Could the minimization of opportunity prevent fraud? An empirical study in the auditors’ perspective
Bibliographic record
Abstract
Fraud prevention is the best effort to solve fraud problems. Minimizing opportunities can be one of the factors that needs to be considered to prevent fraud. This research aimed to analyze the effect minimization opportunity, which consists of several variables, specifically methods of prevention and detection of fraud, internal control, management policy and management integrity, to the prevention of fraud in point of view of the auditors of the Audit Board of the Republic of Indonesia and the local government internal auditors. Data collected by using a questionnaire. Usable sample consisted of 79 respondents. Data were tested using PLS. The research result declared that internal control is an effective factor to minimize opportunities to prevent fraud. Another finding from the study was that fraud prevention and detection methods were not able to reduce fraud. Instead, the fraud prevention and detection methods have a positive effect on the likelihood of fraud. The important thing that needs to be considered in future research is that the distribution of questionnaires to internal and external auditors can be carried out proportionally so that the perceptions of each party can be tested and compared.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".