Using Advanced Analytics to Combat P&C Claims FraudCombating 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 SummaryInsurance fraud is the second biggest white-collar crime in the U.S. after tax evasion, according to the National Insurance Crime Bureau.1 As insur-ers deal with an uncertain economic climate and intense competition, they must also grapple with the increasing incidence and sophistication of fraud, not to mention the resulting losses. The traditional methods of identifying fraud are no longer sufficient.

Advanced analytics can help insurers identify and reduce fraud-related losses, as well as condense the claims cycle, resulting in improved customer satisfaction. Historical claims data, combined with industry data, can be a starting point for insurers to identify common types of fraud early in the claims process. Advanced analytics, such as social media analytics and text mining, can help insurers sift through and draw inferences from unstructured data more quickly and con-vert it into insights that quickly aid in identifica-tion and avoidance of fraudulent claims. By using link analytics in combination with geospatial

analytics, insurers can establish relation-ships among various parties involved in a claim based on geographic location. This can help detect highly sophisticated fraud, as well as organized crime rings.

Achieving this level of sophistication requires an efficient model and approach to enterprise-wide data management. Insurers must focus on breaking down data silos and ensuring a con-tinuous flow of quality data across various func-tional areas of the organization to enable a more systematic use of advanced analytics that detect and prevent fraud. Getting there requires a cultural shift toward fact-based decision-making, which demands a major commitment from senior leadership.

However, many insurers still run their business on traditional database systems that operate in silos, resulting in data inconsistencies. One way to quickly and effectively extract value from these vast data pools is to pursue analytics as a service (AaaS), a new delivery model that enables

cognizant reports | december 2012

• Cognizant Reports

cognizant reports 2

insurers to work closely with specialists who provide analytical insights on a pay-per-use basis. This model shifts the cost of owning tech-nology infrastructure, processes and talent to the chosen partner.

Fraud: A Growing MenaceOn average, insurers lose $30 billion annually to fraudulent claims, representing 10% of their claims expenses, according to the Insurance Infor-mation Institute (see Figure 1).2 Insurance fraud can be divided into two categories: opportunistic/soft fraud and professional/hard fraud. Oppor-tunistic fraud is committed by individuals who inflate damages or repairs in a legitimate claim or provide false information to reduce the premium amount. About half of P&C insurers lose 11 cents to 30 cents or more per premium dollar to soft fraud alone, according to the Insurance Research Council-Insurance Services Office.3

Professional, or hard, types of fraud are committed by organized groups that steal vehicles, deliberately damage property and stage accidents. These gangs are well acquainted with fraud detection systems and collude with doctors, attorneys, insiders in insurance companies, body shops, etc. to lodge fraudulent claims.

A growing concern for insurers is the increas-ing number of questionable claims4 referred to the National Insurance Crime Bureau (NICB) by its member insurance companies. Between 2010 and 2011, property-related questionable claims increased by 2%, while casualty-related claims

Estimated Annual Loss* Due to Fraud

Figure 1

*Assuming 10% of P&C claim expense

Source: Insurance Information Institute, July 2012

($B)

27.2 28.0 28.7 30.1 31.228.7 30.2

34.330.9 31.3

34.8

0

5

10

15

20

25

30

35

40

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

increased by 16% (see Figure 2, next page). The prolonged weak economy is inflicting significant economic hardships for consumers and businesses, which is further increasing the cost of fraud, especially in personal lines, accord-ing to 54% of the 143 insurers surveyed in August 2012 by FICO and the Property Casualty Insurers Association of America (PCI).5

Fraud negatively impacts insurers’ bottom lines (reduced profitability due to the cost of fraudulent claims that would otherwise not be incurred) and competitiveness (delays in claims processing). It increases premiums for customers, as insurers charge them more to make up for the increase in payouts. NICB estimates that fraud increases premiums by $200 to $300 per family, annually.

The Need for AnalyticsThe P&C insurance industry continues to operate in an uncertain economic climate, with low interest rates hampering investment income. The direct loss ratio rose by 2.7 points from 2010, to 67.5% in 2011,6 while the combined ratio in the first half of 2012 was 102%.7 Fraud, along with long-tail liabilities such as incurred but not reported (IBNR) liabilities, produce uncertainty, making it much tougher to assess accurate claims reserves and pricing of premiums.

