THE THE NEW NORM NEW NORM – IDENTIT IDENTITY Y THE THEFT FT AND AND GO GOVERNMENT VERNMENT FRA FRAUD UD Identity Fraud Data Breach Government (1 in 4) Fraud (43%) (12.6 M) 2013 Identity Theft Report – Javelin Strategy & Research FTC Consumer Sentinel Network Report - CY 2012
AN ANAL ALYT YTICS ICS IN IN THE THE BIG BIG DATA A ER ERA DATA SIZE TODAY THE FUTURE
THE “HOLY GRAIL” – PREPAYMENT DETECTION Persistent Myth? Real But Distant Future?
WHA WHAT T DOES DOES PRE PREPAYMENT YMENT LOOK OOK LIKE? LIKE? Provider Recipient MMIS - MMIS- MCO Enrollment Eligibility Provider Pharmacy
ADJUSTING ADJUSTING THE THE TAR ARGET GET COST AVOID COST OIDANCE ANCE PRE PREPAYMENT YMENT Relies on persistent Complete data analysis Timely data Utilize key decision points Detect outliers without Uses parallel analytical vs. patterns models for early detection Predictive models on Quick intervention single claims Light touch interventions
BEST PRACTICE – OPERATIONALIZE ANALYTICS 2 1 Alert Generation Process Operational Exploratory Fraud Data Data Sources Analysis & Staging Rules and Network Alert Data Analytics Analysis Administration Transformation Network Anomaly Recipients Rules Alert Detection Rules Network Predictive Providers Analytics Models 3 Claims Alert Management & Reporting Intelligent Learn and Fraud Improve Cycle Case Management Repositor y 4 5 External Data
BES BEST T PRA PRACTICE CTICE - ENT ENTITY ITY RES RESOL OLUTION UTION FR FROM OM MUL MULTIP TIPLE LE DATA S A SET ETS Claim data sets • Aggregate claims for entities to be used in outlier detection at entity level • Benefit: Analysis by provider ID might not be detected as outlier, but analysis by entity ID could show an obvious outlier Fraud data set • Some providers matched to be the same entity as a known bad provider • Benefit: Discovery of bad providers disguised by masking identities Linkage Analysis • Link entities together based on attributes • Used to create both hard and fuzzy relationships
HEALTH CANADA – DEVELOPING IN-HOUSE SURVEILLANCE OBJECTIVES • Move Detection In-House • Develop Surveillance Approach to Pharmacies, Prescribers and Clients SOLUTION • New Data Warehouse with 1 Billion+ Rows • Pilot Followed by Successful Rollout • Over 40 Custom Analytical Scenarios RESULTS • Multi -Million $ Savings • Continuous Surveillance and Rapid Intervention • Medicine Cabinet Improving Safety
BEST PRACTICE 3 – ENTERPRISE APPROACH Best Practice – An Enterprise Approach to Program Integrity BEST PRACTICE 3 – ENTERPRISE APPROACH Investigative Unit Optimization Prepare Data Model Decision Flow Monitor & Report Alert Generation Program Program Program Common Technology Framework Program Program Integrity Integrity Integrity Integrity Integrity Data Data Data Data Data Consolidated View of Data for Cross-Program Fraud Detection Access Access Access Access Access MCO Program Integrity Child Support Encounter Data Contractor MCO Medicaid Integrity TANF Encounter Data Contractor MMIS Programmatic MCO Medicaid Recovery Medicaid Data SNAP Eligibility Data Encounter Data Audit Contractor MCO Third Party Liability Other Data Encounter Data Contractor
WASHINGTON STATE WORKERS’ COMPENSATION OBJECTIVES • Maximize Impact of Limited Staff • Reduce False Positives • Improve Cross-Program Detection • Single Detection System SOLUTION • Data From 15 Programs, 5 Agencies • Phased Approach with Seven Years of Data. • Single, Integrated Scoring and Ranking RESULTS • 80% Drop in Triage Time • Decreased False Positives • 50% Increase in $/Case and 30:1 ROI • Light Touch Interaction and Policy Effects
BEST PRACTICE – HYBRID FRAUD DETECTION Data Matches Text Business Mining Rules Anomaly Detection Hybrid Hybrid Det Detection ection Engine Engine Link Analysis Predictive Models
BEST BEST PRA PRACTIC CTICE E – TAR ARGET GET NETW NETWOR ORKS KS • High risk and ROI • Connect various participants • Multiple data sources and link types • Network risk multiples • Utilize geospatial overlay to investigate
BES BEST T PRA PRACTI CTICE E – VIS VISUALIZA ALIZATION ION AND AND AD AD-HOC HOC AN ANAL ALYSIS SIS • Visualiz isualize e ca case se tr tren ends ds • Explor Explore to e to identify iden tify ou outli tlier ers • “Hotspots” ar are e e eviden vident • Dete Determine mine co correla elation tions • Per erson sonal and al and un unit me it metrics trics
LEARNING FROM OTHER GOVERNMENT PROGRAMS OBJECTIVES • Combat Identity Theft and “Ghost Returns ” • Analyze Big Data on a Single Platform • Prioritize Audits and Investigations SOLUTION • Integrated Analytics Platform and Multi-Year Analysis • Parallel Analytics for Continuous Monitoring • Pre -Refund Analytics RESULTS • Pre-Refund Detection • Pervasive Real-Time Analytics – 14M Returns on Peak Day • New Treatment Streams Billion Dollar Savings in Partial Year
IRS RETURN REVIEW PROGRAM – RETHINKING ANALYTICS AND INTERVENTION
Final Final Thou houghts ghts
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