Detecting fraudulent financial statements in pharmaceutical companies: Fraud pentagon theory perspective
Bibliographic record
Abstract
A fraudulent financial statement is an issue that continues to be discussed as a form of deviation from corporate governance. Covid-19 pandemic has also demanded management to uphold the company's performance to have a good public image. Thus, the present study sets out to scrutinize the fraud pentagon theory on fraudulent financial statements. Each element is not able to be tested directly. However, there are proxies. The pressure element is proxied as a personal financial need. The opportunity is becoming the nature of industry. Each of the qualities of the external auditors as well as the change of directors propose rationalization and competence. The frequent number of CEO’s appearances in photos is a proxy of arrogance. The testing was carried out on the registered pharmaceutical companies of the Indonesian stock exchange in the span of the 2015-2019 period. The samples were selected by the means of sampling technique which is purposive. Data are scrutinized by the means of panel data regression. The analysis results show that the characteristics of the industry positively affects financial reports which are fraudulent. Changing top management positions such as directors can be an indication of financial reports which are fraudulent. The personal financial need variables, the caliber of external auditors and the quantity of CEO’s appearance in photos pose no effects on the fraudulent financial statements of the Indonesian's pharmaceutical companies.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".