Optimal Auditing with Scoring: Theory and Application to Insurance Fraud
Bibliographic record
Abstract
This article makes a bridge between the theory of optimal auditing and the scoring methodology in an asymmetric information setting. Our application is meant for insurance claims fraud, but it can be applied to many other activities that use the scoring approach. Fraud signals are classified based on the degree to which they reveal an increasing probability of fraud. We show that the optimal auditing strategy takes the form of a “red flags strategy,” which consists in referring claims to a special investigative unit (SIU) when certain fraud indicators are observed. The auditing policy acts as a deterrence device, and we explain why it requires the commitment of the insurer and how it should affect the incentives of SIU staffs. The characterization of the optimal auditing strategy is robust to some degree of signal manipulation by defrauders as well as to the imperfect information of defrauders about the audit frequency. The model is calibrated with data from a large European insurance company. We show that it is possible to improve our results by separating different groups of insureds with different moral costs of fraud. Finally, our results indicate how the deterrence effect of the audit scheme can be taken into account and how it affects the optimal auditing strategy.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".