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AI Summary of Peer-Reviewed Research

This page presents an AI-generated summary of a published research paper. The original authors did not write or review this article. [See full disclosure ↓]

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AI is increasingly integrated into audit workflows

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A person in dark clothing viewed from behind sits at a white table in a modern office, typing on a laptop displaying a blue data dashboard interface, with chairs and office furniture visible in the soft-focused background.
Research area:Business, Management and AccountingAuditing, Earnings Management, GovernanceAccounting

What the study found

The review found that artificial intelligence (AI) methods are increasingly being used in audit workflows, especially for anomaly detection, predictive analytics, document analysis, and automation. It also identified a proposed AI-based audit workflow reference architecture and noted that adoption is still limited by technical, organizational, and regulatory obstacles.

Why the authors say this matters

The authors conclude that the findings have practical implications for auditors, standard-setters, and system designers who may need to revise audit approaches and regulations to support AI-driven assurance. The study suggests that understanding both opportunities and obstacles is relevant for wider adoption.

What the researchers tested

The researchers conducted a systematic review of 100 peer-reviewed articles published from 2015 to 2025 on AI in auditing, including machine learning, natural language processing (NLP), and robotic process automation (RPA). They searched five large databases and other sources, used PRISMA selection procedures, extracted structured data, assessed quality, and performed narrative and thematic analysis.

What worked and what didn't

The review reports that machine learning-based anomaly detection and predictive analytics, NLP-based document analysis, and RPA-based automation are being incorporated into planning, risk assessments, control tests, and substantive procedures/reporting. Various empirical and design science studies reported improvements in detection capabilities, coverage, and efficiency. The paper also describes common architectural models for AI-enabled audit processes, including layered data and governance, model development and oversight, orchestration and automation, auditor-facing applications, and human-in-the-loop controls.

What to keep in mind

The abstract notes gaps in longitudinal assessment, comparative evaluation of AI methods, and regulatory recommendations. It also states that more widespread adoption is still limited, but does not provide further limitations beyond the scope of the reviewed literature.

Key points

  • The review analyzed 100 peer-reviewed articles on AI in auditing from 2015 to 2025.
  • Machine learning, NLP, and RPA were the main AI methods discussed.
  • Reported benefits included better detection, broader coverage, and higher efficiency.
  • AI applications were linked to planning, risk assessment, control tests, and reporting tasks.
  • The paper proposes an AI-based audit workflow reference architecture.
  • The abstract identifies gaps in long-term evaluation, method comparison, and regulatory guidance.

Disclosure

Research title:
AI is increasingly integrated into audit workflows
Authors:
Ashif Anwar, Muhammad Osama Akeel
Institutions:
University of Manchester, Wolters Kluwer (Netherlands), Wolters Kluwer Health
Publication date:
2026-03-09
DOI:
10.3390/accountaudit2010004
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.