The Effectiveness of Alternative Risk Assessment and Program Planning Tools in a Fraud Setting*
Bibliographic record
Abstract
Abstract This study examines the impact of alternative risk assessment (standard risk checklist versus no checklist) and program development (standard program versus no program) tools on two facets of fraud planning effectiveness: (1) the quality of audit procedures relative to a benchmark validated by a panel of experts, and (2) the propensity to consult fraud experts. A between‐subjects experiment, using an SEC enforcement fraud case, was conducted to examine these relationships. Sixty‐nine auditors made risk assessments and designed an audit program. We found that auditors who used a standard risk checklist, structured by SAS No. 82 risk categories, made lower risk assessments than those without a checklist. This suggests that the use of the checklist was associated with a less effective diagnosis of the fraud. We also found that auditors with a standard audit program designed a relatively less effective fraud program than those without this tool but were not more willing to seek consultation with fraud experts. This suggests that standard programs may impair auditors' ability to respond to fraud risk. Finally, our results show that fraud risk assessment (FRASK) was not associated with the planning of more effective fraud procedures but was directly associated with the desire to consult with fraud specialists. This suggests that one benefit of improved FRASK is its relation with consultation. Overall, the findings call into question the effectiveness of standard audit tools in a fraud setting and highlight the need for a more strategic reasoning approach in an elevated risk situation.
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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 | 0.041 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".