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Using Advanced Analytics to Combat P&C Claims Fraud

Using Advanced Analytics to Combat P&C Claims Fraud



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.

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.



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Using Advanced Analytics to Combat P&C Claims Fraud Using Advanced Analytics to Combat P&C Claims Fraud Document Transcript

  • • Cognizant ReportsUsing Advanced Analytics to CombatP&C Claims FraudCombating the growing complexity and sophistication of claims fraudrequires P&C insurers to embrace predictive and advanced analytics, such astext, social media, link and geospatial analysis. By partnering with firms thatcan 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
  • 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 inflictingbasis. This model shifts the cost of owning tech- significant economic hardships for consumersnology infrastructure, processes and talent to the and businesses, which is further increasing thechosen partner. cost of fraud, especially in personal lines, accord- ing to 54% of the 143 insurers surveyed in AugustFraud: A Growing Menance 2012 by FICO and the Property Casualty InsurersOn average, insurers lose $30 billion annually Association of America (PCI).5to fraudulent claims, representing 10% of theirclaims expenses, according to the Insurance Infor- Fraud negatively impacts insurers’ bottom linesmation Institute (see Figure 1).2 Insurance fraud (reduced profitability due to the cost of fraudulentcan be divided into two categories: opportunistic/ claims that would otherwise not be incurred) andsoft fraud and professional/hard fraud. Oppor- competitiveness (delays in claims processing). Ittunistic fraud is committed by individuals who increases premiums for customers, as insurersinflate damages or repairs in a legitimate claim or charge them more to make up for the increaseprovide false information to reduce the premium in payouts. NICB estimates that fraud increasesamount. About half of P&C insurers lose 11 cents premiums by $200 to $300 per family, annually.to 30 cents or more per premium dollar to softfraud alone, according to the Insurance Research The Need for AnalyticsCouncil-Insurance Services Office.3 The P&C insurance industry continues to operate in an uncertain economic climate, withProfessional, or hard, types of fraud are low interest rates hampering investment income.committed by organized groups that steal The direct loss ratio rose by 2.7 points fromvehicles, deliberately damage property and stage 2010, to 67.5% in 2011,6 while the combined ratioaccidents. These gangs are well acquainted with in the first half of 2012 was 102%.7 Fraud, alongfraud detection systems and collude with doctors, with long-tail liabilities such as incurred but notattorneys, insiders in insurance companies, body reported (IBNR) liabilities, produce uncertainty,shops, etc. to lodge fraudulent claims. making it much tougher to assess accurate claims reserves and pricing of premiums.A growing concern for insurers is the increas-ing number of questionable claims4 referred to Compliance with the Dodd-Frank Wall Streetthe National Insurance Crime Bureau (NICB) by Reform and Consumer Protection Act8 andits member insurance companies. Between 2010 the expected impact of Solvency II9 beyondand 2011, property-related questionable claims EU borders requires U.S. insurers to investincreased by 2%, while casualty-related claims in enterprise risk management and relatedEstimated Annual Loss* Due to Fraud($B)40 34.3 34.835 30.1 31.2 30.2 30.9 31.330 28.0 28.7 28.7 27.22520 15 10 5 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011*Assuming 10% of P&C claim expenseSource: Insurance Information Institute, July 2012Figure 1 cognizant reports 2
  • support systems, adding to already strained a shortage of qualified employees. Claims staffoperating costs. There has also been an increase at many organizations must devote at least halfin 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 improvescost of serving customers. Superstorm Sandy claims processing. It reduces cycle time ascost insurers between $20 billion and $25 billion, suspicious claims are weeded out and sent foraccording to disaster-modeling company Risk further investigation, while legitimate claimsManagement Solutions Inc.10 Insurers are there- are prioritized. This, in turn, results in improvedfore looking to significantly reduce costs and customer service, as well as significant savingsimprove process efficiencies. for the organization.Claims are at the heart of P&C insurer Insurers have always had systems in place tooperations and account for about 80% of their identify fraudulent claims and special teamscosts. An efficient claims service is crucial for to investigate suspicious claims. However, thecreating a sustainable customer relationship. growing complexity of fraud and well-executedFurther, with long-tail liabilities looming, timely fraud schemes have exposed the limitationsmanagement of claims becomes very important. of traditional fraud-detection systems, such as internal audits, whistleblower hotlines to reportHowever, the claims departments at many fraud and software that flags anomalies based oninsurers are hamstrung by outdated tools and a pre-defined set of rules.Questionable Property Claims8,0006,0004,0002,000 0 Flood/water Suspicious Inflated Suspicious Fire/arson Hail damage disappearance/ damage theft/loss loss of jewelry (excluding vehicles) 2009 2010 2011Questionable Casualty Claims20,00016,00012,000 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 2011Source: NICB, February 2012Figure 2 cognizant reports 3
  • 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 companieshelps 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
  • In addition, by combining social network and and accident descriptions, which usually consistsocial media analytics, link analysis and geospa- of short or incomplete sentences, misspelledtial analysis, insurers can identify fraud that is words and abbreviations. Based on the key wordshard to detect using traditional methods. used to describe an incident, text analytics helps insurers detect attempted fraud by flaggingSocial Network and Social Media Analytics questionable incidents, exaggerated injuries andCustomers 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 injuredrelationships among people, groups, organiza- customer that contains key phrases such as “cartions and related systems. It reveals the strength moving slowly,” “head-on collision with anotherof the relationships and how information flows slow-moving car,” “complains of severe neckwithin the groups and, most importantly, group pain,” “reports excessive treatment costs,” etc.influencers. This provides valuable input on can help insurers determine whether the claimwhether a customer is affiliated with any