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A Statistical Analysis of The Auto BI Settlement Process and Structure of Negotiated Payments in The Presence of Fraud

A Statistical Analysis of The Auto BI Settlement Process and Structure of Negotiated Payments in The Presence of Fraud and Buildup. Richard A. Derrig, President, OPAL Consulting LLC Visiting Scholar, Wharton School University of Pennsylvania Grezgorz A. Rempala

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A Statistical Analysis of The Auto BI Settlement Process and Structure of Negotiated Payments in The Presence of Fraud

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  1. A Statistical Analysis of The Auto BI Settlement Process and Structure of Negotiated Payments in The Presence of Fraud and Buildup Richard A. Derrig, President, OPAL Consulting LLCVisiting Scholar, Wharton School University of Pennsylvania Grezgorz A. Rempala Associate Professor, Statistics University of Louisville 2005 WRIEC Salt Lake City, Utah, USA August 8-11, 2005

  2. NEGOTIATION • Liability claims are negotiated not “paid by the insurer”. • First party claims have payment regulations both good (Cooperation) and bad (Time Frames for Payment) re fraud. • Negotiation subject only to bad faith and unfair claim practice regulations • Two-person game: Adjusters and Claimant/Attorneys, but not suitable for game theory model. • Example in paper is Auto Bodily Injury Liability – Mass Data

  3. Table 1

  4. Table 2

  5. NEGOTIATION • Claim Payment Components • Demands and Offers • Time Frames for Rounds • Anchoring and Adjusting • Offer/Demand Ratios • Settlements • Mass BI Data for 1996 AY • Statistical Modeling

  6. Table 3 - 1 1 1996 data set - includes "Unknown Disability" claims and claims with a 1st Demand Amount 2 1996 data set - excludes 64 "Unknown Disability" claims

  7. NEGOTIATION • Claim Payment Components • Demands and Offers • Time Frames for Rounds • Anchoring and Adjusting • Offer/Demand Ratios • Settlements • Mass BI Data for 1996 AY • Statistical Modeling

  8. Table 4

  9. NEGOTIATION • Claim Payment Components • Demands and Offers • Time Frames for Rounds • Anchoring and Adjusting • Offer/Demand Ratios • Settlements • Mass BI Data for 1996 AY • Statistical Modeling

  10. Table 6

  11. Offer Demand Ratios (Sorted by Descending Losses) – Figure1

  12. Offer Demand Ratios (Sorted by Descending 1st Demands) – Figure 2

  13. Table 7

  14. Figure 1: The Massachusetts Negotiation Data Estimated standardized rates of the NHPP of arrival of O/D for 2-, 3- and 4-negotiation rounds.

  15. Table 10

  16. Figure 2: Empirical rates for ‘slow’ and ‘fast negotiations’ (solid lines) along with the rates estimated on the basis of the logistic regression classifier (dashed lines) for the subset of 58 negotiations histories from the Massachusetts dataset

  17. Figure 3:95% confidence tunnel for both ‘slow’ and ‘fast’ fitted rates for the subset of 58 negotiations histories from the Massachusetts dataset

  18. Offer / Demand Ratios (Sorted by Descending Pre-Settlement Ratio) – Figure 3

  19. NEGOTIATIONFuture Modeling Work Demands and Offers • Role of Time Frames • Role of Covariates (Injury, etc) • Anchoring and Adjusting • Offer/Demand Ratios • Settlements • Statistical Models • Mass BI Data for 1996 AY • Another Data Set Needed

  20. References • Cooter, Robert D. and Daniel L. Rubinfeld, (1989), Economic Analysis of Legal Disputes and Their Resolution, Journal of Economic Literature, 27, 1067-1097 • Derrig, Richard, and Herbert I. Weisberg, (2004a), Determinants of Total Compensation for Auto Bodily Injury Liability Under No Fault: Investigation, Negotiation and the Suspicion of Fraud, Insurance and Risk Management, 71:4, 633-662, January. • Epley, Nicholas, and Thomas Gilovich, (2001), Putting Adjustment Back in the Anchoring and Adjustment Heuristic: Differential Processing of Self-Generated and Experimenter-Provided Anchors, Psychological Science, 12:5, 391-396. • Loughran, David, (2002) Deterring Fraud: The Role of General Damage Awards in Automobile Insurance Settlements, RAND, Working Paper, August. • Raiffa, Howard, (1982), The Art and Science of Negotiation, The Belknap Press of Harvard University Press. • Ross, Lawrence, H., (1980), Settled Out of Court, (Chicago, III: Aldine). • Tversky, A., and D. Kahneman, (1974), Judgment Under Uncertainty: Houristics and Biases, Science, 195, 1124-1130. • Wright, W.F. and U. Anderson, (1989), Effects of Situation Familiarity and Incentives on use of the Anchoring and Adjustment Heuristic for Probability Assessment, Organizational Behavior and Human Decision Processes, 44, 68-82.

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