Jul 2026· Journal of Creative Power and Ambition (JCPA)· Vol 4, pp. 803-814· 0 citations· 22 references
TL;DR
This study aims to systematically identify, review, and synthesize the role of statistical concepts in AI development for auditing, including their implementation, benefits, challenges, and future directions.
Abstract
Artificial Intelligence (AI) has increasingly been adopted in auditing, however the role of statistical methods in improving AI performance and reliability remains fragmented. This study aims to systematically identify, review, and synthesize the role of statistical concepts in AI development for auditing, including their implementation, benefits, challenges, and future directions. A Systematic Literature Review (SLR) following the PRISMA framework was conducted on 149 studies published between 2017 and 2026 from Google Scholar, Scopus, and SciSpace. The findings reveal a significant increase in AI auditing research since 2023. Regression analysis, hypothesis testing, and Bayesian inference are the most frequently applied statistical methods, supporting fraud detection, risk assessment, audit sampling, anomaly detection, and model validation. Integrating statistical methods with AI improves prediction accuracy, interpretability, transparency, and audit quality. However, challenges remain regarding data quality, model interpretability, auditor competency, and AI governance. Future research should prioritize hybrid AI-statistical models, Explainable AI, and adaptive Bayesian approaches to enhance trustworthy data-driven auditing
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.
Explainable Artificial Intelligence (XAI) has emerged as a response to the need to understand and make transparent the decisions of machine learning models, particularly in sensitive contexts such as accounting and financial auditing. In this domain, XAI enables the interpretation of results generated by automated systems applied to fraud detection, risk management, financial analysis, and regulatory compliance, thereby strengthening the trust of auditors and regulators. The objective of this study was to systematically analyze the literature on XAI in accounting and financial auditing in order to identify its application domains, the methods employed, and the main challenges reported. The research was conducted through a systematic literature review following the PRISMA protocol, based on studies retrieved from Scopus and Web of Science. The selected works were organized and synthesized using an analysis matrix, resulting in 85 primary studies. 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. Although these tools enhance transparency, limitations related to computational cost, data quality, explanation stability, and regulatory adaptation persist, highlighting the need to strengthen their integration into auditing processes. Systematic review registration https://osf.io/pb5cy/.
Iván Patricio Arias-González, Gabriela Serrano-Torres, Eduardo Ramiro Dávalos-Mayorga et al.· Frontiers in Artificial Inte...· 0 citations
The integration of artificial intelligence (AI) into auditing has created a paradigm shift, presenting both unprecedented opportunities to enhance audit quality and significant policy challenges that threaten the foundations of professional judgment. This systematic literature review analyses peer-reviewed articles to synthesize the current landscape of AI in auditing and identify the primary policy challenges confronting the profession. Our analysis reveals a fundamental tension between the automation of audit tasks and the preservation of professional skepticism and judgment. Key themes emerging from the literature include the paradox of professional judgment in an automated environment, the double-edged sword of AI in enhancing audit quality while introducing new risks, the critical need for transparency and explainability in AI systems, the pervasive threat of algorithmic bias, and the significant gaps in regulatory frameworks and professional standards. The findings indicate that while AI offers powerful tools for data analysis, fraud detection, and risk assessment, its adoption is hampered by a complex web of ethical, technical, and organizational barriers. The primary policy challenges identified include regulatory lag, the erosion of professional identity, new quality assurance demands, evolving competency standards, the need for robust ethical frameworks, unresolved liability issues, and a lack of standardization. This review concludes 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. I propose a research agenda focused on the long-term effects of AI on professional judgment, the effectiveness of governance models, and the development of new audit methodologies that effectively integrate human and machine intelligence.
Geoffrey Odoch· International journal of com...· 0 citations
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.
Usman Shahbaz, Amin Beheshti, B. Abedin et al.· ACM Computing Surveys· 0 citations
The increasing complexity of public financial transactions, rising accountability expectations, and
limitations associated with conventional audit approaches have created the need for innovative
technologies capable of improving auditing effectiveness. Artificial Intelligence (AI) has emerged
as a transformative technology with significant potential to reshape auditing practices through
automation, advanced data analytics, predictive modelling, and intelligent decision support. This
study examines the influence of Artificial Intelligence adoption on auditing practice within
Nigeria’s public sector, focusing on its effects on audit efficiency, fraud detection capability, and
audit quality. The study adopted a quantitative research approach using a structured
questionnaire administered to public sector auditors and accounting professionals. Data were
analysed using descriptive statistics, Pearson correlation, and multiple regression analysis. The
findings revealed that Artificial Intelligence adoption has a significant positive influence on audit
efficiency, fraud detection capability, and audit quality. The regression results showed that AI
adoption significantly improves audit outcomes by enabling faster data analysis, enhancing risk
identification, and strengthening the reliability of audit evidence. The study further revealed that
while AI provides substantial opportunities for improving public sector accountability, challenges
such as inadequate digital infrastructure, limited technological competencies, data governance
concerns, and ethical issues may constrain effective implementation. The study concludes that
Artificial Intelligence should be viewed as an enabling technology that complements professional
auditing judgment rather than replacing human auditors. It recommends strategic investment in
digital audit infrastructure, continuous AI-related capacity development for auditors, and the
establishment of appropriate governance frameworks to support responsible AI adoption within
Nigeria’s public sector auditing environment.
S. J. Inyada, Sule Joseph· WORLD JOURNAL OF INNOVATION...· 0 citations
This critical review aims to analyze the article by Baldwin, Brown, and Trinkle (2006) titled Opportunities for Artificial Intelligence Development in the Accounting Domain: The Case for Auditing. The article discusses the opportunities for developing Artificial Intelligence (AI) in the accounting field, particularly in auditing and assurance processes. The method used in this review is a literature study with a descriptive-analytical approach to the article's content, covering research objectives, methods, main findings, contributions, as well as the strengths and limitations of the article. The review results show that auditing is a very potential field for AI application because it involves analyzing large amounts of data, complex decision-making, professional judgment, and anomaly and fraud detection. The authors of the article emphasize that AI is not intended to replace auditors, but rather serves as a tool to enhance efficiency, effectiveness. and the quality of audits. Although this article has limitations because it is conceptual and not yet supported by empirical evidence, the review provides an important contribution as an initial theoretical foundation for research on AI in auditing. Thus, this article remains relevant to be used as a reference in research on audit analytics, continuous auditing, fraud detection, and the use of AI technology in the accounting profession.
Muslimin, Nahwani Fadelan, Wahid Hasyim et al.· Journal of Creative Power a...· 0 citations
