Deductible Contracts Against Fraudulent Claims: Evidence from Automobile Insurance
Bibliographic record
Abstract
Insurance fraud is now recognized as a significant resource allocation problem in many markets. The object of this study is to verify how straight deductible contracts may affect the equilibrium level of falsification in automobile insurance. This type of contract is observed in many markets, even if it is not optimal under costly state falsification. A higher deductible may create incentives to fraud or cheat, particularly when the insured anticipates that the claim has a small probability of being audited. To verify this proposition, we estimate a loss equation for which one of the determinants is the amount of the deductible, using a data set of claims filed for damages following an automobile accident with 20 insurance companies in Quebec in 1992. Since we only have access to reported losses, a higher deductible also implies a lower probability of reporting small losses. In order to isolate the fraud effect related to the presence of a deductible in the contract, we jointly estimate a loss equation and a threshold equation. The threshold is the amount over which an insured decides to report a given loss. It can be interpreted as a personal deductible and it is not observable. Our results indicate, among other things, that with an appropriate correction for selectivity, the amount of the deductible is a significant determinant of the reported loss, at least when no other vehicle is involved in the accident; in other words, when the presence of witnesses is less likely.
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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 | 0.013 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".