Remembering Fraud in the Future: Investigating and Improving Auditors' Attention to Fraud during Audit Testing*
Bibliographic record
Abstract
ABSTRACT During the testing stages of the audit, auditors must divide their attention simultaneously between (i) performing the planned audit procedures and (ii) remaining broadly skeptical and alert for fraud. Regulators note instances in which auditors do not take actions that effectively respond to fraud risks during these later stages, suggesting auditors may devote insufficient attention to fraud while they are busy executing the planned audit procedures. Leveraging prospective memory theory, I identify and test an intervention that can improve auditors' attention to fraud by encouraging auditors to have implementation intentions—that is, more detailed plans about when and how they will consider fraud. I find that encouraging implementation intentions interacts with auditors' perceived fraud task importance to increase auditors' attention to fraud when this attention would otherwise be lower, making auditors more likely to take effective fraud actions. Importantly, these results also indicate that, even in a high fraud risk setting, auditors may devote insufficient attention to fraud while performing the planned audit procedures, confirming concerns voiced by regulators. However, my study also highlights concerns about regulators' inspection processes prompting auditors to focus too heavily on inspection risk, as the results suggest auditors are less likely to detect fraud in high‐risk audit areas thought to have low inspection risk.
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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 | 0.048 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".