Richard A. Derrig Ph. D.Richard A. Derrig Ph. D. OPAL Consulting LLC OPAL Consulting LLC

Visiting Scholar, Wharton SchoolVisiting Scholar, Wharton SchoolUniversity of PennsylvaniaUniversity of Pennsylvania

Daniel FinneganDaniel FinneganQuality Planning CorpQuality Planning Corp

Innovative Solutions ISOInnovative Solutions ISO

Fraud Fighting ActuariesMathematical Models for

Insurance Fraud Detection

CAS Predictive ModelingSeptember 19-20, 2005

ACTUARIAL PROBLEMSACTUARIAL PROBLEMS

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

TRADITIONALTRADITIONALMATHEMATICAL TECHNIQUESMATHEMATICAL 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)

NON-TRADITIONAL NON-TRADITIONAL MATHEMATICSMATHEMATICS

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

CLASSIFICATIONCLASSIFICATION

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

FRAUDFRAUD

The Major QuestionsWhat Is Fraud?How Much Fraud is There?What Companies Do about Fraud?How Can We Identify a Fraudulent Claim?

FRAUD DEFINITIONFRAUD DEFINITION

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

FRAUD TYPESFRAUD 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

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

TYPES OF CLAIM FRAUDTYPES OF CLAIM FRAUDAUTO

Bodily Injury -Staged Accidents -Actual Accidents/Faked Injuries -Jump-Ins -Provider Abuse / False BillingVehicle Damage -Staged Thefts -Chop Shops -Body Shop Fraud -Adjuster Fraud

TYPES OF CLAIM FRAUDTYPES OF CLAIM FRAUDWORKERS’ COMPENSATION

Employee Fraud -Working While Collecting -Staged Accidents -Prior or Non-Work InjuriesEmployer Fraud -Misclassification of Employees -Understating Payroll -Employee Leasing

-Re-Incorporation to Avoid Mod

HOW MUCH CLAIM FRAUD?HOW MUCH CLAIM FRAUD?

10%Fraud

Table 11989 Bodily Injury Liability Claim Sample

"Fraud Definition" Approximate ClaimCount Percentage

1. Apparent Fraud or Build-up 43.80% 2. Apparent Fraud Only 9.10% 3. Apparent Fraud Referable for

Criminal Investigation1.00%

4. IFB Referrals Qualifying forActive Investigation

0.50%

5. IFB Investigations Referableto Prosecution

0.10%

6. Prosecution Successes 0.09% Source: AIB Studies of 1989 BI Claims; RAD estimates of IFB Data

HOW MUCH FRAUD?

ALL FRAUDALL FRAUD

What Can Be Done?

WHAT COMPANIES DO ABOUT FRAUDWHAT COMPANIES DO ABOUT FRAUD Investigate

Investigation reduces BI Claim payments by 18 percent. Additional investigation not cost-effective. Better claim selection may be cost-effective.

NegotiateNegotiation reduces BI claim payments on build-up claims by 22 percent compared 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.

THEORY OF CLAIM FRAUDTHEORY OF CLAIM FRAUD

Utility Maximization UTL (Fraud v. No Fraud)

Asymmetric Information Inf (Claimant/Provider v. Insurer)

Welfare LossWFL (Detection $ v. Fraud $)

_________________________________ All Rely on Detection Probabilities

THE INSURER’S PROBLEMTHE INSURER’S PROBLEM

Self-interested behavior of claimants

Asymmetric information

Attitudes and social norms

FRAUD AND ABUSE FRAUD AND ABUSE THE TOP TEN DEFENSESTHE 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

REAL CLAIM FRAUDREAL CLAIM FRAUDDETECTION PROBLEMDETECTION 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

DMDatabases

Scoring Functions

Graded Output

Non-Suspicious ClaimsRoutine Claims

Suspicious ClaimsComplicated Claims

Settlement Ratios by Injury and SuspicionVariable PIP Suspicion Score

= Low (0-3)PIP Suspicion Score = Mod to High (4-10)

PIP Suspicion Score = All

1996 (N-336) 1996 (N-216) 1996 (N-552)

Str/SP All Other Str/SP All Other Str/SP All Other

Settlement Settlement Settlement

81% 19% 94% 6% 86% 14%

Avg. Settlement/ Specials Ratio 3.01 3.81 2.58 3.61 2.82 3.77

Median Settlement/ Specials Ratio 2.69 2.89 2.40 2.57 2.55 2.89

DATA DATA

POTENTIAL VALUE OF AN ARTIFICIAL POTENTIAL VALUE OF AN ARTIFICIAL INTELLIGENCE SCORING SYSTEMINTELLIGENCE 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

Examples of Fraud DetectionExamples of Fraud Detection

Dan Finnegan