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 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 | 0.005 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".