The Audit Risk Assessment of European Small- and Mid-Size Enterprises
Bibliographic record
Abstract
To build trust, SMEs must pass on information as clearly as possible, which can be achieved through a transparent financial reporting process. The article aims to study the impact of six accounting quality risk indicators in audit risk assessment from SME audit reports in EU countries, comparing the findings with the analysis of the same indicators at CEECs level. The qualitative research methodology consists of a descriptive study of the risks in the audit reports, emphasizing their types and connection with the company’s characteristics. The study is based on a sample of 443 SMEs listed on the European stock markets and included in the Audit Analytics database, an online platform with information from the company’s financial statements and audit reports. According to the results, the “Audit Fees-Significant Non-Audit Fees” indicator had the highest accounting quality risk impact on SMEs audit reports in the EU. In contrast, for the CEECs companies, the “Audit Fees–Significant Change” index had a more significant impact on the audit reports. The study’s results showed an average trend of 15–16 reported situations per year, with a substantial increase over recent years for CEECs. The main conclusion from the study is that the uncertainties reported by the auditors depend more on the company’s field of activity and how it is managed; therefore, the SME sector should be coordinated according to the accounting regulations regarding the principles and the content of the financial reports. Considering the fast evolution of risks that may affect the audit reports of a small company and the fact that this topic has yet to be thoroughly researched, we find it relevant. The contribution of this article consists of a systematic analysis of the audit risk matrix completing the existing literature, which is why the field can be discussed more widely.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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 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".