Are Fraud Specialists Relatively More Effective than Auditors at Modifying Audit Programs in the Presence of Fraud Risk?
Bibliographic record
Abstract
ABSTRACT Previous studies indicate that auditors are able to identify fraud risk factors, but may not be able to translate this knowledge into an audit plan that effectively takes these factors into account to increase the likelihood of detecting fraud. Fraud specialists may be able to compensate for such limitations. This study investigates the relative merits of involving fraud specialists in assisting auditors by developing an audit plan that would effectively address fraud risk in a revenue cycle. Results show that fraud specialists did not differ from auditors in the number of procedures selected from a standard audit program; nor were these procedures cumulatively more effective than those selected by auditors. Fraud specialists generated a greater number of non-standard additional audit procedures, and those procedures were marginally more effective, but less efficient, than those of auditors, except for certain groups of procedures. Finally, although the fraud specialists proposed significantly more additional (non-standard) procedures than auditors, their proposed budget increase for this category of procedures was significantly smaller than the budget increase proposed by auditors. Adjustments to the overall time budget did not differ between fraud specialists and auditors. Data Availability: Data are available from the authors upon request.
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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 | 0.023 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".