Can Audit Prevent Fraudulent Financial Reporting Practices? Study of Some Motivational Factors in Two Atlantic Canadian Entities
Bibliographic record
Abstract
Much as has been written and done to prevent Fraudulent Financial Reporting (FFR) practices but FFR is still exists in the corporate world. It is common to think about FFR practices in large companies for its greater amount of consequences, though such practises have negative consequences in small companies as well. FFR practices raise questions about the legitimacy of contemporary financial reporting process, roles of auditors, regulators, and analysts in financial reporting. This empirical study attempts to investigate the motivational factors of the prevention and detection of FFR through the auditing process. The interviewees were carried out within the entity and proprietary theoretical framework with some accounting related management in two medium-sized organizations in Atlantic Canada in winter 2008. The findings of this research demonstrate that an audit is not enough to prevent and detect FFR. The audit structure needs to be revised and employees need to be educated in order for them to better understand their internal control process, and their own role. Companies need to evaluate their controls and internal audit process instead of relying on the yearly audit. This study found that the most common methods used for FFR are improper revenue recognition, understatement of expenses/liabilities, and overstated and misappropriation of assets.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".