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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

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


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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.


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Fraud Definition

PRINCIPLES

  • Clear and willful act

  • Proscribed by law

  • Obtaining money or value

  • Under false pretenses

    Abuse: Fails one or more Principles


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HOW MUCH CLAIM FRAUD? (CRIMINAL or CIVIL?)


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10%

Fraud


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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


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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




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FRAUD IDENTIFICATION

  • Experience and Judgment

  • Artificial Intelligence Systems

    • Regression & Tree Models

    • Fuzzy Clusters

    • Neural Networks

    • Expert Systems

    • Genetic Algorithms

    • All of the Above


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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



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CRIMINAL FRAUD? (Massachusetts)


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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.


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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


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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


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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%


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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!


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Criminal Fraud Deterrence

  • General Deterrence – Mixed results

  • Specific Deterrence – Good Results

  • Big Deterrence – There is nothing comparable to the “Lawrence Deterrent”


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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


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Insurance Fraud Bureau of Massachusetts

Results 2005-2006

  • Total Cases referred to Pros. 244

  • Total Individuals Charged 528


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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



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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)


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FRAUD INDICATORS Fraud DeterrenceVALIDATION 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?


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Underwriting Red Flags Fraud Deterrence

  • 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


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Claims Red Flags Fraud Deterrence

  • 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


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Auditing Red Flags Fraud Deterrence

  • 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


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POLICY Fraud Deterrence

Estimated Premium

Audited /Adjusted Premium


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WORKERS’ COMPENSATION PREMIUM TERMINOLOGY Fraud Deterrence

  • Payroll - All Compensation

  • Classification Rate - Based on Type of Job (Risk of Injury)

  • Mod - Multiplier Based on Claims History


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WORKERS’ COMPENSATION PREMIUM FORMULA Fraud Deterrence

  • Payroll x Classification Code x Experience Mod


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TYPES OF PREMIUM FRAUD Fraud Deterrence

  • Payroll Misrepresentation

  • Classification Misrepresentation

  • Modification Avoidance


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Case Study – Lanco Scaffolding Fraud Deterrence

Lanco Representations

  • Small scaffolding operation

  • Limited accounting records

  • Outside accountant prepared and possessed tax records

  • Premium of $28,000


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Lanco Scaffolding, Inc. Fraud Deterrence


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AUDIT PROCESS Fraud Deterrence

  • 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)


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INSURANCE RECORDS Fraud Deterrence

  • Audit Reports

    -Work Papers

    -Supporting Documents from Insured

  • Claim/Loss Runs

  • Underwriting Documents

    -Agent

    -Insured

  • Loss Prevention Reports


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**ACME INSURANCE COMPANY** Fraud Deterrence

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


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**ACME INSURANCE COMPANY** Fraud Deterrence

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


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SIU INVOLVEMENT Fraud Deterrence

  • What is the Issue?

  • Referrals can be Optimized

  • Review Company Files

  • Surveillance

  • Interview Agent

  • Interview Insured

  • Interact with Fraud Bureau


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REFERENCES Fraud Deterrence

  • 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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