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Fraud Detection and Deterrence in Workers’ Compensation

Fraud Detection and Deterrence in Workers’ Compensation. Richard A. Derrig, PhD, CFE President Opal Consulting, LLC Visiting Scholar, Wharton School, University of Pennsylvania. PCIA Joint Marketing and Underwriting Seminar March 18-20, 2007. Insurance Fraud- The Problem.

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Fraud Detection and Deterrence in Workers’ Compensation

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  1. Fraud Detection and Deterrence in Workers’ Compensation Richard A. Derrig, PhD, CFE President Opal Consulting, LLC Visiting Scholar, Wharton School, University of Pennsylvania PCIA Joint Marketing and Underwriting Seminar March 18-20, 2007

  2. Insurance Fraud- The Problem • ISO/IRC 2001 Study: Auto and Workers Compensation Fraud a Big Problem by 27% of Insurers. • CAIF: Estimation (too large) • Mass IFB: 1,500 referrals annually for Auto, WC, and (10%) Other P-L.

  3. Fraud Definition PRINCIPLES • Clear and willful act • Proscribed by law • Obtaining money or value • Under false pretenses Abuse: Fails one or more Principles

  4. HOW MUCH CLAIM FRAUD? (CRIMINAL or CIVIL?)

  5. 10% Fraud

  6. REAL PROBLEM-CLAIM FRAUD • 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

  7. Company Automation - Data Mining • Data Mining/Predictive Modeling Automates Record Reviews • No Data Mining without Good Clean Data (90% of the solution) • Insurance Policy and Claim Data; Business and Demographic Data • Data Warehouse/Data Mart • Data Manipulation – Simple First; Complex Algorithms When Needed

  8. DATA

  9. Computers advance

  10. FRAUD IDENTIFICATION • Experience and Judgment • Artificial Intelligence Systems • Regression & Tree Models • Fuzzy Clusters • Neural Networks • Expert Systems • Genetic Algorithms • All of the Above

  11. 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

  12. Implementation Outline Included at End

  13. CRIMINAL FRAUD? (Massachusetts)

  14. Prosecution Study Mass. IFB Data 1990-2000 • 17,274 Referrals; 59% auto, 31% wc, 35% accepted for investigation. • 3,349 Cases, i.e. one or more related accepted referrals. • 552 Cases were referred for prosecution;293 cases had prosecution completed.

  15. Prosecution Study Mass. IFB Data 1990-2000 • Case Outcomes: No Prosecution (CNP) Prosecution Denied (PD), Prosecution Completed (PC) • Auto Cases: 1,156 CNP,50 PD,121PC • WC Claim: 524 CNP,40 PD, 82PC • WC Premium: 70 CNP, 9 PD, 34PC

  16. Subjects Prosecuted • 543 subjects were prosecuted • 399 were claimants/insureds • 65 were insureds only • 46 were professionals associated with the insurance system as company personnel or service providers

  17. Prosecution Findings • Guilty or Equivalent – 84% • Pled Guilty – 55% • Continued without a Finding – 19% • Not Guilty – 8% • Not Disposed (Fled) – 3% • Other (e.g. filed) – 5%

  18. Fraudsters • Prior Convictions – 51% • Prior Property Conviction – 9.6% • Subsequent Offenses – 29% + • Subsequent Offense Prior to End of Fraud Sentence – 19% + • Conclusion: These are general purpose criminals not career insurance fraudsters!

  19. Criminal Fraud Deterrence • General Deterrence – Mixed results • Specific Deterrence – Good Results • Big Deterrence – There is nothing comparable to the “Lawrence Deterrent”

  20. Insurance Fraud Bureau of Massachusetts • 2003 Lawrence Staged Accident Results In Death • IFB Joined w/Lawrence P.D and Essex County DA’s Office to form 1st Task Force

  21. Insurance Fraud Bureau of Massachusetts Results 2005-2006 • Total Cases referred to Pros. 244 • Total Individuals Charged 528

  22. TYPES OF 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

  23. NON-CRIMINAL FRAUD?

  24. NON-Criminal Fraud Deterrence Workers Compensation • General Deterrence – DIA, Med, Att Government Oversight • Specific Deterrence – Company Auditor, Data, Predictive Modeling, Employer Incentives (Mod, Schd Rate) • Big Deterrence – None, Little Study, NY Fiscal Policy Institute (2007) CA SIU Regulations (2006)

  25. FRAUD INDICATORSVALIDATION PROCEDURES • Canadian Coalition Against Insurance Fraud (1997) 305 Fraud Indicators (45 vehicle theft) • “No one indicator by itself is necessarily suspicious”. • Problem: How to validate the systematic use of Fraud Indicators?

