Decomposition of Fraud‐Risk Assessments and Auditors' Sensitivity to Fraud Cues*
Bibliographic record
Abstract
Abstract Practitioners and regulators are concerned that when auditors perceive management's attitude or character as indicative of low fraud risk, they are not sufficiently sensitive to high levels of incentive or opportunity risks in their overall fraud‐risk assessments. In this study, we examine whether a fraud‐triangle decomposition of fraud‐risk assessments (that is, separately assessing attitude, opportunity, and incentive risks prior to assessing overall fraud risk) increases auditors' sensitivity to opportunity and incentive cues when perceptions of management's attitude suggest low fraud risk. In an experiment with 52 practicing audit managers, we find that auditors who decompose fraud‐risk assessments are more sensitive to opportunity and incentive cues when making their overall assessments than auditors who simply make an overall fraud‐risk assessment. However, this increased sensitivity to opportunity and incentive cues appears to happen only when those cues suggest low fraud risk. When opportunity and incentive cues suggest high fraud risk, auditors are equally sensitive to those cues whether they use a decomposition or a holistic approach. We discuss and examine potential explanations for this finding.
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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 | 0.011 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".