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Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection

Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection. Richard A. Derrig Ph. D. OPAL Consulting LLC Visiting Scholar, Wharton School University of Pennsylvania Daniel Finnegan Quality Planning Corp Innovative Solutions ISO. CAS Predictive Modeling September 19-20, 2005.

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Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection

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  1. Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection Richard A. Derrig Ph. D. OPAL Consulting LLCVisiting Scholar, Wharton SchoolUniversity of PennsylvaniaDaniel FinneganQuality Planning CorpInnovative Solutions ISO CAS Predictive Modeling September 19-20, 2005

  2. ACTUARIAL PROBLEMS • WHAT: Product Design • WHERE: Market Characteristics • WHO: Classification & Sale • HOW: Claims Paid • WHEN: Forecasting • WHY: Profit (Expected)

  3. TRADITIONALMATHEMATICAL TECHNIQUES • Arithmetic (Spreadsheets) • Probability & Statistics (Range of Outcomes) • Curve Fitting (Interpolation & Extrapolation) • Model Building (Equations for Processes) • Valuation (Risk, Investments, Catastrophes) • Numerical Method (Analytic Solution Rare)

  4. NON-TRADITIONAL MATHEMATICS • Fuzzy Sets & Fuzzy Logic • Elements: “in/out/partially both” • Logic: “true/false/maybe” • Decisions: “incompatible criteria” • Artificial Intelligence: “data mining” • Neural Networks: “learning algorithms” • Classification and Regression Trees

  5. CLASSIFICATION • Segmentation: A major exercise for insurance underwriting and claims • Underwriting: Find profitable risks from among the available market • Claims: Sort claims into easy pay and claims needing investigation

  6. FRAUD • The Major Questions • What Is Fraud? • How Much Fraud is There? • What Companies Do about Fraud? • How Can We Identify a Fraudulent Claim?

  7. FRAUD DEFINITION Principles • Clear and willful act • Proscribed by law • Obtaining money or value • Under false pretenses Abuse/ Ethical Lapse: Fails one or more Principles

  8. FRAUD TYPES • Insurer Fraud • Fraudulent Company • Fraudulent Management • Agent Fraud • No Policy • False Premium • Company Fraud • Embezzlement • Inside/Outside Arrangements • Rating and Claim Fraud • Policyholder/Claimant/Insured • Providers/Rings

  9. OTHER FRAUD • MGAs • TPAs • Primary Insurers • Commercial Lines (auto, wc) • Claim Fraud • Premium Fraud (wc) • Auditing • Data Availability • Data Manipulation • Fraud Plans

  10. TYPES OF CLAIM FRAUD AUTO Bodily Injury -Staged Accidents -Actual Accidents/Faked Injuries -Jump-Ins -Provider Abuse / False Billing Vehicle Damage -Staged Thefts -Chop Shops -Body Shop Fraud -Adjuster Fraud

  11. TYPES OF CLAIM FRAUD WORKERS’ COMPENSATION Employee Fraud -Working While Collecting -Staged Accidents -Prior or Non-Work Injuries Employer Fraud -Misclassification of Employees -Understating Payroll -Employee Leasing -Re-Incorporation to Avoid Mod

  12. HOW MUCH CLAIM FRAUD?

  13. 10% Fraud

  14. HOW MUCH FRAUD?

  15. ALL FRAUD • What Can Be Done?

  16. WHAT COMPANIES DO ABOUT FRAUD • InvestigateInvestigation reduces BI Claim payments by 18 percent. Additional investigation not cost-effective. Better claim selection may be cost-effective. • Negotiate Negotiation reduces BI claim payments on build-up claims by 22 percentcompared to valid claims with same medicals, injuries, etc. • LitigateLitigation of bogus claims results in high number of company verdicts. When effective, claim withdrawals and closed-no-pay increase.

  17. THEORY OF CLAIM FRAUD • Utility Maximization UTL (Fraud v. No Fraud) • Asymmetric Information Inf (Claimant/Provider v. Insurer) • Welfare Loss WFL (Detection $ v. Fraud $) _________________________________ • All Rely on Detection Probabilities

  18. THE INSURER’S PROBLEM • Self-interested behavior of claimants • Asymmetric information • Attitudes and social norms

  19. FRAUD AND ABUSE THE TOP TEN DEFENSES • 1. Adjusters • 2. Computer Technology • 3. Criminal Investigators • 4. Data and Information • 5. Experts • 6. Judges • 7. Lawyers • 8. Legislators • 9. Prosecutors • 10. Special Investigators

  20. REAL CLAIM FRAUDDETECTION PROBLEM • Classify all claims • Identify valid classes • Pay the claim • No hassle • Visa Example • Identify (possible) fraud • Investigation needed • Identify “gray” classes • Minimize with “learning” algorithms

  21. DATA

  22. POTENTIAL VALUE OF AN ARTIFICIAL INTELLIGENCE SCORING SYSTEM • Screening to Detect Fraud Early • Auditing of Closed Claims to Measure Fraud • Sorting to Select Efficiently among Special Investigative Unit Referrals • Providing Evidence to Support a Denial • Protecting against Bad-Faith

  23. Examples of Fraud Detection • Dan Finnegan

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