Outsourcing and Audit Risk for Internal Audit Services*
Bibliographic record
Abstract
Abstract Some companies now outsource their internal audit function to public accountants. Internal auditors and accounting firms disagree about the merits of outsourcing. Each type of auditor claims to provide more cost‐effective services and appears to claim superior expertise. This paper uses agency theory to examine outsourcing and reconciles the outsourcing debate without resorting to differential auditor expertise. Under the assumptions that public accountants' “deep pockets” provide incentives to outsource and their higher opportunity cost provides a disincentive, we characterize the optimal employment contract with each auditor. We find that public accountants provide higher levels of testing, but possibly for a higher expected fee. This result supports both the internal auditor's claim as the lower cost provider, and the public accountant's claim of higher quality. We also find that incentives to outsource generally increase in various measures of risk, including the risk that a control weakness exists and the size of the loss that can result from an undetected control weakness.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".