Remembering Fraud in the Future: Investigating and Improving Auditors' Attention to Fraud during Audit Testing*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it