AI-Augmented Auditing: Enhancing Accuracy, Coverage, and Predictive Testing in Assurance

Approved

Relevance and Originality

This work engages with a topic of high contemporary relevance, exploring AI-driven auditing within a rapidly evolving regulatory and technological landscape. The originality is found in framing AI adoption as a structural transformation rather than a mere technical enhancement. The integration of multiple AI techniques and their application to predictive testing and continuous assurance enhances the contribution. Some overlap with existing literature is present, but the cohesive conceptual model adds value.

Methodology

The qualitative, interpretive approach is justified and well-executed, providing a synthesis of diverse studies and practical insights. The methodology clearly outlines steps taken to analyze literature and integrate findings. Limitations are acknowledged, particularly regarding secondary data dependence. The framework construction is methodical, yet more detail on how the literature was selected or weighted would improve transparency and reproducibility.

Validity and Reliability

The authors carefully discuss data and methodological limitations, including model bias, data quality challenges, and auditor skill gaps. The cited studies are relevant and contemporary, supporting the reliability of assertions. The discussion of potential risks enhances credibility. However, the paper could strengthen generalizability by including examples from multiple industries or geographies, rather than predominantly literature-based conclusions.

Clarity and Structure

The manuscript maintains a professional tone with logical flow from theoretical background to discussion and implications. Sectioning is clear and appropriate, although some paragraphs contain dense information that may challenge readers less familiar with AI terminology. Figures and tables are informative, and overall language quality is high, promoting reader comprehension. Minor editorial adjustments could further improve conciseness.

Results and Analysis

The analysis effectively interprets AI’s impact on audit accuracy, coverage, and predictive capabilities. The discussion on continuous assurance and auditor transformation provides strong insight into practical implications. Evidence is drawn logically from the reviewed literature. Greater inclusion of comparative data or case examples could strengthen the analytical robustness, but the synthesis of prior studies is thorough and insightful.