Forensic Accounting and Risk Management: Exploring the Impact of Generalized Audit Software and Whistleblowing Systems on Fraud Detection in Indonesia
Bibliographic record
Abstract
Purpose—This paper investigates the role of forensic accounting skills in enhancing auditor self-efficacy towards fraud detection in Indonesia. It also examines the moderating effect of the implementation of Generalized Audit Software (GAS) and the whistleblowing system on the relationship between accounting and auditing skills and auditor self-efficacy, as well as their combined role in enhancing fraud detection. Methodology—A cross-sectional survey was conducted with 537 external auditors in Indonesia. Data were analyzed using multiple linear regression with moderation models, employing WarpPLS 8.0 software. Findings—The results indicate that practical communication skills, psychosocial skills, and accounting and auditing skills significantly enhance auditor self-efficacy. However, technical and analytical skills do not show a significant effect on auditor self-efficacy. Furthermore, auditor self-efficacy is found to have a direct and significant impact on fraud detection. This study also reveals that implementing GAS moderates the relationship between auditor self-efficacy and fraud detection, whereas the whistleblowing system does not demonstrate a significant moderating effect. Novelty—This study contributes to the literature by highlighting the role of forensic accounting skills and the implementation of GAS in enhancing auditor self-efficacy and fraud detection in the Indonesian auditing context.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".