Communicating Key Audit Matters: A Post Implementation Review
Bibliographic record
Abstract
This report summarizes the results of a post implementation review of the implementation of Key Audit Matters (KAMs) in the Canadian market for entities with yearends beginning after December 15, 2020. KAM data was collected using the Audit Analytics database and interviews were conducted with stakeholders including auditors, audit committee members, preparers, and sophisticated users. In all, the data set consisted of 618 enhanced audit reports encompassing 895 KAMs and 38 hour-long stakeholder interviews conducted between March and July 2022. \nThe study shows that a distinctive Canadian KAMs reporting practice has developed since the enhanced audit reporting standards became effective. This practice is characterized by a low average number of KAMs per audit report (1.55) compared to other jurisdictions that require KAMs reporting. Fewer than 5% of audit reports have zero KAMs. Zero KAM reports involve extensive consultations across audit firms, as practitioners weigh the pros and cons of reporting a zero KAM report versus reporting one that is of limited value to users. \nThe Canadian KAMs reporting practice is virtually universal in opting not to include outcomes or observations. Interview data suggests that the Canadian legal and regulatory environment had an important influence, with auditors hesitant to go beyond the requirements of the standard because of possible legal exposure. The result is a KAM with tight, rigorous wording. Many interviewees mentioned aligning with the Critical Audit Matter practices used by their US counterparts. \nTextual analysis of the KAM passages across the data set suggests that Canadian auditors are customizing their KAM descriptions and responses to the particular circumstances of their clients. In contrast, KAMs reported for the same entity on the same issue in successive years are much less textually diverse. In most cases, the text includes few if any changes because the conditions that drove the KAM reporting in the first year persist. Although this practice could result in an atrophy to boilerplate, leading to diminished usefulness of the reports, consistency in the year-over-year wording was also viewed as logical and inevitable when conditions are unchanged. \nOur interviews indicate that the training and support for the KAMs implementation served well: significant time and effort was required by engagement and professional practice staff but the implementation proceeded according to expectations, with financial reporting process stakeholders (auditors, preparers, audit committee members) consistently reporting “no surprises” and few areas of sensitivity. \nUsers reported limited awareness of the existence of KAMs, and very limited use of the information. Even after reviewing example KAMs, users indicated that they were unlikely to use the information. Given the barrage of information, users viewed the KAMs as dated and not sufficiently informative about the business and/or business model. \nThe KAMs practice developed for entities listed on the Toronto Stock Exchange (TSX) can provide helpful guidance for the upcoming TSX-Venture Exchange (TSX-V) implementation. However, the less mature governance structures within many TSX-V-listed entities could pose challenges to a smooth KAMs implementation. Auditors are best placed to mitigate these challenges through education and engagement with preparers and audit committee members.
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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 | 0.253 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".