Data analytics in fraud prevention

1. Data analytics in fraud prevention Durban, Aug 24TH 2014

2. Agenda Introductions General Concepts around Fraud ,Waste, and Abuse Overview of the SFF Screenshots Questions/Discussion

3. introductions Chris McAuley, Director, Security & Intelligence Practice Chris.McAuley@SAS.com +44 7747 100189 (m)

4. General Concepts on FWA

5. Healthcare Fraud: who has this problem?

6. Healthcare Fraud: How big is the problem? “…potential losses to healthcare fraud and corruption between €30-100 billion across Europe” “…estimates conservatively that $68 billion (3%) is fraud” “…approximately €180 billion euros or6 percent of global health care spending is lost to fraud each year”

7. Healthcare Fraud: wait…There’s more?? Estimates of 20% – 30% total FWAC in health care

8. Healthcare FWA: So these are different, right?

9. Fraud, waste, & abusea continuum Fraud Waste Abuse

10. Fraud, waste, & abuseNefariousness scaleTM Fraud Abuse Waste

11. Fraud, waste, & abuseHowever, in terms of [€£$]to the system… Waste Abuse Fraud

12. Fraud, waste, & abuseWe need to focus on all of it Fraud Waste Abuse

13. Combating fwa: how do we deal with it? • Sometimes prosecutions are in order: • Criminal organizations • Doctors committing true fraud • Grievously offending doctors • In other words – for Fraud • What if companies do not want to prosecute? • Bad PR • Bad for customer retention • Legal action not possible • What do they do about waste and abuse? • Want to develop alternative strategies for identifying and dealing with doctors who are engaging in aberrant behavior (as opposed to fraud)

14. comprehensive cost-containmentstrategy

15. Examples: Provider Fraud and Abuse

16. Examples: member Fraud

17. Examples: WASTE

18. Not Just Doctors Radiological Centers Personal Care Assistants Transportation Services Infusion Centers Pharmacies Nursing Homes Doctors Optometrists Dialysis Centers Substance Abuse Clinics Home Health Care Medical Equipment Suppliers Podiatrists Chiropractors Laboratories Dentists Hospitals Adult Foster Care

19. Overview of the SFF

20. SAS Fraud Framework END-TO-END SOLUTION for Health Insurance

21. SAS fraud framework Unique Hybrid approach to analytics Predictive modelling (example): Number of previous investigations on the network may be input to the predictive model of a suspicious claim Text mining (example): Harnessing call center data Anomaly detection (example): Providers that have volumes or intensity far above their peers Database Searches (example): Looking for matches across the lists of sanctioned providers or death master files Database Searches Text Mining Predictive Modeling Anomaly Detection Analytic Decisioning Engine Business rule (example): A claim is suspicious if it is submitted for a person of the wrong gender Automated Business Rules Social Network Analysis SNA (example): Looking for a number of similar connected actors

22. Industry best practiceUsing hybrid analytics for fraud detection • Leverage unstructured data elements in analytics • Examples: • Claim/call center notes high-lighting key fraud risks (e.g., policy questions) • Static data elements (e.g., address) used for linking suspicious activity • Integration of rich case file information Enterprise Data Network Analysis Text Mining • Associative discovery thru automated link analysis • Examples: • Provider/claimant associated to known fraud • Linked members with like suspicious behaviors • Suspicious referrals to linked providers • Collusive network of providers & referrals Employer Data Medical Procedure For known patterns For unknown patterns For complex patterns For unstructured data For associativelinking • Rules to surface known fraud behaviors • Examples: • Inaccurate eligibility information • Unlicensed or Suspended Provider • Daily provider billing exceeds possible • CPT up-coding • Value of charges for procedure exceeds threshold • Algorithms to surface unusual (out-of-band) behaviors • Examples: • Abnormal service volume compared to similar providers • Ratio of $ / procedure exceed norm • # patients from outside surrounding area exceeds norm • Identify attributes of known fraud behavior • Examples: • Like patterns of claims as confirmed known fraud • Provider behavior similar to known fraud cases • Like provider/ network growth rate (velocity) Rules Anomaly Detection Predictive Models Claims Payments Provider / Member Referral Known Bad Lists 3rd Party Data Proactively applies combination of all approaches at entity and network levels Hybrid Approach

23. Industry best practiceUsing hybrid analytics for fraud detection Internationally Developed IP • Leverage unstructured data elements in analytics • Examples: • Claim/call center notes high-lighting key fraud risks (e.g., policy questions) • Static data elements (e.g., address) used for linking suspicious activity • Integration of rich case file information Network Analysis Text Mining • Associative discovery thru automated link analysis • Examples: • Provider/claimant associated to known fraud • Linked members with like suspicious behaviors • Suspicious referrals to linked providers • Collusive network of providers & referrals Employer Data Medical Procedure • Rules to surface known fraud behaviors • Examples: • Inaccurate eligibility information • Unlicensed or Suspended Provider • Daily provider billing exceeds possible • CPT up-coding • Value of charges for procedure exceeds threshold • Algorithms to surface unusual (out-of-band) behaviors • Examples: • Abnormal service volume compared to similar providers • Ratio of $ / procedure exceed norm • # patients from outside surrounding area exceeds norm • Identify attributes of known fraud behavior • Examples: • Like patterns of claims as confirmed known fraud • Provider behavior similar to known fraud cases • Like provider/ network growth rate (velocity) Rules Anomaly Detection Predictive Models Claims Payments Provider / Member Referral Known Bad Lists 3rd Party Data Proactively applies combination of all approaches at entity and network levels Hybrid Approach

24. SAS Fraud framework Efficacy and the hybrid approach Additional variables further this benefit even more █Advanced analytics via Hybrid Analytics enhance fraud detection, improving the accuracy as well as finding cases near impossible in a manual process █Advanced analytics without Hybrid FRAUD If you examine 50% of the population, you would expect to find 50% of the fraud █ RANDOM POPULATION If the accuracy of detection doubles by using a hybrid approach, an investigation team would be able to find twice the amount of fraud with the same number of referrals!

25. SAS Fraud framework Improved identification, quality, and efficiency Detection Investigation Enhanced scoring model with network attributes and scores incorporated Visual representation of data from multiple systems in one single environment Capability Increase in volume & quality of fraud detected Increased efficiency during fraud investigations Outcome Benefit Increased fraud saving Improved operational saving

26. SAS Fraud Framework Our approach Alert Generation Data Management Ingest Cleansing Enrichment Quality analysis Entity resolution Social networks generation Operational Sources Fraud Detection Alert Management & Reporting GUI for self-administration Policies Investigations Claims Potential Fraud Risk Suspicious alerts for Investigation Investigations Intelligence Repository Watch-lists Case Management Actions taken Intelligence updates Additional sources Data updates

27. Screenshots?

28. SFF Screenshots – alerts view

29. SFF Screenshots – Drill into alert

30. SFF Screenshots – Drill into alert

31. SFF Screenshots – SNA

32. SFF Screenshots – SNA

33. SFF Screenshots – SNA

34. SFF Screenshots – SNA

35. SFF Screenshots – SNA

36. SFF Screenshots – Dashboards

37. SFF Screenshots – dashboards

38. SFF Screenshots – alert view

39. Questions?