Beyond Risk Shifting: The Knowledge‐Transferring Role of Audit Liability Insurers*
Bibliographic record
Abstract
ABSTRACT Regulators and researchers tend to focus primarily on the risk‐shifting benefits of audit liability insurance. We obtain field data from the US audit insurance industry (16 interviews and 83 survey responses) to examine whether insurers also possess characteristics favorable to transferring their private risk management knowledge to the audit firms they insure. Possessing such characteristics would enable insurers to use their relative knowledge advantage to provide a benefit to audit firms beyond risk shifting. Thus, examining this issue helps broaden our understanding of insurers' role in auditing. We examine our data through the lens of the knowledge transfer theory and find evidence that audit liability insurers have the motivation and capacity to accumulate and transfer risk management knowledge to the audit firms they insure. We find that audit firms, particularly the small resource‐constrained firms (i.e., non–Big 4 and non‐second‐tier), rely on and benefit from their insurers' risk management knowledge. We also find that insurers transfer such knowledge through multiple mechanisms, including free consultative services, policy premium incentives, and continuing professional education classes. Our results highlight the important role of audit insurers as transferors of risk management knowledge to audit firms. Broadly, our results extend knowledge transfer theory and suggest areas for future research in US and international contexts.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".