How Fraud, Waste and Abuse are Seen Outside of North America

1. How Fraud, Waste and Abuse are Seen Outside of North America “Are the fraudsters all reading the same fraud manual?” Paul CrowderPre Sales ConsultingFICO 08 May 2014

2. The International Healthcare Landscape • The Blessing & the Curse of Auto-Adjudication • Common Payment Integrity Themes • The Range of Approaches to Payment Integrity • The Demand for Force-Multiplying Technology • Questions?

3. The International Healthcare Landscape • Healthcare delivery models vary • National Healthcare Systems • Single Payer, Single Provider • Single Payer, Multi Provider • Fee-For-Service Healthcare • Combinations of the above • Healthcare systems and payment policies vary • By geographic region • By plan • Everyone loses money to fraud, waste and abuse.

4. Case Study 1: Mathematics are the Universal Language • The Client: A European Healthcare Payer • The Engagement: A competitive “bakeoff” for detection of fraudulent dentists • The Approach: • Scoring of the Scheme’s Dentistry Providers • Delivery of results that are “blind” due to a language barrier between FICO & the client. • The Results: € Many Millions in Savings from review of 20 high scoring dentists (14 were found fraudulent). • The Conclusion: Data-driven predictive analytics that use mathematics deliver universal results

5. The Blessing & the Curse of Auto-Adjudication • Who processes any of their healthcare claims manually? • If not now, soon: Nobody • The Advantages of Auto-Adjudication • For the Providers: Quick Payment • For the Members: Limited involvement • For the Payers, payment of: • The correct claims • For the correct amount • For your members • For providers who are authorized to participate in your plans. • The Problem with Auto-Adjudication: The Payer will automatically pay claims that, by policy, contract or design, should not be paid.

6. Case Study 2: Mathematics are Effective in All Lines of Business • The Client: A European Pharmacy Payer • The Engagement: An IFM POV to detect aberrant pharmacy claims • The Approach: • Scoring of a 12 month set of the Payer’s Pharmacy Claims • Review and decisioning of a subset of claims by the payer’s Pharmacy Material Control group • The Results: € Millions in Savings from review of 7,000 high scoring claim lines • The Conclusion: Data-driven predictive analytics that use mathematics deliver universal results in all lines of business

7. Common Payment IntegrityThemes • Every payer loses money to fraud, waste and abuse • The payers’ views of the problem vary • “Our regulatory and contractual controls stop the losses. (We hope.)” • We’ve spent years, and a lot of money, setting up straight through processing (and we haven’t had the time or the money to address fraud, waste and abuse).” • “Our administrator is responsible for stopping fraud, waste and abuse. If we’re losing money to FWA, it’s their fault, and if we discover that, we’ll make them pay for it.” • “We’re solvent, so the losses aren’t important.” • “All providers are thieves.” • “The patients are thieves.” • “Our payment system stops inappropriate claims.” • “I’m terrified that we’ll get our name in the paper.” • “The quality of our data is so poor that we can’t do anything.” • “Our BI processes are so cumbersome that we can’t do very much.” • “We have no idea what our losses really are, and the prospect of discovering the magnitude of the problem makes us really nervous.”

8. The Universal Lure of Data-Driven Predictive AnalyticsEveryone “Gets It” Queries/Rules Simple schemes and billing errors Known fraud and abuse patterns Predictive/Data-Driven Analytics Queries/Rules benefits above AND Complex fraud and abuse patterns Undiscovered fraud New and emerging issues Organized Fraud

9. Case Study 3 – Excitement, Terror & Jubilation in Southern Africa • The Client: A healthcare administrator in Southern Africa • The Engagement:An IFM POV for one of the administrator’s customer Schemes. • The Approach: • Scoring of a 12 month set of the Scheme’s Medical Professional Claims • Review and decisioning of a random set of claims by the administrator’s Forensic (SIU) team. • The Results: • Hundreds of Millions in Identified Savings • Claim scoring “hit rates” from 50% to 90% • The Conclusion: • Proven predictive analytics deliver exciting results • The results are terrifying • Force-multiplying technology that enables rapid suspect review & decisioning renews the jubilation over predictive analytics.

10. The Payment Integrity LandscapeA Range of Approaches “Crawl” Fraud, Waste & Abuse (FWA) are ID’d by chance. A limited program of strategy, process, or people. “Run” FWA are ID’d post-payment via rules-based solutions. There is a formal PI program of strategy, process & people. “Fly” FWA are ID’d pre- and post-payment via rules & predictive analytics. There is a sophisticated PI program of strategy, process & people. “Walk” FWA are ID’d post-payment with Enterprise BI Strategy & process may be formal, or ad hoc. People are otherwise engaged.

11. The Demand for Force-Multiplying Technology • The Problem • High volume, high speed environments of claims, patients and providers • Consistent concerns regarding prompt payment, patient satisfaction and provider retention • Limited Payment Integrity headcount • The Solution • Automated ID of suspicious claims, providers & networks. • Integrated Social Network Analysis • Purpose-built software to enable force-multiplied results. • The Results • 10x increases in Payment Integrity productivity • Automated adjustment to changes in aberrant behavior

12. Case Study 4: Be Careful Out There, People • The Client: A healthcare administrator in Southern Africa • The Engagement: An IFM POV for one of the administrator’s customer Schemes. • The Approach: • Scoring of a 12 month set of the Scheme’s Medical Professional Claims • Review and decisioning of a random set of claims by the administrator’s Forensic (SIU) team. • Among the Results: • A previously known fraudster, who had been sent to prison, was “back at it,” defrauding the scheme • The fraudster had not been sent to prison for fraud, however. • The fraudster had been imprisoned for shooting a witness. • The Conclusions: • We’re doing important work. • Be careful out there, people.

13. Questions?

14. THANK YOU Paul Crowder +1 (720) 257-1459 paulcrowder@fico.com 08 May 2014