fraud fighting actuaries mathematical models for insurance fraud detection
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Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection . Richard A. Derrig Ph. D. OPAL Consulting LLC Visiting Scholar, Wharton School University of Pennsylvania Daniel Finnegan Quality Planning Corp Innovative Solutions ISO. CAS Predictive Modeling September 19-20, 2005. - PowerPoint PPT PresentationTRANSCRIPT
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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
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ACTUARIAL PROBLEMSACTUARIAL PROBLEMS
WHAT: Product Design WHERE: Market Characteristics WHO: Classification & Sale HOW: Claims Paid WHEN: Forecasting WHY: Profit (Expected)
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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)
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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
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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
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FRAUDFRAUD
The Major QuestionsWhat Is Fraud?How Much Fraud is There?What Companies Do about Fraud?How Can We Identify a Fraudulent Claim?
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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
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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
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OTHER FRAUDOTHER FRAUD MGAs TPAs Primary Insurers Commercial Lines (auto, wc) Claim Fraud Premium Fraud (wc) Auditing Data Availability Data Manipulation Fraud Plans
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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
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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
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HOW MUCH CLAIM FRAUD?HOW MUCH CLAIM FRAUD?
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10%Fraud
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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?
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ALL FRAUDALL FRAUD
What Can Be Done?
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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.
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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
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THE INSURER’S PROBLEMTHE INSURER’S PROBLEM
Self-interested behavior of claimants
Asymmetric information
Attitudes and social norms
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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
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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
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DMDatabases
Scoring Functions
Graded Output
Non-Suspicious ClaimsRoutine Claims
Suspicious ClaimsComplicated Claims
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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
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DATA DATA
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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
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Examples of Fraud DetectionExamples of Fraud Detection
Dan Finnegan