Aug 2026· ACM Computing Surveys· 0 citations· 119 references
TL;DR
A systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations, reveals gaps in current auditing practices and highlights the importance of incorporating AI value chain stages and AI maturity levels into auditing frameworks.
Abstract
Over the past decade, the exponential integration of artificial intelligence (AI) systems across various sectors has been propelled by significant advances in machine learning algorithms, data availability, and computational power. This progress has produced highly effective AI systems, but also underscores the critical need for effective auditing to critically evaluate these technologies. In this paper, we conduct a systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations. By reviewing key academic databases, including Google Scholar, IEEE, ACM, and Springer, we establish the scope of our survey and derive topics from our research questions. Our findings reveal gaps in current auditing practices and highlight the importance of incorporating AI value chain stages and AI maturity levels into auditing frameworks. This approach enables us to distinguish and recommend existing frameworks and methodologies that are most suitable for the specific contexts of different organisations, thus enhancing the effectiveness of AI system evaluations.
It is concluded that the auditing profession is at a critical juncture, requiring a concerted effort from regulators, standard-setters, firms, and educators to navigate the transformative impact of AI.
Geoffrey Odoch· International journal of com...· 0 citations
It is suggested that AI enhances fraud detection, risk assessment, and audit quality, while raising concerns regarding algorithmic bias, transparency, and professional skepticism.
Artificial Intelligence (AI) has emerged as one of the most transformative technologies influencing organizational governance, financial oversight, and risk management practices worldwide. The integration of AI into internal audit functions has significantly altered the traditional audit landscape by enhancing operational efficiency, improving fraud detection capabilities, strengthening risk assessment procedures, and enabling real-time auditing practices. This research paper examines the transformative role of AI technologies such as machine learning, neural networks, natural language processing, robotic process automation, and predictive analytics in reshaping internal audit operations. The study explores how AI-driven systems automate repetitive audit tasks, analyze large volumes of structured and unstructured data, and improve audit accuracy while reducing operational costs. Furthermore, the paper evaluates the challenges associated with AI adoption, including ethical concerns, cybersecurity risks, data privacy issues, technological dependence, and skill gaps among auditors. A comparative analysis between traditional and AI-enabled audit practices is also presented to assess the effectiveness and efficiency of AI-based auditing systems. The study concludes that AI is not replacing internal auditors but transforming their roles into more strategic, analytical, and advisory-oriented functions. Organizations that successfully integrate AI into their audit frameworks can achieve greater transparency, stronger governance, and improved organizational resilience in an increasingly digital business environment.
F. Raidah, M. Jobair, Md. Halimuzzaman et al.· American Journal of Financia...· 0 citations
Artificial intelligence (AI) is fundamentally reshaping the
auditing profession, challenging traditional competency
frameworks and redefining the scope of the auditor’s role.
This study is based on the premise that, beyond traditional
financial audit tasks, the contemporary auditor is
increasingly expected to contribute to audit committee
governance, sustainability (ESG) assurance, as well as
the direct application of AI-based tools in audit
engagements. Despite the growing academic and
professional interest in AI adoption, a comprehensive and
integrated framework capturing the full spectrum of AI-
related competencies required across all auditor roles
remains insufficiently developed in the literature. This
paper addresses this gap through a Structured Literature
Review (SLR) that examines 22 peer-reviewed articles
indexed in Web of Science and published between 2019
and 2025, identifying and synthesizing evidence on how
AI is reshaping auditor competencies across four
interconnected roles: financial auditor, audit committee
member, ESG assurance provider, and user of AI tools.
Based on the synthesized evidence, the authors propose
an integrated competency framework for the auditor
prepared for the AI era, structured around six competency
dimensions and four professional roles, with direct
implications for professional bodies, Continuing
Professional Development (CPD) programmes, and
certification requirements.
Elena Claudia Badea (florea), Andreea-Larisa Olteanu (Burca), M. Bunea et al.· Audit Financiar· 0 citations
A baseline review of the selected papers volume of the inaugural International Conference on Formal Methods and Foundations of Artificial Intelligence (FMF-AI 2025), published as Annales Mathematicae et Informaticae, introduces simple baseline metrics that can be recomputed in future FMF-AI editions to observe structural changes in the research landscape.
Gábor Kusper· Annales Mathematicae et Info...· 0 citations
Relevance.
The digital transformation of the economy and the exponential growth of the volumes of data generated by ERP systems create a new technological context for auditing activities. Traditional verification methods based on selective procedures and manual document processing face time, labor, and data coverage constraints. Under these conditions, the introduction of artificial intelligence (AI) technologies is considered as one of the key areas of audit modernization, which makes it possible to automate analytical processes, process one hundred percent of the transaction array, and generate predictive risk assessment models. However, the practical integration of AI into the audit of financial statements is hampered by methodological, legal and ethical barriers, as well as the lack of clear regulatory requirements for the verification of algorithms.
The purpose
of the study is to systematize the directions, assess the effectiveness and limitations of the use of AI in the audit of financial statements, as well as substantiate the conditions under which the integration of innovative technologies into the audit process contributes to improving the quality of audit evidence while maintaining the independence and responsibility of the auditor. The paper uses
methods
of theoretical analysis and synthesis of scientific literature, regulatory documents and practical situations of implementation of AI solutions by leading audit organizations. A system-functional approach has been implemented to classify the levels of analytics, as well as a risk-based method for assessing potential threats of algorithmic bias, a «black box» and the weakening of professional skepticism.
The theoretical basis
of the research was the works of foreign and domestic scientists in the field of auditing and its digital transformation, as well as research on statistical methods for detecting distortions (Benford’s law). The International Standards on Auditing (ISA 500, 570, 230) are used as the regulatory framework.
The results of the study
are to determine the levels of analytics (descriptive, diagnostic, predictive, prescriptive) and the corresponding technological stack. The key audit procedures subject to automation are systematized, it is shown that AI transforms the professional judgment of the auditor, increasing its evidence, but creating the risk of «automated trust». The groups of implementation effects and risk categories are highlighted. It has been established that the system-forming factor of efficiency is not the degree of automation, but a balanced combination of algorithmic tools and professional skepticism, ensured by continuous verification of models and the development of the regulatory framework.
I. Vetrova, Yu. I. Plottseva· Accounting Analysis Auditing· 0 citations
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