Fuzzy logic: application for audit risk and uncertainty

Friedlob, G. Thomas; Schleifer, Lydia L. F.

doi:10.1108/02686909910259103

Cited by 34 publications

(12 citation statements)

References 15 publications

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“…The research on the conjoint application of fuzzy sets and probability theory reports on several studies including marine and offshore safety assessment (Eleye-Datubo et al 2008), financial modeling (Muzzioli and Reynaerts 2007), information systems (Rolly Intan and Mukaidono 2004), auditing (Friedlob and Schleifer 1999), manufacturing cost estimation (Jahan-Shahi et al 1999), and water quality management (Benoit 1994).…”

Section: Mathematical Model and Proceduresmentioning

confidence: 99%

A soft multi-criteria decision analysis model with application to the European Union enlargement

2010

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This paper proposes a new multi-criteria decision analysis (MCDA) model that uses a series of existing intuitive and analytical methods to systematically capture both objective and subjective beliefs and preferences from a group of decision makers (DMs). A defuzzification method that combines entropy and the theory of displaced ideal synthesizes crisp values from the DMs' subjective judgments. This approach assists the DMs in their selection process by plotting alternatives in a four quadrant graph and considering their Euclidean distance from the "ideal" choice. A pilot study illustrates the details of the proposed method. The DMs were a group of graduate students from the University of Paderborn in Germany. The pilot study concerned the addition of new members into the European Union (EU), a decision that has profound economic and political effects on both the entering and existing members of the Union. The DMs were required to consider a large number of internal strengths and weaknesses and external opportunities and threats in assessing the decision to enlarge the EU. Although the pilot study was not performed by actual DMs from the EU, it was an excellent platform for testing the proposed model.

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Section: Mathematical Model and Proceduresmentioning

confidence: 99%

A soft multi-criteria decision analysis model with application to the European Union enlargement

2010

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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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“…The scoring system The research on the conjoint application of fuzzy sets and probability theory reports on several studies including marine and offshore safety assessment (Eleye-Datubo, Wall and Wang, 2008), financial modelling (Muzzioli and Reynaerts, 2007), information systems (Rolly Intan and Mukaidono, 2004), auditing (Friedlob and Schleifer, 1999), manufacturing cost estimation (Jahan-Shahi, Shayan and Masood, 1999) and water quality management (Benoit, 1994). Many defuzzification techniques have been proposed in the literature.…”

Section: Tablementioning

confidence: 99%

A multiple criteria decision support system for autonomous underwater vehicle mission planning and control

Tavana

Bourgeois

2010

IJOR

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The growing use of autonomous air, surface, ground and underwater systems is continually demonstrating new military and commercial possibilities and applications. The state of the art in the planning and control of autonomous underwater vehicles (AUVs) is largely precarious because AUVs provide infrequent feedback, operate autonomously for long periods of time and yet have little knowledge of their dynamic environment. Consequently, mission planning and control is typically conducted based on human expert knowledge of vehicle capabilities, some level of observed environmental conditions and ad hoc optimisation with little assistance from computers. While the human expert offers a significant ability to mentally process data, the result typically lacks numeric and quantitative analysis of alternatives. Navigation of AUVs in the complex ocean environment involves time dependent dynamics, resulting in a problem that is computationally prohibitive for the use of brute force optimisation techniques. Although some research has been conducted for specific types of missions, and 'greedy' global-optimisation approaches have been investigated, no systematic and coherent approach to the requirement exists. We propose a dynamic multiple criteria decision support system that considers dynamic and episodic ocean phenomenon to provide reasonable and in-context recommendations with respect to the stated objective and subjective mission goals. Multi-criteria decision analysis, analytic network process and fuzzy sets are used in the model to reduce the vehicle routing solution space and maximise time-on station in adverse environments. The proposed system can be an added hierarchical layer on the top of a mission planning system currently under development by the United States Navy.

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Fuzzy logic: application for audit risk and uncertainty

Cited by 34 publications

References 15 publications

A soft multi-criteria decision analysis model with application to the European Union enlargement

A soft multi-criteria decision analysis model with application to the European Union enlargement

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

A multiple criteria decision support system for autonomous underwater vehicle mission planning and control

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