Fraud Examination: Investigation and Audit Procedures in the Perspective of International Studies
Bibliographic record
Abstract
Financial statement fraud still becomes a significant threat in an organization. In the other hand, the auditor is the one who is responsible to reveal financial statement fraud. Therefore, auditor has to understand technical steps to prevent and detect financial statement fraud. The aim of this research is to study research findings related to investigation and audit procedures in several countries. Moreover, this study also analyzes the appropriate ways to prevent and detect financial statement fraud based os the findings. The method used in this research is systematic literature review. An article search was limited in the last 5 years from 2017-2022 on indexed international journals. Based on the method, from the research objects, the location of the research was in South Africa, Nigeria, Ghana, Jordan, Middle East, Australia, Canada, Ireland, Israel, Vietnam and Indonesia. Moreover, research finding showed that the prevention and detection toward financial statement fraud could be done in some ways; they are: external audit, investigative audit and forensic accounting, the use of information and technology and also key audit matters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".