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

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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 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

negotiation
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
negotiation7
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
table 3 1
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

negotiation9
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
negotiation11
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
slide16

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

slide18

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

slide19

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

negotiation future modeling work
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
references
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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