Challenging Global Group Audits: The Perspective of US Group Audit Leads*
Bibliographic record
Abstract
ABSTRACT Regulators are concerned about the quality of group audits because of poor inspection results, recent enforcement actions against component auditors, and the significance of these audits to the global economy. Yet research in this area is nascent. In response, we survey and interview US‐based global group audit leads to better understand the group audit process and their perceptions of challenges that arise on such engagements. Our findings indicate that group auditors organize their audits consistent with the client's structure, which drives component auditor selection, scoping, and fees. Group auditors routinely find fault with component auditors, perceiving that work performed and/or documentation provided is not sufficient, not appropriate and/or not communicated timely, to comply with US standards and reporting deadlines. In some cases, they perceive that the component auditor is unable and/or unwilling to comply and in others, that the component auditor misunderstands group instructions due to language proficiency or differing interpretation of the standards. Notably this perspective is ethnocentric, as group auditors almost exclusively attribute issues to component auditors. While ethnocentric tendencies appear myopic, they provide insight into unrecognized or overlooked aspects of the global firm model of cooperative arrangements. This study highlights the significance of the global firm's network structure to global group audits. Thus, we encourage intercommunity research, among (quantitative and qualitative) scholars and standard setters, to consider group audits in the broader context of firm networks.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".