Does Artificial Intelligence Help Reduce Audit Risks?

Abstract

This article aims to discover how AI-powered systems facilitate auditing, what risks emerge for AI-assisted audits and how to deal with these new risks. The paper studies the impact of cognitive computing on audit risk. AI-powered software is capable of self-learn so that it can identify patterns in data and codify them in predictions, rules and decisions. This self-learning ability can become both a benefit and, at the same time, insecurity. Although AI-self-learning helps make the process more efficient and calculations more accurate by improving the algorithm, eliminating errors and reducing risks, it creates new previously unknown threats. We discovered inherent limitations of cognitive-based technologies and risks for the audit process associated with using AI systems. We also proposed a complex security model that can reduce the uncertainty of AI-enabled audit and provides insight into future research opportunities.

Publication DOI: https://doi.org/10.1109/ACIT58437.2023.10275661
Divisions: College of Business and Social Sciences > Aston Business School > Cyber Security Innovation (CSI) Research Centre
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: Copyright © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 13th International Conference on Advanced Computer Information Technologies
Event Type: Other
Event Dates: 2023-09-21 - 2023-09-23
Uncontrolled Keywords: artificial intelligence,machine learning,automation,audit,risk
Last Modified: 12 Apr 2024 07:23
Date Deposited: 01 Aug 2023 09:24
Full Text Link:
Related URLs: https://ieeexpl ... cument/10275661 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2023-10-17
Accepted Date: 2023-06-20
Authors: Adamyk, Oksana
Benson, Vladlena (ORCID Profile 0000-0001-5940-0525)
Adamyk, Bogdan (ORCID Profile 0000-0001-5136-3854)
Al-Khateeb, Haider (ORCID Profile 0000-0001-8944-123X)
Chinnaswamy, Anitha (ORCID Profile 0000-0002-3817-4239)

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Access Restriction: Restricted to Repository staff only until 17 October 2024.


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