Compliance with the Dodd-Frank Wall Street Reform and Consumer Protection Act8 and the expected impact of Solvency II9 beyond EU borders requires U.S. insurers to invest in enterprise risk management and related

cognizant reports 3

support systems, adding to already strained operating costs. There has also been an increase in the frequency of natural catastrophes (see Figure 3, next page) and, consequently, in the cost of serving customers. Superstorm Sandy cost insurers between $20 billion and $25 billion, according to disaster-modeling company Risk Management Solutions Inc.10 Insurers are there-fore looking to significantly reduce costs and improve process efficiencies.

Claims are at the heart of P&C insurer operations and account for about 80% of their costs. An efficient claims service is crucial for creating a sustainable customer relationship. Further, with long-tail liabilities looming, timely management of claims becomes very important.

However, the claims departments at many insurers are hamstrung by outdated tools and

a shortage of qualified employees. Claims staff at many organizations must devote at least half of their time to routine administrative tasks. Identifying fraudulent claims early improves claims processing. It reduces cycle time as suspicious claims are weeded out and sent for further investigation, while legitimate claims are prioritized. This, in turn, results in improved customer service, as well as significant savings for the organization.

Insurers have always had systems in place to identify fraudulent claims and special teams to investigate suspicious claims. However, the growing complexity of fraud and well-executed fraud schemes have exposed the limitations of traditional fraud-detection systems, such as internal audits, whistleblower hotlines to report fraud and software that flags anomalies based on a pre-defined set of rules.

Questionable Property Claims

Questionable Casualty Claims

Figure 2

0

2,000

4,000

6,000

8,000

Flood/water Suspicious disappearance/loss of jewelry

Inflated damage

Suspicious theft/loss

(excluding vehicles)

Fire/arson Hail damage

2009 2010 2011

2009 2010 2011

Source: NICB, February 2012

0

4,000

8,000

12,000

16,000

20,000

Du

plic

ate

bill

ing

Exc

essi

vetr

eatm

ent

Bill

ing

fo

r s

ervi

ces

no

t r

end

ered

Un

bu

nd

ling

/u

pco

din

g

Fake

d/

exag

ger

ated

inju

ry

Pri

or

inju

ries

Ju

mp

-in

Infl

ated

bill

ing

Slip

an

d f

all

So

licit

atio

n

Pro

vid

er/f

acili

ty im

pro

per

ly li

cen

sed

/in

corp

ora

ted

Sta

ged

/cau

sed

acc

iden

t

cognizant reports 4

Such systems can detect only some types of fraud (usually soft fraud) and not early enough for preventive action. They have also been known to cause major embarrassment by flagging legitimate claims as fraudulent. Additionally, in a bid to retain customers in a highly competitive environment, insurers have refrained from making a serious effort to investigate suspi-cious cases. Not surprisingly, fewer than 20% of fraudulent claims are detected.11

Insurers have large amounts of data that can help identify fraud. However, the data is usually

scattered across organiza-tional silos and exists as unstructured data, making it practically impossible to use it for gaining meaning-ful insights. To deal with this, insurers must adopt an enterprise-wide data-centric approach, clean and integrate the historic claims data collected over the years and stored in dispa-rate databases, and develop predictive models to gain a complete view of custom-

ers and their transactions. This can help identify a variety of fraud types quickly and effectively, reducing losses significantly.

Catastrophe Insured Losses

Figure 3

* Only Sandy-related losses, as estimated by Risk Management Solutions, Inc.

U.S. CAT losses in 2011 were the fifth highest in U.S. history on an inflation adjusted basis.

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

13.7

4.7 7.8

36.9

8.6

25.8

12.3 10.73.7

14 11.36

33.9

7.4

15.9

32.9

71.7

10.3 7.3

28.5

11.2 14.1

32.625*

0

20

40

60

80

198

9

199

0

199

1

199

2

199

3

199

4

199

5

199

6

199

7

199

8

199

9

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12($B)

Predictive analytics helps to quickly and

more accurately determine

whether a claim needs further investigation

and to determine the complexity of

the claim.

Analytics for Improved Fraud DetectionInsurers generate large volumes of customer data, such as policy details, previous claims and information gathered from adjusters. This data can be used in combination with data from industry sources such as NICB to run predictive analytics to identify fraudulent claims early in the claims process.

