Fraud detection and deterrence in workers compensation l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 46

Fraud Detection and Deterrence in Workers’ Compensation PowerPoint PPT Presentation


  • 511 Views
  • Updated On :
  • Presentation posted in: General

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.

Download Presentation

Fraud Detection and Deterrence in Workers’ Compensation

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Fraud detection and deterrence in workers compensation l.jpg

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 l.jpg

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.


Fraud definition l.jpg

Fraud Definition

PRINCIPLES

  • Clear and willful act

  • Proscribed by law

  • Obtaining money or value

  • Under false pretenses

    Abuse: Fails one or more Principles


How much claim fraud criminal or civil l.jpg

HOW MUCH CLAIM FRAUD? (CRIMINAL or CIVIL?)


Slide5 l.jpg

10%

Fraud


Real problem claim fraud l.jpg

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


Company automation data mining l.jpg

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


Slide8 l.jpg

DATA


Computers advance l.jpg

Computers advance


Slide10 l.jpg

FRAUD IDENTIFICATION

  • Experience and Judgment

  • Artificial Intelligence Systems

    • Regression & Tree Models

    • Fuzzy Clusters

    • Neural Networks

    • Expert Systems

    • Genetic Algorithms

    • All of the Above


Potential value of an artificial intelligence scoring system l.jpg

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


Implementation outline included at end l.jpg

Implementation Outline Included at End


Criminal fraud massachusetts l.jpg

CRIMINAL FRAUD? (Massachusetts)


Prosecution study mass ifb data 1990 2000 l.jpg

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.


Prosecution study mass ifb data 1990 200018 l.jpg

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


Subjects prosecuted l.jpg

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


Prosecution findings l.jpg

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%


Fraudsters l.jpg

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!


Criminal fraud deterrence l.jpg

Criminal Fraud Deterrence

  • General Deterrence – Mixed results

  • Specific Deterrence – Good Results

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


Insurance fraud bureau of massachusetts l.jpg

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


Insurance fraud bureau of massachusetts24 l.jpg

Insurance Fraud Bureau of Massachusetts

Results 2005-2006

  • Total Cases referred to Pros.244

  • Total Individuals Charged528


Types of fraud l.jpg

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


Non criminal fraud l.jpg

NON-CRIMINAL FRAUD?


Non criminal fraud deterrence workers compensation l.jpg

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)


Fraud indicators validation procedures l.jpg

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?


Underwriting red flags l.jpg

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


Claims red flags l.jpg

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


Auditing red flags l.jpg

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


Slide32 l.jpg

POLICY

Estimated Premium

Audited /Adjusted Premium


Slide33 l.jpg

WORKERS’ COMPENSATION PREMIUM TERMINOLOGY

  • Payroll - All Compensation

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

  • Mod - Multiplier Based on Claims History


Slide34 l.jpg

WORKERS’ COMPENSATION PREMIUM FORMULA

  • Payroll x Classification Code x Experience Mod


Types of premium fraud l.jpg

TYPES OF PREMIUM FRAUD

  • Payroll Misrepresentation

  • Classification Misrepresentation

  • Modification Avoidance


Case study lanco scaffolding l.jpg

Case Study – Lanco Scaffolding

Lanco Representations

  • Small scaffolding operation

  • Limited accounting records

  • Outside accountant prepared and possessed tax records

  • Premium of $28,000


Slide38 l.jpg

Lanco Scaffolding, Inc.


Slide41 l.jpg

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)


Insurance records l.jpg

INSURANCE RECORDS

  • Audit Reports

    -Work Papers

    -Supporting Documents from Insured

  • Claim/Loss Runs

  • Underwriting Documents

    -Agent

    -Insured

  • Loss Prevention Reports


Slide43 l.jpg

**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


Good audit l.jpg

**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


Siu involvement l.jpg

SIU INVOLVEMENT

  • What is the Issue?

  • Referrals can be Optimized

  • Review Company Files

  • Surveillance

  • Interview Agent

  • Interview Insured

  • Interact with Fraud Bureau


References l.jpg

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.


  • Login