consumer Protection Act requires insurers to comply
with data requests on sales, customer location, etc. from the Federal Insurance Office (FIO), Office of
Financial Research (OFR), in addition to state regulators. Further, insurers with more than $50 billion
in assets are identified as systematically important financial institutions (SIFI) and have to comply
with heightened regulations. This means big insurers must rewire their legacy systems to create more
effective reporting mechanisms, which is expensive and can result in a competitive disadvantage
when compared with smaller insurance companies.
9
Scheduled to come into effect on January 1, 2014, Solvency II requires U.S. insurers operating in
the European Union to comply with the regime’s new risk management and reporting and account-
ing standards. Meeting the data requirements and reporting frequency of Solvency II requires U.S.
insurers to invest in revamping and consolidating existing IT systems.
10
“Sandy May Cost Insurers Up To $25 Billion,” The Wall Street Journal, November 14, 2012, http://online.
wsj.com/article/SB10001424127887324735104578119301366617508.html.
11
“Predictive Analytics: A Powerful Weapon In Fight Against Fraud,” PropertyCasualty360, April 4, 2011,
http://www.propertycasualty360.com/2011/04/04/predictive-analytics-a-powerful-weapon-in-fight-ag.
12
“Join NICB,” NICB, https://www.nicb.org/about-nicb/join_nicb.
13
“The State of Insurance Fraud Technology,” SAS Institute, September 2012, http://www.sas.com/reg/
wp/corp/50373.
14
“The State of Insurance Fraud Technology,” Coalition Against Insurance Fraud, September 2012,
http://www.insurancefraud.org/downloads/techStudy_2012.pdf.
15
“Predictive Analytics Can End the Isolation,” PropertyCasualty360, October 1, 2012, http://www.
propertycasualty360.com/2012/10/01/predictive-analytics-can-end-the-isolation.
16
Ibid.
17
“Using IBM Analytics, Santam Saves $2.4 Million in Fraudulent Claims,” IBM, May 9, 2012, http://
www-03.ibm.com/press/us/en/pressrelease/37653.wss.
cognizant reports 9

10. 18
“Operationalizing a Fraud Detection Solution: Buy or Build?” Insurance & Technology, August 20,
2012, http://www.insurancetech.com/business-intelligence/operationalizing-a-fraud-detection-solut/
240005814.
“Data Storage: Managing Unstructured Data in the Cloud: 12 Factors to Consider,” July 27, 2011, eWeek,
19
http://www.eweek.com/c/a/Data-Storage/Managing-Unstructured-Data-in-the-Cloud-12-Factors-to-
Consider-215018/.
20
igital data, the majority of which is unstructured data, is expected to grow from 130 exabytes to
D
40,000 exabytes between 2005 and 2020, according to a 2012 survey by IDC and EMC.
References
• “Uncertainty Weighs Down U.S. Insurers,” Life Insurance International, July 2012, http://www.
goldbergsegalla.com/sites/default/files/uploads/JW_LifeInsuranceInternational_July2012.pdf.
• “You Know There Are Fraudulent Claims. Let’s Find Them Now,” Statsoft, 2011, http://www.statsoft.
com/portals/0/solutions/StatSoft_InsuranceFraud_Brochurev.pdf.
• “The Three Best Targets For Attacking P&C Insurance Fraud,” FICO, September 2010, http://www.efma.
com/efmaweb_files/file/Partnerships/Fico_Insights44_Insurance_Fraud.pdf.
•
“Driving Operational Excellence in Claims Management,” February 2011, http://www.deloitte.
com/assets/Dcom-UnitedStates/Local%20Assets/Documents/FSI/US_FSI_DrivingOperational
ExcellenceInClaimsManagement_022311.pdf.
• “Advanced ‘Big Data’ Analytics with R and Hadoop,” Revolution Analytics, 2011, http://www.revolution-
analytics.com/why-revolution-r/whitepapers/R-and-Hadoop-Big-Data-Analytics.pdf.
• “Identify Claims Fraud with Advanced Analytics: Uncover Fraud that Traditional Methods Cannot Find,”
FICO, November 1, 2011, http://www.fico.com/en/FIResourcesLibrary/NewYorkInsuranceForum2011-3_
Identify_Insurance_Claims_Fraud.pdf.
• “Fraud Detection Acid Test,” SAS Institute, October 4, 2012, http://www.sas.com/knowledge-exchange/
risk/fraud-financial-crimes/fraud-detection-acid-test/index.html.
cognizant reports 10

11. Credits
Author
Vinaya Kumar Mylavarapu, Senior Research Associate, Cognizant Research Center
Subject Matter Expert
Nipun Kapur, Director and Head of Analytics COE, Cognizant Analytics
Design
Harleen Bhatia, Creative Director
Suresh Sambandhan, Designer
About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process
outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered
in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep in-
dustry and business process expertise, and a global, collaborative workforce that embodies the future of work. With
over 50 delivery centers worldwide and approximately 150,400 employees as of September 30, 2012, Cognizant is a
member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the
top performing and fastest growing companies in the world.
Visit us online at www.cognizant.com for more information.
World Headquarters European Headquarters India Operations Headquarters
500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road
Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam
Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India
Fax: +1 201 801 0243 Phone: +44 (0) 207 297 7600 Phone: +91 (0) 44 4209 6000
Toll Free: +1 888 937 3277 Fax: +44 (0) 207 121 0102 Fax: +91 (0) 44 4209 6060
Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com
©
­­ Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is
subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.