Communicating Key Audit Matters: A Post Implementation Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,253 | 0,422 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,015 | 0,014 |
| Études des sciences et des technologies | 0,010 | 0,006 |
| Communication savante | 0,012 | 0,005 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».