For example, NICB’s ForeWARN database allows member companies to search and identify whether a party had committed fraud in the past and obtain additional information to develop fraudulent patterns and trends. NICB also pro-vides analytics support to member companies to identify fraud patterns and exposure, helping organizations in fraud investigations.12

Predictive analytics, which involves the use of regression models and advanced techniques such as neural networks, helps to quickly and more accurately determine whether a claim needs fur-ther investigation and to determine the complex-ity of the claim. This speeds up the processing of legitimate claims, resulting in improved customer satisfaction, as well as preventing payouts for fraudulent claims. It also aids in assigning staff with the appropriate level of experience based on the severity of a claim. Insurers have also begun deploying expert systems that apply artificial intelligence algorithms to proactively identify fraudulent activities.

cognizant reports 5

In addition, by combining social network and social media analytics, link analysis and geospa-tial analysis, insurers can identify fraud that is hard to detect using traditional methods.

Social Network and Social Media Analytics Customers share varying degrees of relationships with other individuals with whom they share group membership. Social network analytics, for example, helps to identify proximities and relationships among people, groups, organiza-tions and related systems. It reveals the strength of the relationships and how information flows within the groups and, most importantly, group influencers. This provides valuable input on whether a customer is affiliated with any fraudu-lent group and helps to predict the chances of a particular customer committing fraud.

Insurance companies are also increasingly min-ing social media to detect and investigate fraud. With two out of three people in the U.S. using social networking sites, tracking customers’ social media updates can help insurers investi-gate suspicious claims. By tracking social media accounts and applying social network analytics to the information on social media, insurers can gain information about claimants, medical provid-ers, body shops, etc., as well as a claimant’s con-nection with organized crime networks. Investiga-tors in California recently used Facebook to find that four women, who staged an auto accident to defraud insurers of about $40,000 and denied knowing each other, were in fact friends.

A majority of social media users are either ignorant of the security settings that hide their information from others or do not bother to enable them, thus providing claims’ investigators with clues. For instance, Facebook’s location ser-vice, which allows users to update their locations, offers investigators insights into whether a car driver visited a bar before hitting a tree.

Text Mining Text mining and predictive modeling will be the primary tools that insurers will deploy to com-bat fraud in the next two years, according to a 2012 study by SAS Institute and Coalition Against Insurance Fraud (CAIF) of 74 U.S. insurance exec-utives.13

Text analytics helps companies gain critical insights from large volumes of unstructured data, such as adjuster notes, first notice of loss, e-mail

and accident descriptions, which usually consist of short or incomplete sentences, misspelled words and abbreviations. Based on the key words used to describe an incident, text analytics helps insurers detect attempted fraud by flagging questionable incidents, exaggerated injuries and treatment costs, reckless driving, etc. and recom-mends actions.

For example, an adjuster’s notes of an injured customer that contains key phrases such as “car moving slowly,” “head-on collision with another slow-moving car,” “complains of severe neck pain,” “reports excessive treatment costs,” etc. can help insurers determine whether the claim needs to be probed further. This ensures that only cases with strong fraud patterns are forwarded to investigation units and improves an adjuster’s ability to quickly process genuine claims.

Link Analysis and Geospatial AnalysisAn individual claim may not appear false at first glance. Often, it is only when it is seen in the context of previous fraudulent claims, or claims with a high fraud score, that those anomalies become apparent. Link analysis provides that larger picture for a claim. In the case of a car accident involving multiple claimants, link analy-sis can use claimants’ addresses, phone numbers, vehicle numbers, etc. to unearth links among the claimants, the clinics where the claimants were treated and the body shop they used, thus leading investigators to rings of professional fraudsters.

While link analysis allows investigators to under-stand whether the parties involved in a large group of injury claims are interrelated, geospatial analysis can provide location-based information related to a claim, as well as the physical prox-imity of the claimants and others involved in a claim. In the case of a staged accident, geospatial analysis provides information about the loca-tion of the accident, the distance between the various claimants’ residences and their proximity to resources such as a lawyer, a body shop and a medical provider. This provides investigators with evidence to pursue a hunch and to identify potential fraud rings.

Geospatial analysis can also be used to identify the exact area affected by a natural disaster or an explosion, which helps determine the amount of risk to insured properties and weed out claims that are filed from areas that are not located in the affected zone.

cognizant reports 6

ChallengesInsurers generally use a combination of anti-fraud technologies. Older technologies, such as red flags/business rules and scoring capability, still dominate the scene, according to the CAIF and SAS survey. Fewer than 50% of respondents use more advanced techniques, such as workflow routing, text mining, predictive modeling and geographic data mapping, while 12% do not use any anti-fraud technologies, the survey found.

