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Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection (HANDOUT)

Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection (HANDOUT). Richard A. Derrig Ph. D. OPAL Consulting LLC Visiting Scholar, Wharton School University of Pennsylvania. CAS Predictive Modeling October 4-5, 2004. FRAUD DEFINITION. Principles Clear and willful act

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Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection (HANDOUT)

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  1. Fraud Fighting Actuaries Mathematical Models for Insurance Fraud Detection (HANDOUT) Richard A. Derrig Ph. D. OPAL Consulting LLCVisiting Scholar, Wharton SchoolUniversity of Pennsylvania CAS Predictive Modeling October 4-5, 2004

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

  3. HOW MUCH FRAUD?

  4. AIB FRAUD INDICATORS 1989 Examples • Accident Characteristics (19) • No report by police officer at scene • No witnesses to accident • Claimant Characteristics (11) • Retained an attorney very quickly • Had a history of previous claims • Insured Driver Characteristics (8) • Had a history of previous claims • Gave address as hotel or P.O. Box

  5. AIB FRAUD INDICATORS 1989 Examples • Injury Characteristics (12) • Injury consisted of strain/sprain only • No objective evidence of injury • Treatment Characteristics (9) • Large number of visits to a chiropractor • DC provided 3 or more modalities on most visits • Lost Wages Characteristics (6) • Claimant worked for self or family member • Employer wage differs from claimed wage loss

  6. Fraud Detection Plan • STEP 1:SAMPLE • STEP 2:FEATURES • STEP 3:FEATURE SELECTION • STEP 4:CLUSTER • STEP 5:ASSESSMENT • STEP 6:MODEL • STEP7:STATIC TESTING • STEP 8:DYNAMIC TESTING: Real time operation of acceptable model, record outcomes, repeat steps 1-7 as needed to fine tune model and parameters.

  7. 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

  8. Using Kohonen’s Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud PATRICK L. BROCKETT Gus S. Wortham Chaired Prof. of Risk Management University of Texas at Austin XIAOHUA XIA University of Texas, at Austin RICHARD A. DERRIG Senior Vice President Automobile Insurers Bureau of Massachusetts Vice President of Research Insurance Fraud Bureau of Massachusetts JOURNAL OF RISK AND INSURANCE, 65:2, 245-274, 1998,

  9. Patterns MAPPING: PATTERNS-TO-UNITS

  10. Modeling Hidden Exposures in Claim Severity via the EM Algorithm Grzegorz A. Rempala Department of Mathematics University of Louisville and Richard A. Derrig OPAL Consulting LLC & Wharton School, University of Pennsylvania

  11. Figure 2: EM Fit Left panel: mixture of normal distributions fitted via the EM algorithm to BI data Right panel: Three normal components of the mixture. Source: Modeling Hidden Exposures in Claim Severity via the EM Algorithm, Grzegorz A. Rempala, Richard A. Derrig, pg. 13, 11/18/02

  12. Fraud Classification Using Principal Component Analysis of RIDITs PATRICK L. BROCKETT Gus S. Wortham Chaired Prof. of Risk Management University of Texas at Austin RICHARD A. DERRIG Senior Vice President Automobile Insurers Bureau of Massachusetts Vice President of Research Insurance Fraud Bureau of Massachusetts LINDA L. GOLDEN Marlene & Morton Meyerson Centennial Professor in Business University of Texas Austin, Texas ARNOLD LEVINE Professor Emeritus Department of Mathematics Tulane University New Orleans LA MARK ALPERT Professor of Marketing University of Texas Austin, Texas JOURNAL OF RISK AND INSURANCE, 69:3, SEPT. 2002

  13. The BI Settlement Process and Structure of Negotiated Payments Richard A. Derrig Automobile Insurers Bureau of MA Herbert I. Weisberg Correlation Research Inc. NBER Insurance Group Meeting Cambridge, Massachusetts February 6-7, 2004

  14. Evaluation Variables Prior Tobit Model (1993AY) • Claimed Medicals (+) • Claimed Wages (+) • Fault (+) • Attorney (+18%) • Fracture (+82%) • Serious Visible Injury at Scene (+36%) • Disability Weeks (+10% @ 3 weeks) New Model Additions (1996AY) • Non-Emergency CT/MRI (+31%) • Low Impact Collision (-14%) • Three Claimants in Vehicle (-12%) • Same BI + PIP Co. (-10%) [Passengers -22%]

  15. Negotiation Variables New Model Additions (1996AY) • Atty (1st) Demand Ratio to Specials (+8% @ 6 X Specials) • BI IME No Show (-30%) • BI IME Positive Outcome (-15%) • BI Ten Point Suspicion Score (-12% @ 5.0 Average) • [1993 Build-up Variable (-10%)] • Unknown Disability (+53%) • [93A (Bad Faith) Letter Not Significant] • [In Suit Not Significant] • [SIU Referral (-6%) but Not Significant] • [EUO Not Significant] Note: PIP IME No Show also significantly reduces BI + PIP by discouraging BI claim altogether (-3%).

  16. Total Value of Negotiation Variables

  17. INSURANCE FRAUD RESEARCH REGISTER (IFRR) • Annotated Bibliography of Insurance Fraud Research Worldwide. • Available www.derrig.com or www.ifb.org • 160 Participants • 360 References to Published Research and Working Papers • Join, It’s Free!

  18. REFERENCES Brockett, Patrick L., Derrig, Richard A., Golden, Linda L., Levine, Albert and Alpert, Mark, (2002), Fraud Classification Using Principal Component Analysis of RIDITs, Journal of Risk and Insurance, 69:3, 341-373. Brockett, Patrick L., Xiaohua, Xia and Derrig, Richard A., (1998), Using Kohonen’ Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud, Journal of Risk and Insurance, 65:245-274 Derrig, R.A. and H.I. Weisberg, [2004], Determinants of Total Compensation for Auto Bodily Injury Liability Under No-Fault: Investigation, Negotiation and the Suspicion of Fraud, ”, Insurance and Risk Management, Volume 71, (4), pp. 633-662. Derrig, R.A., H.I. Weisberg and Xiu Chen, [1994], Behavioral Factors and Lotteries Under No-Fault with a Monetary Threshold: A Study of Massachusetts Automobile Claims, Journal of Risk and Insurance, 61:2, 245-275. Derrig, Richard A. and Ostaszewski, Krzysztof M., (1995), Fuzzy Techniques of Pattern Recognition in Risk and Claim Classification, Journal of Risk and Insurance, 62:447-482 Viaene, Stijn, Derrig, Richard A., Baesens, Bart, and Dedene, Guido, (2002), A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Fraud Detection, Journal of Risk and Insurance, 69:3, 373-423.

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