Determinants of Total Compensation forAuto Bodily Injury Liability Under No-Fault:Investigation, Negotiationand the Suspicion of Fraud
Bibliographic record
Abstract
Auto Bodily Injury Liability claim payments are predominantly negotiated settlements, with less than two percent the result of complete litigation and jury trials. All settlements consist of a combination of claimed economic loss, called special damages, and a payment for “pain and suffering”, called general damages. The dependence of the total compensation on a variety of factors relating to the type and magnitudes of the economic losses, medical and wage loss, and to the type and severity of injury has been explored by prior researchers who found medical losses to be the primary determinant of total compensation but they also found that other severity variables play a distinct and significant role in the final settlement values. Further research introduced the notion that both the information gathered in the course of investigation and the adjuster’s attitude toward the quality of the claim, especially the suspicion of fraud, also played a significant role in the final settlement value. Recently, it has been shown that settlement values for subjective injury claims are systematically lower relative to special damages and indicate that insurers use their negotiating power to obtain lower settlements on questionable claims as a rational response to the presence of fraud and build up claims. The current paper extends that research by examining additional variables specifically related to the investigation and negotiation processes and quantifying the effect of those variables on the final total compensation. In particular, we find that strain and sprain claims command lower general damages relative to specials, even in the absence of suspicion of fraud and build up, but that the intensity of suspicion of fraud and build up can reduce overall payments as much as 24 percent. For the first time, the negotiating effect of attorney demands enters the quantitative model in addition to the usual contingency fee. Finally, evidence that insurers are isolating low impact collisions and reducing the compensation through negotiation is explored and quantified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".