Formation of the up-to-date paradigm of audit as applied science

Zhil'tsova, Yu.V.; Кемаева, С А; Kozmenkova, S.V.; Maslova, T.S.

doi:10.24891/ia.21.9.1037

2018

DOI: 10.24891/ia.21.9.1037

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Formation of the up-to-date paradigm of audit as applied science

Yu.V. Zhil'tsova¹,

С А Кемаева²,

S.V. Kozmenkova³

et al.

Abstract: a кандидат экономических наук, доцент кафедры финансов и кредита, Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского (ННГУ),

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Cited by 7 publications

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The Professional and Entrepreneurial Element in Auditing

Andrienko,

Zhukov,

Kiz

et al. 2024

E3S Web Conf.

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The trust of society, investors, banks, and the state in the published information of economic entities is ensured through auditing, making auditing a public-legal function. The authors have analyzed the current state of the audit services market in Russia. As a result of the analysis conducted, inconsistencies in the state regulation of this function have been identified. Self-regulation of auditing in Russia, under the conditions of a non-alternative self-regulatory organization and strict control measures by government authorities, leads to a significant reduction in both auditors and auditing organizations, thereby reducing competition in the audit services market. The authors conclude that there are two components in auditing activities: professional and entrepreneurial, which have different goals and objectives. The authors identify insufficient implementation of professional elements in the activities of auditors and auditing organizations, as well as the predominance of entrepreneurial elements, as the prerequisites for problems in the industry's development and a decrease in society's trust in the work of auditors.

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The Professional and Entrepreneurial Element in Auditing

Andrienko,

Zhukov,

Kiz

et al. 2024

E3S Web Conf.

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Assessing the materiality threshold and scope of auditing procedures: The sector-specific approach

Safonova

Korovina

2020

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Subject. As part of any audit engagement, the auditor needs to obtain reasonable assurance that financial statements are free from material misstatements. For this, they assess the tolerable threshold of errors for financial statements, which, if exceeded, will influence the correctness of users' conclusions and adequacy of their subsequent decisions. International Standards on Auditing guide the assessment of the materiality threshold. However, the approach they stipulate fails to accommodate for the sectoral specifics and distinctions of some audited entities. Objectives. We assess the stable structural correlation of key financial reporting indicators through a sample of 130 agricultural entities operating in the Krasnodar Krai. The study presents a technique for assessing the materiality by variance from the average values of the sector as the size of a company may be. Methods. We apply general and special methods, such as induction, deduction, analysis, synthesis, computational graphics, monographs, algorithms. Results. In some cases, the resultant data appeared to significantly diverge at the estimated materiality level. Having analyzed the causes, we proved the reasonableness of the computations done by out technique. As a conclusion, we sum up strengths and weaknesses of the materiality assessment technique. Conclusions and Relevance. The enhanced materiality assessment technique accommodates for the sectoral specifics of agricultural entities. Assessing the materiality level, we revealed the structural correlation of various assets and liabilities. The technique is based on actual data and adaptable, if needed, to the given algorithm. It is more precise for non-standard entities. The findings can be used in the auditing theory and practice, serve for setting the accounting process and financial reporting in various entities. It will also be useful for the Master's Degree Programs in Economics.

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Artificial intelligence technologies: Prospects for use in the company’s internal audit

Safonova

ALEKSEENKO

2022

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Subject. This article studies the trends in the development of artificial intelligence technologies in the field of internal audit. Objectives. The article aims to highlight the main trends in the development of artificial intelligence technologies in the field of internal audit, substantiating the prospects for the application of such technologies in the context of the development of a new concept of internal audit. Methods. For the study, we used analysis and synthesis, induction and deduction, systems approach, and the computational and graphic, and accounting and analytical methods. Results. The article reveals the prerequisites for the discrepancy between business expectations aimed at obtaining advisory support in making management decisions and the existing paradigm of internal audit, focused on testing the internal control and risk management system. The analysis of the existing machine learning methods in the context of its potential applicability in the internal auditor’s work helped identify key areas for using machine learning to improve the efficiency of performing audit procedures. Conclusions and Relevance. Machine learning as a field of knowledge of the theory of artificial intelligence can help automate the routine processes of internal audit and orient this function in the field of information and consulting support for business, contributing to its shifting away from control and audit activities. The use of individual elements of artificial intelligence technology contributes to improving the efficiency of internal audit. The results of the study can be used both for practical application by internal audit services, and for the development of the theory and practice of this field of knowledge.

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Formation of the up-to-date paradigm of audit as applied science

Cited by 7 publications

References 0 publications

The Professional and Entrepreneurial Element in Auditing

The Professional and Entrepreneurial Element in Auditing

Assessing the materiality threshold and scope of auditing procedures: The sector-specific approach

Artificial intelligence technologies: Prospects for use in the company’s internal audit

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