Audits of Complex Estimates as Verification of Management Numbers: How Institutional Pressures Shape Practice
Bibliographic record
Abstract
Abstract Auditors and regulators have invested heavily in improving audits of estimates in recent years, but problems in this area persist. We examine the causes of these problems and why they persist. To do so, we interview 24 very experienced auditors about how they audit complex accounting estimates such as fair values and impairments and what problems they experience in the process. We find that auditors overwhelmingly choose to audit the details of management's estimate rather than use other allowable approaches. The steps auditors describe and the language they use to describe those steps indicate that they follow a process of verifying individual elements of management's assertions on a piecemeal basis, resulting in overreliance on management's process, rather than engaging in a critical analysis of the overall estimate. The problems that auditors identify are consistent with this view, and include failures to notice inconsistencies among the estimate and other internal data or external conditions and overreliance on specialists to identify, evaluate, and challenge critical assumptions. We interpret these processes and problems using institutional theory and identify two root causes: standards' and firm policies' emphasis on verifying management's model, and audit firms' division of knowledge between auditors and specialists. Institutional theory proposes these conventions arise from firms extending use of procedures that are legitimate in one area (i.e., auditing accounts without significant uncertainty) to a new area (i.e., auditing complex estimates), even though they are likely less effective in the new area. These conventions are reinforced by regulators' method of inspection and by firms' reluctance to change methods without a prompt to change to a clearly better method. We argue that these institutionalized conventions thwart auditors' good‐faith attempts to engage in skeptical analysis of estimates. Thus, audit quality problems are likely to persist.
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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 | 0.181 | 0.407 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".