Impact of AI on Audit and Assurance

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Author(s): Mahesh Thaluru (University at Buffalo, SUNY, USA), Moksh Gupta (University at Buffalo, SUNY, USA), Zishen Liao (University at Buffalo, SUNY, USA), Tianyi Zhang (University at Buffalo, SUNY, USA)and Raj Sharman (University at Buffalo, SUNY, USA)
Copyright: 2026
Pages: 38
Source title: Advancing IT Audits Through Integrative Approaches and Emerging Technologies
Source Author(s)/Editor(s): Manish Gupta (University at Buffalo, SUNY, USA), John Walp (University at Buffalo, SUNY, USA)and Raj Sharman (University at Buffalo, SUNY, USA)
DOI: 10.4018/979-8-3373-3078-5.ch003

Keywords: Engineering Science Reference / IT Governance / IT Policy and Standardization / Library & Information Science

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Abstract

The integration of artificial intelligence (AI) into auditing marks a transformative shift in finance, enhancing the conduct, assessment, and optimization of audits. AI-driven audits leverage machine learning, big data analytics, and automation to deliver more accurate, efficient, and reliable financial assessments. This paper explores AI's impact on corporate audits, particularly in risk assessment, compliance monitoring, and decision-making. AI enables a move from traditional practices to real-time reporting, predictive analysis, and automated fraud detection, addressing persistent challenges in manual processes. Compared to conventional methods, AI audits provide scalability, cost efficiency, and improved data integrity, while also raising concerns around data privacy, algorithmic bias, and ethical implications. The study offers insights into how AI is reshaping auditing—promoting transparency, refining resource allocation, and redefining audit functions in the digital era.

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