A major obstacle to embracing analytics is the lack of enterprise-wide data management at many insurers. While insurance companies are data-rich, not many have made progress on the data management front. Much of their data resides in numerous independent legacy systems, often resulting in data inconsistency. It is, there-fore, important that data structures across the organization be standardized and inconsistencies resolved to realize the full benefits of analytics.

Other major challenges in deploying analytics include the lack of IT resources and concerns about return on investment, according to the CAIF and SAS study (see Figure 4).14 Some insurers also cite legal and compliance issues that can arise from using social media data for investigations.

Overcoming Obstacles To leverage the benefits of advanced analytics, insurers need to focus on fresh approaches to data management that can integrate disparate systems and effectively deal with data overflow. By integrating predictive analytics with enterprise

systems, insurers can build real-time analytical capabilities that help in creating a just-in-time understanding of opera-tional issues, effective fraud identification and more meaningful and timely decisions. A large U.S. insurance company that deployed real-time analytics to sift through unstructured claims data from two fraud-prone states found that more than 1,000 insured customers were actually high-risk custom-ers. Another insurer identi-fied actionable claims worth $20 million within the first three months of deploying fraud analytics.15

BenefitsWhile there is no denying that deployment of advanced analytics requires significant invest-ment, the benefits far outweigh the costs. Some examples:

• Efficient fraud detection reduces annual claims payouts.

• The number of false positives identified and pursued is minimized. This boosts employee productivity, minimizes loss adjustment expenses and avoides customer ire and legal hassles. Advanced analytics helped a U.S. insurer improve its false-positive detection rate by 17%.16

Challenges in Deploying Analytics

36%

38%

14%

7%5%

Cost/benefit analysis (ROI)

Lack of IT resources

Proof of concept and unknown effectiveness

Acquisition and integration of data

Legal and compliance issues

Figure 4

Source: The State of Insurance Fraud Technology, Coalition Against Insurance Fraud and SAS, September 2012

A large U.S. insurance company that deployed real-time analytics to sift through unstructured claims data from two fraud-prone states found that more than 1,000 insured customers were actually high-risk customers.

cognizant reports 7

• Claims processing cycle time can be reduced, resulting in faster processing of claims and increased customer satisfaction.

• Losses through payouts can be minimized, thus eliminating the need to increase premi-ums and thereby helping to build strong cus-tomer relationships. Santam, a South African short-term insurer, saved $2.4 million on fraud-ulent claims within four months of deploying analytics. It also improved fraud detection capabilities and unearthed a motor fraud ring within one month of deploying analytics.17

• Insurers committed to fighting fraud will be able to send a strong message to fraudsters and enhance their image in the eyes of customers.

Embracing Analytics as a Service The growing complexity of fraud requires organizations to move beyond rules-based and judgmental approaches toward more fact-based and self-learning analytical models. We believe an ideal fraud detection approach must combine the best of analytics and rules-based approaches.

Insurers acknowledge that deploying predictive analytics is the most effective way to combat fraud, according to the FICO and PCI study. The increas-ing confidence in analytics is reflected in the rise in data and analytics budgets. According to a recent survey by Strategy Meets Action of 165 insur-ers, three quarters of the respondents plan to increase their annual data and analytics spend-ing between 2012 and 2014, with 19% planning

to increase outlays by more than 10% per year (see Figure 5).

Major insurers have employed statisticians and predictive modelers and are capable of building efficient fraud detection models. However, many insurers are revisiting their decision to build in-house capabili-ties due to the complexities involved in handling analytics and the expertise required for text mining, using social media and geospatial analysis. While in-house solutions offer greater control over development, “operational-izing” a fraud detection model and the infrastruc-ture required to implement and run an analytical solution can be expensive.18 Vendor solutions, on the other hand, offer lower total cost of ownership.

Open source projects, such as R and Apache Hadoop, are being used by organizations to do more with big data. While Apache Hadoop helps to efficiently store and manage huge volumes of data, R is widely used for data manipula-tion, calculation and graphical display. Further, by combining R and Hadoop, organizations can overcome the complexity of processing large volumes of unstructured data and analyzing social media networks in short periods.

