Розроблення концептуальної методології періодичного аудиту облікової інформації
Notice bibliographique
Résumé
Purpose of the research. The purpose of this article is to develop methodologies for the continuous control and auditing of financial and accounting information amidst the rapid advancement of hardware and software technologies that enable real-time electronic reporting. The research aims to describe modern technologies applicable to continuous auditing and data analysis, specifically to ensure the reliability of information generated by information systems. It distinctively examines the concept of “periodic audit” as an alternative to embedded control modules, deeming it particularly relevant for the Ukrainian context.Methods of the research. The article employs an analysis of current research and publications on realtime reporting and continuous auditing, referencing works by American and Canadian researchers and professional organizations (AICPA, CICA, Institute of Internal Auditors). It reviews theoretical frameworks for implementing continuous control and conceptual models of periodic audit. Electronic reporting standards, notably XBRL, and their implementation practices globally and in Ukraine are analyzed. Technologies for obtaining, verifying, loading, and analyzing data for periodic audit purposes are described, including statistical methods (trend analysis, regression analysis, cluster analysis, etc.). Data from surveys of Ukrainian enterprises regarding the online publication of financial statements are also utilized.Results of the research. The study demonstrates that while technology facilitates real-time reporting, it necessitates corresponding continuous control and auditing to assure information reliability. Traditional continuous audit approaches involving embedded control modules in ERP systems present significant drawbacks: risks to system stability, high implementation costs, and limited developer support. The concept of “periodic audit” is proposed as an alternative. This approach involves periodically (e.g., nightly) extracting transactions into a separate data warehouse for subsequent analysis using specialized audit software. This enables near-continuous assurance regarding information, irrespective of the effectiveness of the enterprise’s internal controls. A general model for acquiring data for periodic audit purposes is presented, encompassing data verification, correction, loading, and analysis stages. The primary goal of data analysis is identified as developing patterns to detect unexpected data or transactions indicative of fraud or errors.Possible application of the research results. The proposed “periodic audit” methodology can be applied by internal and independent auditors to provide near-continuous assurance regarding the reliability of financial and accounting information generated by corporate computer systems. This approach is particularly beneficial in environments with inadequate internal controls, such as in Ukraine. The technology is versatile for identifying significant internal control weaknesses across various conditions, enterprises, information systems, and business/accounting structures. The results can inform the enhancement of audit practices, improve financial reporting quality, and enable the timely detection of fraud and errors.Conclusions. Rapid technological development necessitates a shift towards continuous or near-continuous control of financial information. The “periodic audit” concept, involving regular analysis of data extracted to a separate warehouse, offers a practical and compromise alternative to embedded continuous control systems, especially where resources are limited or internal controls are weak. This approach allows auditors to provide timely assurance with minimal delay. Despite their advantages, continuous control and periodic audit systems have limitations, including the risk of failing to detect errors (false negatives) and generating false alarms (false positives). Successful implementation requires an understanding of business processes and appropriate internal control technologies. The periodic audit concept holds particular relevance for Ukraine, given the need to improve management and control practices.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».