Who Blows the Whistle on Corporate Fraud?
Bibliographic record
Abstract
What external control mechanisms are most effective in detecting corporate fraud? To address this question we study in depth all reported cases of corporate fraud in companies with more than 750 million dollars in assets between 1996 and 2004. We find that fraud detection does not rely on one single mechanism, but on a wide range of, often improbable, actors. Only 6% of the frauds are revealed by the SEC and 14% by the auditors. More important monitors are media (14%), industry regulators (16%), and employees (19%). Before SOX, only 35% of the cases were discovered by actors with an explicit mandate. After SOX, the performance of mandated actors improved, but still account for only slightly more than 50% of the cases. We find that monetary incentives for detection in frauds against the government influence detection without increasing frivolous suits, suggesting gains from extending such incentives to corporate fraud more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".