fraudu- needs to be probed further. This ensures that onlylent group and helps to predict the chances of a cases with strong fraud patterns are forwardedparticular 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 AnalysisWith two out of three people in the U.S. using An individual claim may not appear false at firstsocial networking sites, tracking customers’ glance. Often, it is only when it is seen in thesocial media updates can help insurers investi- context of previous fraudulent claims, or claimsgate suspicious claims. By tracking social media with a high fraud score, that those anomaliesaccounts and applying social network analytics become apparent. Link analysis provides thatto the information on social media, insurers can larger picture for a claim. In the case of a cargain 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 thetors in California recently used Facebook to find claimants, the clinics where the claimants werethat four women, who staged an auto accident to treated and the body shop they used, thus leadingdefraud 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 largeignorant of the security settings that hide their group of injury claims are interrelated, geospatialinformation from others or do not bother to analysis can provide location-based informationenable 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 avice, which allows users to update their locations, claim. In the case of a staged accident, geospatialoffers 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 proximityText Mining to resources such as a lawyer, a body shop andText mining and predictive modeling will be the a medical provider. This provides investigatorsprimary tools that insurers will deploy to com- with evidence to pursue a hunch and to identifybat fraud in the next two years, according to a potential fraud rings.2012 study by SAS Institute and Coalition AgainstInsurance Fraud (CAIF) of 74 U.S. insurance exec- Geospatial analysis can also be used to identifyutives.13 the exact area affected by a natural disaster or an explosion, which helps determine the amountText analytics helps companies gain critical of risk to insured properties and weed out claimsinsights from large volumes of unstructured data, that are filed from areas that are not located insuch as adjuster notes, first notice of loss, e-mail the affected zone. cognizant reports 5
  • Challenges systems, insurers can build real-time analyticalInsurers generally use a combination of anti- capabilities that help in creating a just-in-timefraud 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 andand SAS survey. Fewer than 50% of respondents more meaningful and insurance companyuse more advanced techniques, such as workflow timely decisions. A large that deployedrouting, text mining, predictive modeling and U.S. insurance company real-time analyticsgeographic data mapping, while 12% do not use that deployed real-timeany anti-fraud technologies, the survey found. analytics to sift through to sift through unstructured claims data unstructured claimsA major obstacle to embracing analytics is the from two fraud-prone states data from twolack of enterprise-wide data management at found that more than 1,000many insurers. While insurance companies are insured customers were fraud-prone statesdata-rich, not many have made progress on actually high-risk custom- found that morethe data management front. Much of their data ers. Another insurer identi- than 1,000 insuredresides in numerous independent legacy systems, fied actionable claims worthoften resulting in data inconsistency. It is, there- $20 million within the first customers werefore, important that data structures across the three months of deploying actually high-riskorganization be standardized and inconsistencies fraud analytics.15 customers.resolved to realize the full benefits of analytics. BenefitsOther major challenges in deploying analytics While there is no denying that deployment ofinclude 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. Someand SAS study (see Figure 4).14 Some insurers also examples:cite legal and compliance issues that can arisefrom using social media data for investigations. • Efficient fraud detection reduces annual claims payouts.Overcoming Obstacles • The number of false positives identified andTo leverage the benefits of advanced analytics, pursued is minimized. This boosts employeeinsurers need to focus on fresh approaches to productivity, minimizes loss adjustmentdata management that can integrate disparate expenses and avoides customer ire and legalsystems 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%.16Challenges 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 issuesSource: The State of Insurance Fraud Technology, Coalition Against Insurance Fraud and SAS, September 2012Figure 4 cognizant reports 6
  • • 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 analyticalorganizations 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 ofand self-learning analytical models. We believe an ownership.ideal fraud detection approach must combine thebest of analytics and rules-based approaches. Open source projects, such as R and Apache Hadoop, are being used by organizations to doInsurers acknowledge that deploying predictive more with big data. While Apache Hadoop helpsanalytics is the most effective way to combat fraud, to efficiently store and manage huge volumesaccording 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 cansurvey by Strategy Meets Action of 165 insur- overcome the complexity of processing largeers, three quarters of the respondents plan to volumes of unstructured data and analyzingincrease their annual data and analytics spend- social media networks in short periods.ing between 2012 and 2014, with 19% planningInsurers 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% Decreasen=165Source: SMA Research, Data and Analysis, 2012Figure 5 cognizant reports 7
  • 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 adoptionand 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
  • 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
  • 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
  • CreditsAuthorVinaya Kumar Mylavarapu, Senior Research Associate, Cognizant Research CenterSubject Matter ExpertNipun Kapur, Director and Head of Analytics COE, Cognizant AnalyticsDesignHarleen Bhatia, Creative DirectorSuresh Sambandhan, DesignerAbout CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business processoutsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquarteredin 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. Withover 50 delivery centers worldwide and approximately 150,400 employees as of September 30, 2012, Cognizant is amember of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among thetop 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 anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.