Internal audit planning using spherical fuzzy ELECTRE

Internal audit is an independent and objective assurance and consulting activity that aims to improve the operations of an organization and add value to them. Planning internal audit by prioritizing the units to be audit is critical in terms of effective use of available audit and financial resources. In this paper, a new ELimination and Choice Translating Reality (ELECTRE) based decision support model is developed for addressing an internal audit prioritization problem. Spherical fuzzy sets are used for modeling the uncertainty in the nature of the problem and three different approaches are proposed within the study. The first approach is constructed with gradual concordance and discordance sets by comparing spherical fuzzy membership, non-membership, and hesitancy degrees of alternatives; the second approach is developed based on a single type of outranking relation obtained by utilizing score and accuracy functions of spherical fuzzy sets, and the third approach provides an increased fuzziness modeling capacity by using interval-valued spherical fuzzy sets. In the application part of the study, the units of an organization are prioritized for internal audit activity based on five components of the internationally recognized Committee of Sponsoring Organizations (COSO) framework. Sensitivity analyses for decision-maker and criterion weights and a comparative analysis with six other state-ofthe-art multi-criteria decision making (MCDM) models are also presented to analyze the consistency and validity of the proposed spherical fuzzy ELECTRE model. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a decision support model based on spherical fuzzy sets for addressing the internal audit prioritization problem. Three different approaches are proposed and applied to prioritize the units of an organization for internal audit activity. Sensitivity analyses for decision-maker and criterion weights are conducted, and a comparative analysis with other multi-criteria decision making models is presented.