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Fraud Prevention

Fraud Prevention. Data Analytics and other Methodologies. Paul Crowder, FICO. FICO Snapshot. Payment Integrity A Range of Approaches. “Crawl” Fraud, Waste & Abuse (FWA) are ID’d by chance. A limited program of strategy, process, or people. “Run”

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Fraud Prevention

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  1. Fraud Prevention Data Analytics and other Methodologies Paul Crowder, FICO

  2. FICO Snapshot

  3. Payment IntegrityA 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 predictive analytics. 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.

  4. FICO Client Case Study 1Provider Abuse + Systemic Weakness • The Scheme discovered that it was paying all of their expense procedure codes in conflict with the Scheme’s established policy. • Further, the Provider is discovered to be acting with intent. • The US$259 high scoring claim became a US$1 million case with part-time effort by one investigator over a 2 week period … 100:1 ROI. A single claim line (CPT 99070, Supplies and Materials), approved for US$259 payment, scored high for Procedure Rate (unusually rapid repetition over time). On review, this claim line should have been automatically denied during auto-adjudication, as it was for an expense that is considered to be part of Provider overhead expenses.

  5. The Power of Data-Driven Predictive Analytics 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

  6. FICO Client Case Study 2Clinically Unnecessary Care A dentistry provider scored high (aberrancy) for 5 reasons, including “High Member Day” • Peers averaged US$195 per member per day • The suspect averaged over US$1,700 per member per day • Findings • Stainless Steel Crowns were routinely installed on every tooth of every child treated by this provider. • Clinically unnecessary in every case. • Multiple provider identities • 3 years of abuse. • Result • A US$3 million case • Imprisonment of the provider

  7. Preventive Measures • Credential (review) providers before you admit them to your scheme. • Employ a strong claims adjudication system. • Score claims for aberrancy, post adjudication/pre-payment, manually review high scoring (suspicious) claims, & don’t pay the claims that you shouldn’t pay. • Use integrated “force multiplier” technologiessuch as decision management software, Link Analysis & Investigational Case Management to rapidly review & decision findings, ID broader suspicious patterns & build prosecution-ready case documentation. • Use pre- and post-payment findings to strengthen your claims adjudication results, & take action on identified systemic weaknesses and policy gaps. • Application of (new) preventive measures is a change in process … success depends upon the strength of your relationships with your internal & external peers, customers, members & stakeholders.

  8. FICO Client Case Study 3Mathematics are the Universal Language • The Client: A Dutch Dentistry Scheme • 1.2 million beneficiaries • €101 million paid Dental claims per year • The Engagement: A for-fee FICO IFM Analytic Assessment • FICO scoring of 3,200 Providers using 12 months of paid claims data. • FICO Delivery of results that are “blind,” due to a language barrier between FICO’s analytic scientists & the client’s data. • The Results: • 106 (3%) aberrant Providers. In the top 30, ID of all 12 known fraudsters & 12 new suspects. • €$15 Million Savings from review of 20 high scoring dentists - 14 were found fraudulent (70% Hit Rate). • 250% ROI projected for Year 1 of the predictive analytics solution. • The Conclusion: Mathematics is the universal language.

  9. The Recommended Approach for the Medical Schemes • Rules that target known types of fraud and abuse • Example: Claim System Edits • Unsupervised models that score paid claims and providers to detect known, unknown and emerging problems pre- and post-payment • Vigorous pre- and post-pay workflow for scoring/detection, review and investigation. • Integrated software that maximizes efficiency and that makes analytic results actionable is key. • Strong relationships with internal and external peers, customers, members and stakeholders.

  10. FICO Client Case Study 3Providers Have Bills to Pay Too • The Client: A commercial Medical Aid Scheme who scores claims daily, post-adjudication, but reviews claims “quick post-payment.” • The Initial Claim Scoring Result: A claim line which ordinarily pays at US$250 scores high for High Paid Claim (an unusually high amount paid for the procedure) … at US$25,000 paid. • The Initial Explanation: “A clerical error.” • On Further Review: Submittal and payment for the same US$25,000 procedure, one time in each of the previous 2 years. • The Finding: The provider was manipulating the Scheme’s auto-adjudication system, one time each year, for payment of his child’s university tuition bill.

  11. The Rise of Auto-Adjudication • Who processes their claims manually? • If not now, soon: Nobody • Advantages of Auto-Adjudication • For the Providers: Quick Payment • For the Members: Limited level of involvement • For the Medical Aid Scheme, payment of: • The correct claims • For the correct amount • For your members • For providers who are authorized to participate in your plan. • The Problem with Auto-Adjudication: You will pay claims that, by policy, contract or design, should not be paid.

  12. FICO Client Case Study 4Technology: Good, Technology: Bad • The Client: A commercial Medical Aid Scheme who successfully promoted the use of “Baxter” machines for drug dispensing • More accurate dispensing for chronic conditions • Lower dispensing fees for use of automated dispensing • The Initial Claim Scoring Result: Numerous pharmacy claims scoring high for Duplicate Class, Rate and Excess Days • The Findings: • Unnecessary normal dispense on the same day as the first Baxter fill. • Normal + Baxter dispense to mask excessive dispensing of narcotics • Excess supplies not prevented with use of Baxter • Claiming for normal dispense of drugs that should have been (or were) dispensed with Baxter • The Results: €1.4 million in annual savings via clarification of policy & policing of abusive providers.

  13. Questions?

  14. THANK YOU Paul CrowderPre Sales Consulting, FICO paulcrowder@fico.com

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