1. SAS FOR CLAIMS FRAUD MORE INFORMATIONC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

2. GLOBAL INSURANCE CLAIMS FRAUD US Insurance Information Institute estimate $30 billion losses annually; about 10% incurred losses and lossadjustment expenses FBI estimate costs $40+ billion per annum; costing between $400 and $700 in extra premiums Insurance Council of Australia estimates that between 10 and 15% of insurance claims across of lines exhibitelements of fraud Swedish Association estimate that 5 to 10% of claims include fraud ALFA estimate that fraud 15% of claims paid, or 4-8% of premiums collected equating to 2.5bn per annum ABI estimates that undetected fraud = 2.1bn adding about 50 to average premium South Africa Insurance Crime Bureau estimate that 30% of short term insurance claims include fraud Swiss Insurance Association estimate that 10% of claims paid are fraudulent German Insurance Association estimates that fraud costs circa 4bn per annumC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 3. THE SHIFTING LANDSCAPE OF INSURANCE FRAUDInsurance fraud is on the rise & todays schemes are: Increasingly sophisticated More agile Higher velocity Cross industry Influenced by regulatory & political climateYesterdays methods are insufficientto address todays fraud risk!C op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 4. CHALLENGES TODEALING WITH DATAFRAUD DETECTIONA good fraud detection solution must: Integrate data from multiple disparate sources like claims,underwriting, human resources, billing/payment systems and 3rd partysources Match identities across all data sets Address data quality issues like misspellings, input errors, typos,missing data, acronyms, shorthand and jargon Leverage unstructured data text data sources like claims notes andservice logs Provide transparency & adaptability to quickly respond to changingfraud threatsC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 5. CHALLENGES TOTRANSPARENCYFRAUD DETECTION PushPullvs. Reliance on Advanced rules / red flagsdetection methods Inconsistent Consistent First-come, Optimal first-served prioritizationC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 6. CHALLENGES TOLEGACY SIU PROCESSFRAUD DETECTION Multi-claims organized frauds may be difficult for individual adjusters toidentify Organizational structures may be inadequate Relationships are increasingly importantand complex Business rules are marginally effective Supervised predictive models can be biased toward single-claim frauddetection Distinction between fraud vs. abuseC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 7. BUSINESS ANALYTICS AND FRAUD DETECTION Allows insurers to identify suspicious cases Works underneath the insurers existing processes Does not replace expertise of claims team members but ensures casesare not missed Allows insurers to detect fraud by multi-dimensions Case-by-case Repeat Organised ringsC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 8. FRAMEWORK-BASED END-TO-END SOLUTION APPROACH Data Detection ReportingAdministration Structured & Business Rules Advanced Ranking Self administered Unstructured Data Anomaly Detection Technology Custom alert queues Sources Easy to use web Advanced Alert suppression & Batch or real time Predictive Models based interfacerouting rules processing Advanced Query Watch Lists Workflow analysis Data Cleansing of integrated data Social Network Direct integration Data Integration Analysis Full businesswith Case Variable Extractionintelligence Management Network-level reporting capability & Sentimentanalytics Analysis with Text Claim system Mining Hybrid Technology integrationC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 9. SAS FRAUD FRAMEWORK FOR PROCESS FLOW INSURANCE OperationalExploratoryData SourcesData Analysis & Alert Generation ProcessTransformation Business AlertSAS Social FraudRules AdministrationNetwork Data AnalysisStaging NetworkProviders Rules AnalyticsAnomalyNetworkDetectionAnalyticsMembersPredictiveModeling FacilitiesAlert Management &BI / Reporting Intelligent Learn andFraud RepositoryImproveClaims CycleCase ManagementC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 10. FRAUD ANALYTICS USING A HYBRID APPROACH FOR FRAUD DETECTION Text MiningDatabasePredictiveSearchesModelingAnomalyDetectionAutomated AnalyticBusiness Rules Decisioning EngineSocialNetworkAnalysisLEVERAGING SAS HYBRID APPROACH TO SCORE TRANSACTIONS, ENTITIES, AND NETWORKS ACROSS MULTIPLE ORGANIZATIONSC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 11. WHY SAS? MAKING LIFE EASIER Establish Query Rank & Combine &Analysis DecisionFinalSearchVarious PrioritizeSynthesizeof to Analysis &ParametersSystemsResults Information Findings Proceed?Summary Framework-Based Predictive AnalyticsAnalytical Value-AddWhat used to take me most of a day, now takes 10 minutes.It completely streamlines where we need to go. -SIU AnalystC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 12. CUSTOMER STORY CNA (US)Business Problem Detect and prevent fraud in four separate commercial lines of businessCustomer Quote Optimally direct its investigation resources on cases with higher likelihood of fraudWe have an excellentpartnership with SAS.They took the time to Solutionmeet with us and trulyunderstand the nuances SAS Fraud Framework for Insuranceof CNA so that we couldbuild effective predictivemodels for each line ofour businessResults $2.1m in fraud recovery / prevention within the first 9Tim Wolfe, SIU Directormonths of implementation Detection and investigation of 15 potentially fraudulent provider networks four times what CNA anticipatedC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 13. WHY SAS? More suspicious cases identified Including both previously undetected fraudulent networks and extensions to already identifiedfraudWe discovered that 5% of its claims pay-outs were fraudulent, and these can now becorrected and prevented in the future."Assistant General Manager, Market Leader, Southern Europe Reduction in false positive rates Significant improvement in quality of suspicious cases past for investigation 84% of the claims flagged as possibly fraudulent, turned out to be fraud. A 69 % uplift in suspicious claim detection compared with the old system.." SIU Manager, Major Tier 1 USA Insurer Improved investigation efficiency Each referral taking 1/2 1/3 the time to investigate using SAS link analysis visualization What used to take me most of a day, now takes 10 minutes. SIU Manager, Major Tier 1 USA InsurerC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 14. MOREINFORMATION Contact information: Stuart Rose, SAS Global Insurance Marketing Director e-mail: Stuart.rose@sas.com Blog: Analytic Insurer Twitter: @stuartdrose White Papers: Combatting Insurance Claims Fraud Insurance Fraud Race Research: State of Insurance Fraud TechnologyC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . 15. THANK YOUC op yr i g h t 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . www.SAS.com