Amiri, Sayed Mahbub Hasan and Mithila, Marzana (2025) Redefining the Auditor’s Toolkit: AI and the future of business assurance. International Journal of Science and Research Archive, 15 (3). pp. 737-744. ISSN 2582-8185
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Abstract
The role of AI in auditing is quickly changing the world of business assurance. Traditional audit is limited in terms of scope, speed, and accuracy as business operates in more complex financial ecosystems. AI tools from machine learning algorithms to natural language processing and robotics process automation have the ability to enable auditors to analyse large data sets and detect anomalies leading to a continuous real-time audit. This article discusses how AI is changing the auditor’s toolbox and by extension, transforming auditor role in providing value-added insights to companies. Based on a thorough literature review and an empirical analysis with practicing audit professionals and financial professionals, this work examines the current use of AI in the context of audit, as well as the beliefs about its potential and risks, and the emerging role it is expected to have in the work of auditors. Results show that AI can significantly improve audit quality, fraud detection, and/or enterprise risk and introduces some important challenges in data governance, transparency, and ethical oversight. The paper finds that AI does not substitute auditors but complements them by reallocating their focus from manual checking to strategic thinking and analysis. This development will require upskilling, adaptation of regulation, and a nuanced approach to combining the possibilities offered by technologies with professional norms. The paper provides actionable suggestions for auditors and audit firms and policy makers interested in leveraging AI for better business assurance.
Item Type: | Article |
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Official URL: | https://doi.org/10.30574/ijsra.2025.15.3.1789 |
Uncontrolled Keywords: | Accounting Innovation; Artificial Intelligence; Auditing; Auditor Role Evolution; Business Assurance; Digital Transformation; Machine Learning |
Depositing User: | Editor IJSRA |
Date Deposited: | 27 Jul 2025 14:47 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/2291 |