Common types of fraudulent accounting / Akma Hidayu Abdul Wahid and Rafizan Abdul Razak
Bibliographic record
Abstract
Accounting fraud occurs when an individual knowingly alters financial documents to provide a false impression of a company's financial health. Several distinct forms of accounting fraud are more common than others. Among these are the following: overstatement of income, misrepresentation of assets, understatement of expenses. There are several methods by which a firm might intentionally inflate its reported income. A firm, for example, will acknowledge the sales prior to their realization or before receiving money. Simply put, the timing of acknowledging income is incorrect. For example, changing the date of an event to occur after the end of the year allows for extra sales transactions for the present year. Another method involves generating counterfeit invoices. In 2022, Securities Commission Malaysia reported that Serba Dinamik was prosecuted in the Kuala Lumpur Sessions Court for providing a false statement about Serba Dinamik's revenue to Bursa Malaysia Securities Berhad. The charge that was preferred under section 369(a)(B) of the Capital Markets and Services Act 2007 ("CMSA") was related to Serba Dinamik's Quarterly Report on the Consolidated Results for the Quarter and Year Ended 31 December 2020 (Securities Commission Malaysia, 2022).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".