Detecting fraudulent financial statements in pharmaceutical companies: Fraud pentagon theory perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it