Transcript PCI 2007 WC Fraud Detection

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
 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
PRINCIPLES
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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?)
10%
Fraud
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
 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
DATA
Computers advance
FRAUD IDENTIFICATION
 Experience and Judgment
 Artificial Intelligence Systems
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Regression & Tree Models
Fuzzy Clusters
Neural Networks
Expert Systems
Genetic Algorithms
All of the Above
DM
Databases
Scoring Functions
Graded Output
Non-Suspicious Claims
Routine Claims
Suspicious Claims
Complicated Claims
DM
Databases
Scoring Functions
Graded Output
Non-Suspicious Risks
Routine Underwriting
Suspicious Risks
Non-Routine Underwriting
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
CRIMINAL FRAUD?
(Massachusetts)
MASS INSURANCE FRAUD BUREAU
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PRIVATE INVESTIGATIVE AGENCY,
WORKING WITH AG, DAs, DIA
FORMED IN MAY 1991 BY STATUTE,
STRENGTHENED FOR WC IN
DECEMBER 1991
RECEIVES REFERRALS FROM
INSURANCE COS., EMPLOYERS, THE
PUBLIC, AND LAW ENFORCEMENT
ALL LINES OF INSURANCE
BUDGET FUNDED BY AUTO AND WC
INS. COS.
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 1990-2000
 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
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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
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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
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”
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 Massachusetts
Results 2005-2006
Case Referred to Prosecution
Total Individuals Charged
2005
70
155
2006
117
248
 Total Cases referred to Pros.244
 Total Individuals Charged
528
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?
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
 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
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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
 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
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
Estimated Premium
Audited /Adjusted Premium
WORKERS’ COMPENSATION
PREMIUM TERMINOLOGY
Payroll - All Compensation
Classification Rate - Based on Type of
Job (Risk of Injury)
Mod - Multiplier Based on Claims
History
WORKERS’ COMPENSATION
PREMIUM FORMULA
Payroll x Classification Code x
Experience Mod
TYPES OF PREMIUM FRAUD
Payroll Misrepresentation
Classification Misrepresentation
Modification Avoidance
WORKERS' COMP COVERAGE FOR HIGH RISK CLASSIFICATIONS
DIGGEM/BEREEYEM/BILDEM STEEL ERECTORS
1986
1987
1988
1989
1990
1991
1992
1993
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1.0 MOD
1.43 MOD
DIGGEM
1/3
12/19
BANKRUPTCY (OWES PREMIUM)
1.0 MOD
1.27 MOD
BEREEYEM
12/29
9/29
CANCELLED
1.8 MOD
BILDEM
9/24
HIGH PREMIUM CLASSIFICATIONS DELETED FROM COVERAGE
Case Study – Lanco Scaffolding
Lanco Representations
 Small scaffolding operation
 Limited accounting records
 Outside accountant prepared and
possessed tax records
 Premium of $28,000
Lanco
Scaffolding, Inc.
Lanco Scaffolding Payroll Scheme
Financial Impact
$1,984,000.00
$1,660,000.00
$2,000,000.00
$1,800,000.00
$1,600,000.00
$1,400,000.00
$1,200,000.00
$831,000.00
$1,000,000.00
$800,000.00
$600,000.00
$294,500.00
$400,000.00
$200,000.00
$0.00
WC
Labor Union
Carpenter's
Union
IRS
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
Audit Reports
-Work Papers
-Supporting Documents from Insured
Claim/Loss Runs
Underwriting Documents
-Agent
-Insured
Loss Prevention Reports
BAD AUDIT
**ACME INSURANCE COMPANY**
AUDIT FOR POLICY #12345678
Effective date: 4/1/04
Employees: (?)
NAME?
SSN?
CLASS CODE
SALARY
8227
$55,899.00
8742
$107,939.00
8810
$76,014.00
9403
$102,956.00
GOOD AUDIT
**ACME INSURANCE COMPANY**
AUDIT FOR POLICY #12345678 INSURED: DD Waste Haulers
Effective date: 4/1/04
CLASS CODE
NAME
Auditor: J. Martini
SSN
SALARY-1993
8227
Joseph Kennedy
015-73-2521
$29,012.00
8742
Joe Phelan
034-54-7861
$28,447.00
8742
8810
Matthew Franks
Roberta Martines
022-43-6677
025-48-3465
$39,218.00
$21,554.00
8810
Theodore Daniels
038-64-7344
$27,995.00
9403
9403
9403
Richard Collins
Steve Cane
Paul Young
547-88-3195
522-94-5985
012-66-4935
$41,887.00
$26,558.00
$34,511.00
SIU INVOLVEMENT
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What is the Issue?
Referrals can be Optimized
Review Company Files
Surveillance
Interview Agent
Interview Insured
Interact with Fraud Bureau
REFERENCES
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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.