Insurers' IT Spending Plans for Data and Analytics (2012-14)

Figure 5

n=165

Source: SMA Research, Data and Analysis, 2012

19%

21%

35%

23%

2%

Increase by more than 10% per year

Increase by 6%-10% per year

Increase by 1%-5% per year

Spending will remain flat

Decrease

Insurers acknowledge that deploying predictive analytics is the most effective way to combat fraud, according to the FICO and PCI study.

cognizant reports 8

However, deploying analytics is no easy task. Unstructured data accounts for about 80%19 of organizational data and is bound to grow at 60% annually,20 with the increas-ing chatter created on social media. The traditional IT infrastructure deployed by most insurers is insufficient to analyze such large volumes of data and requires organizations to invest in people, processes, IT tools and infrastructure.

Choosing the Right Partner With process virtualization and cloud comput-ing, opportunities now exist for cost-cutting

through global sourcing via the business process as a service (BPaaS) model. This can save precious Cap-Ex by transferring the cost of acquiring expensive hard-ware, software and key talent through consumption-based pricing models.

A subset of BPaaS, analytics as a service combines tradi-tional knowledge process out-sourcing (KPO) and business process outsourcing (BPO) capabilities with more effi-cient, cloud-enabled ways

of delivering analytical insights. This approach allows organizations to deploy analytics solu-tions tailored to their needs. The service can be increased or decreased as business requirements dictate, providing more Op-Ex flexibility.

Organizations should seek a partner that can seamlessly marry analytics with technology rather than a pure-play analytics player that may not have industry domain expertise. The key 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.

The partner must have expertise in extracting meaningful insights from insurance-related social networks and social media and perform complex analyses on the data. The technology com-ponent includes the part-ner’s ability to integrate advanced analytics with insurers’ claims systems, and create new claims effi-ciencies and improve over-all claims effectiveness.

As analytics processes become standardized and can uniformly be applied via cloud-enabled models (harnessing the growing clout of utility comput-ing architectures), we believe that insurers stand to benefit greatly by associating themselves with partners that have invested in such capabilities.

Looking Forward To experience the potential of analytics, we believe that insurers should:

• Develop an enterprise-wide data architecture.

• Identify key areas for deploying analytics.

• Design a comprehensive strategy for adoption and implementation of analytics, including information technology.

• Develop a fact-based decision-making culture focused on achieving specific goals.

• Formulate customized strategies to capitalize on unique data.

• Continuously innovate and renew analytics implementation.

• Enter into relationships with partners capable of providing AaaS to advance competitive advantage.

The traditional IT infrastructure

deployed by most insurers is insufficient

to analyze large volumes of data

and requires organizations to invest in people,

processes, IT tools and infrastructure.

Organizations should seek a partner that can seamlessly marry analytics with technology rather than a pure-play analytics player that may not have industry domain expertise.

cognizant reports 9

Footnotes1 “Insurance Fraud: Understanding The Basics,” NICB, April 21, 2011, https://www.nicb.org/File%

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 10

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.

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.

19 “Data Storage: Managing Unstructured Data in the Cloud: 12 Factors to Consider,” July 27, 2011, eWeek, http://www.eweek.com/c/a/Data-Storage/Managing-Unstructured-Data-in-the-Cloud-12-Factors-to-Consider-215018/.

20 Digital data, the majority of which is unstructured data, is expected to grow from 130 exabytes to 40,000 exabytes between 2005 and 2020, according to a 2012 survey by IDC and EMC.

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

500 Frank W. Burr Blvd.Teaneck, NJ 07666 USAPhone: +1 201 801 0233Fax: +1 201 801 0243Toll Free: +1 888 937 3277Email: inquiry@cognizant.com

European Headquarters

1 Kingdom StreetPaddington CentralLondon W2 6BDPhone: +44 (0) 207 297 7600Fax: +44 (0) 207 121 0102Email: infouk@cognizant.com

India Operations Headquarters

#5/535, Old Mahabalipuram RoadOkkiyam Pettai, ThoraipakkamChennai, 600 096 IndiaPhone: +91 (0) 44 4209 6000Fax: +91 (0) 44 4209 6060Email: 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.

Credits

Author Vinaya Kumar Mylavarapu, Senior Research Associate, Cognizant Research Center

Subject Matter ExpertNipun Kapur, Director and Head of Analytics COE, Cognizant Analytics

DesignHarleen Bhatia, Creative DirectorSuresh Sambandhan, Designer