Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique

Abstract

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

“…Using big data, firms can create process and analyze complicated and incoherent data into meaningful information. AI and ML propel for easy, accurate, and comprehensive auditing that enhances the accountability and credibility of financial reports ensures the quality of the audit, and helps the stakeholders to make reliable decisions [17].…”

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

“…Initiated by Gabus and Fontela (1972), DEMATEL is a structural technique that analyzes interdependent relationships and models the cause-and-effect relationships between complex elements of a system. It has been widely and successfully applied to resolve real-life problems (Chen et al 2015(Chen et al , 2018Hu et al 2020;Lin and Hsu 2018;Liou et al 2016; Peng and Tzeng 2019; Petrovic and Kankaras 2020; Tzeng and Huang 2012). Given the cluster of intertwined issues in complex systems, DEMATEL is appropriate and suitable for dealing with such problems (Hu et al 2018a, b).…”

A Novel Decision-Making Framework Based on Probabilistic Linguistic Term Set for Selecting Sustainable Supplier Considering Social Credit

“…Having different occurrence probabilities of all possible linguistic terms, PLTSs reflect experts' preference rationally (Gao et al, 2019). Thus PLTS DEMATEL method reveals the interaction relationship among different factors (Hu et al, 2021;Xu & Wang, 2017). In line with identification of CSFs, this decision-making framework adopts the DEMATEL method on the basis of PLTSs.…”