It is suggested that AI enhances fraud detection, risk assessment, and audit quality, while raising concerns regarding algorithmic bias, transparency, and professional skepticism.
Abstract
This study examines artificial intelligence (AI) in external auditing by synthesizing existing evidence, clarifying key concepts, identifying theoretical and methodological gaps, and outlining future research directions. A systematic literature review and bibliometric analysis were conducted on 130 peer-reviewed articles retrieved from Scopus and Web of Science databases. The review followed the PRISMA 2020 guidelines, while VOSviewer was used to map research trends, and thematic clusters. Research on AI in auditing has grown substantially, with the United States, China, and the United Kingdom leading scholarly contributions. The analysis identified three dominant research streams: machine learning and fraud detection, audit analytics and big data, and AI adoption and governance. Commonly applied AI techniques include machine learning, neural networks, natural language processing, robotic process automation, and expert systems. The study suggested that AI enhances fraud detection, risk assessment, and audit quality, while raising concerns regarding algorithmic bias, transparency, and professional skepticism. The study develops an integrated framework linking AI applications to audit quality and provides a research agenda to guide future inquiry. The findings offer practical insights for auditors, regulators, and organizations seeking to implement AI responsibly and effectively in audit processes.
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
The findings indicate that XAI is mainly applied to fraud detection, credit assessment, financial auditing, and decision-support processes, with a predominance of techniques such as SHAP and LIME.
Iván Patricio Arias-González, Gabriela Serrano-Torres, Eduardo Ramiro Dávalos-Mayorga et al.· Frontiers in Artificial Inte...· 0 citations
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
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.
Usman Shahbaz, A. Beheshti, B. Abedin et al.· ACM Computing Surveys· 0 citations
This study reviews the development and application of artificial intelligence (AI) and big data analytics (BDA) for fraud detection in accounting and auditing. The review adopts a systematic literature review approach guided by PRISMA principles and synthesizes 20 peer-reviewed and scholarly sources covering data mining, machine learning, natural language processing, deep learning, audit analytics, and big data. The literature indicates that AI and BDA extend fraud detection from periodic, sample-based procedures toward continuous, risk-oriented analysis of large volumes of structured and unstructured data. Machine learning methods, including logistic regression, support vector machines, decision trees, ensemble methods, neural networks, and deep learning, are increasingly used to classify suspicious observations and identify nonlinear fraud patterns. BDA strengthens these models by integrating financial ratios, transaction records, audit evidence, textual disclosures, management commentary, and external information. The review also identifies persistent challenges involving class imbalance, data quality, explainability, privacy, model bias, cybersecurity, and auditor competencies. Overall, the evidence suggests that AI and BDA are most effective when deployed as decision-support mechanisms that complement professional skepticism and audit judgment rather than replace them. Future research should emphasize multimodal data integration, explainable AI, real-time analytics, robust validation across jurisdictions, and governance frameworks for responsible AI-enabled accounting and auditing.
Rosiana Ramadhon, Emmarani Nuristya, Batista Sufa Kefi et al.· Journal of Creative Power a...· 0 citations