  26. Underwriting Red Flags • Prior Claims History (Mod) • High Mod versus Low Premium • Increases/Decreases in Payroll • Changes of Operation • Loss Prevention Visits • Preliminary Physical Audits • Check Yellow Pages • Check Websites

  27. Claims Red Flags • Description of Accident vs. Underwriting Description of Operation • Description of Employment • Length of Services/Supervisor • Pay • Kind of Work • Copies of Payroll Checks • Claims vs. Payroll

  28. Auditing Red Flags • Be Aware of Prepared Documents • Check Original Files • Check Loss Reports • Check Class Distribution • Estimated Payroll Compared to Audited Payroll • Prior Claims • Changes of Operations

  29. POLICY Estimated Premium Audited /Adjusted Premium

  30. WORKERS’ COMPENSATION PREMIUM TERMINOLOGY • Payroll - All Compensation • Classification Rate - Based on Type of Job (Risk of Injury) • Mod - Multiplier Based on Claims History

  31. WORKERS’ COMPENSATION PREMIUM FORMULA • Payroll x Classification Code x Experience Mod

  32. TYPES OF PREMIUM FRAUD • Payroll Misrepresentation • Classification Misrepresentation • Modification Avoidance

  33. Case Study – Lanco Scaffolding Lanco Representations • Small scaffolding operation • Limited accounting records • Outside accountant prepared and possessed tax records • Premium of $28,000

  34. Lanco Scaffolding, Inc.

  35. AUDIT PROCESS • Auditor spends 2-3 hours on site, reviewing records provided by the insured (payroll, tax records, jobs) • Auditor compares these with insurance records (claims history, prior audits, loss prevention reports)

  36. INSURANCE RECORDS • Audit Reports -Work Papers -Supporting Documents from Insured • Claim/Loss Runs • Underwriting Documents -Agent -Insured • Loss Prevention Reports

  37. **ACME INSURANCE COMPANY** AUDIT FOR POLICY #12345678 Effective date: 4/1/04 Employees: (?) SALARY CLASS CODE NAME? SSN? 8227 $55,899.00 8742 $107,939.00 8810 $76,014.00 9403 $102,956.00 BAD AUDIT

  38. **ACME INSURANCE COMPANY** AUDIT FOR POLICY #12345678 INSURED: DD Waste Haulers Effective date: 4/1/04 Auditor: J. Martini CLASS CODE NAME SSN SALARY-1993 8227 Joseph Kennedy 015-73-2521 $29,012.00 8742 Joe Phelan 034-54-7861 $28,447.00 8742 Matthew Franks 022-43-6677 $39,218.00 8810 Roberta Martines 025-48-3465 $21,554.00 8810 Theodore Daniels 038-64-7344 $27,995.00 9403 Richard Collins 547-88-3195 $41,887.00 9403 Steve Cane 522-94-5985 $26,558.00 9403 Paul Young 012-66-4935 $34,511.00 GOOD AUDIT

  39. SIU INVOLVEMENT • What is the Issue? • Referrals can be Optimized • Review Company Files • Surveillance • Interview Agent • Interview Insured • Interact with Fraud Bureau

  40. REFERENCES • Canadian Coalition Against Insurance Fraud, (1997) Red Flags for Detecting Insurance Fraud, 1-33. • Derrig, Richard A. and Krauss, Laura K., (1994), First Steps to Fight Workers' Compensation Fraud, Journal of Insurance Regulation, 12:390-415. • Derrig, Richard A., Johnston, Daniel J. and Sprinkel, Elizabeth A., (2006), Risk Management & Insurance Review, 9:2, 109–130. • Derrig, Richard A., (2002), Insurance Fraud, Journal of Risk and Insurance, 69:3, 271-289. • Derrig, Richard A., and Zicko, Valerie, (2002), Prosecuting Insurance Fraud – A Case Study of the Massachusetts Experience in the 1990s, Risk Management and Insurance Review, 5:2, 7-104 • Francis, Louise and Derrig, Richard A., (2006) Distinguishing the Forest from the TREES: A Comparison of Tree Based Data Mining Methods, Casualty Actuarial Forum, Winter, pp.1-49. • Johnston, Daniel J., (1997) Combating Fraud: Handcuffing Fraud Impacts Benefits, Assurances, 65:2, 175-185. • Rempala, G.A., and Derrig, Richard A., (2003), Modeling Hidden Exposures in Claim Severity via the EM Algorithm, North American Actuarial Journal, 9(2), pp.108-128.

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