Auditors’ Usage of Non-Financial Data and Information during the Assessment of the Risk of Material Misstatement for an Audit Engagement: A Field Study
Bibliographic record
Abstract
Audit firms that fail to detect fraud or material misstatements in the financial statements of their audit clients may suffer substantial monetary penalties and negative publicity in the event of audit failure. The present study investigates auditors’ perception regarding the use of non-financial data and information to verify the validity of financial data and information reported by an audit client during an audit engagement. In addition, the current study considers the sources of information that auditors may depend on when searching for explanations for unusual trends in the financial statements of an audit client. The present study is based on a field study conducted in Egypt during the year 2014.Using a questionnaire supplemented by in-depth interviews, the present study showed that auditors are likely to make moderate use of non-financial data and information when assessing the risk of material misstatements during an audit. However, it seems that auditors prefer to depend on financial data and information more than non-financial data and information when developing expectations about account balances during an audit engagement. Furthermore, the present study pointed out that auditors appear to rely more on inquiries of audit client personnel than other sources of information when searching for explanations for unexpected trends in the financial statements of an audit client.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".