Enhancing auditors fraud risk assessment by using throughput model as a decision aid
Bibliographic record
Abstract
Following the recommendations in the current standards (e.g., Canadian Institute of chartered accountants, IAASB, AICPA (SAS No. 82 and 99)), along with the fraud triangle factors, in this work, a decomposition approach that employs SAS No. 99 factors is proposed, whereby these are decomposed in a Throughput model (TP) that serves as a decision aid. Auditors’ task of assessing fraud risk is a critical step that affects auditing planning and procedures, especially in the light of the recent major financial scandals. Authors of several prior studies suggest that a decision aid is an effective way to improve fraud risk assessment and make the best use of professional skepticism. Throughput model breaks up the decision making into four main dominant concepts: Perception (P), Information (I), Judgment (J), and Decision Choice (D). This decision aid is expected to be beneficial in the performance of comprehensive fraud risk assessments, and direct the auditor’s attention to wide classes of problems, especially those associated with the SAS No. 99/ ISA 240 requirements. This work is intended to test the decomposition of the categorized fraud risk factors into processes comprising the thinking model. In the present study, an experimental setting comprising of 42 auditors from different audit positions was adopted, and the model was tested using Partial Equation Modeling PLS. A comparison analysis was subsequently performed to compare auditors characterized by high and low skepticism in two fraud risk conditions (high and low). The results suggest that, when the SAS No. 99 factors were decomposed into the dominant concepts of the Throughput model, an effect was found between these dominant concepts. In addition, study findings reveal no significant differences between high and low skepticism when auditors follow the process of thinking model to assess fraud risk. These findings suggest that the requirement and recommendation under SAS No. 99 can effectively increase auditors’ sensitivity to high risk factors when the situation suggests high fraud risk.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".