Multivalued logic in audit risk assessment

Bulyga, R. P.; Гисин, В. Б.; Volkova, E. S.

doi:10.1109/scm.2016.7519833

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…It has been extended, for example, in Shafer and Srivastava (1990), Srivastava and Shafer (1992), Dutta, et al (1993), Srivastava and Dutta (2002), Srivastava and Mock (2002), Harrison et al (2002), Sun et al (2006), Srivastava and Shafer (2008), Srivastava (2011), Srivastava et al (2011) and, Nehmer andSrivastava (2016). Recently, some researchers also believe fuzzy systems are suitable mathematical modeling for uncertain conditions such as those professional, complex judgments in assessing risks of audit (Friedlob and Schleifer, 1999;Lin et al, 2003;Chang et al, 2008;Huang et al, 2009;Comunale et al, 2010;Bulyga et al, 2016).…”

Section: Risk-based Auditmentioning

confidence: 99%

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach

Sardasht¹,

Rashedi²

2018

IJDAR

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In the professional circles, factors affecting audit risk are treated independently; however, a more objective approach in assessing detection risk should be involved the relationships among the audit risk factors. This study introduces a framework based on a fuzzy multi-criteria decision support to identify the influencing factors may affect the audit risk model considering the interdependencies among them. We first takes advantages of a fuzzy screening method to screen forty more practical factors may affect the audit risk model out of 58 potential ones. Then, we applied a combined approach of Analytic Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to weight the factors and prioritize them considering their inner and outer dependencies complemented with a case study of Iran. To be more compatible with ambiguities related to human beings and make more useful decisions in the real world, the fuzzy set theory is used. The combined approach used in this study providing correct, precise weight for each factor can explore a more rigorous framework for decision-making in risk assessment by integrating interdependent relationships within and among audit risk factors. of audit evidence through acceptable audit risk, and the degrees of inherent risk and control risk . Therefore, to determine detection risk properly would influence the results of the audit.However, each component of the audit risk model is influenced by various factors. Most studies which have been conducted so far often focus only on the audit risk formula and rarely pay attention to its factors. The risk-based audit will achieve its targets if influencing factors firstly are identified.As Dusenbury et al. (2000) highlighted proper use of the audit risk model requires the component risks should be characterized as dependent risks. Anyhow, auditing standards, risk-based audit manuals, and the practical procedures individually consider audit risk components and those factors that may affect them. They treat them as independent, separate parts without taking into account any inner and outer interdependencies. Regarding the audit risk model, while the outer dependency is the one-way relationship between the underneath level that affects the upper one, the inner dependency is the relationship within factors of a certain level which are not independent of one another. The former refers to, for example, the dependency between the cluster of Inherent risk and Financial statement level factors and the latter is the dependency among factors of Financial statement level and Account the remaining sum level within Inherent risk cluster (Table 5).According to Rozario and Vasarhelyi (2018), "the external audit profession substantially lags in technological innovation."In the audit risk context, also, the traditional and normative trends mostly consider the direct and unidirectional effects of each component of the audit risk and their factors independently. Therefore, the interdependencies of them and their mutual effects are ignore...

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Section: Risk-based Auditmentioning

confidence: 99%

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach

Sardasht¹,

Rashedi²

2018

IJDAR

View full text Add to dashboard Cite

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Falsifcation of Financial Statements: Concept and Tools of Identifcation

Викторовна

Денисовна

2018

Učët, analiz, audit

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To succeed in a market economy an economic entity should attract investments. The quality of accounting and analytical information about a company’s activities is an important factor in the decisions potential investors make. Accounting (fnancial) reporting is a signifcant part of the informational support of the company’s activity. The problem of reliability of the accounting (fnancial) statements has always been relevant. According to the modern concept of business audit, audit is primarily understood as an activity aimed at reducing business risks. Nowadays the main task of an auditor is to provide assurance that the accounting (fnancial) reporting does not contain signifcant misstatements because of its falsifcation, or mistakes made by employees of the auditee. Assessing the risk of fnancial statements falsifcation is an urgent and diffcult task. Though the term “falsifcation of fnancial statements” is widely used and seems clear in terms of common sense, the scientifc understanding and normative defnition of this concept is not so defnite. This article analyzes the concept of “falsifcation of fnancial statements” and approaches to its defnition in foreign and domestic practice; reviews modern tools to identify risks of fnancial statements falsifcation; discusses issues related to the use of mathematical models to identify the risk of fnancial statements falsifcation. To do this the authors analyze the index model of the American scientist M. Benish, carry out econometric tests within the assumptions of the Gauss-Markov theorem and propose a variant of developing an index model to detect accounting (fnancial) statements falsifcation. An attempt was made to create a model to identify the risk of reporting information falsifcation with a certain degree of probability that could be applied in Russia. To create such a model, called NARM, there were selected 75 reports of Russian organizations, of which 1/3 were falsifed. This model makes it possible to identify the probability of fnancial statements falsifcation to within 76%.

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Multivalued logic in audit risk assessment

Cited by 2 publications

References 2 publications

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach

Falsifcation of Financial Statements: Concept and Tools of Identifcation

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