The use of artificial intelligence in independent audit practices

In today's rapidly evolving technological landscape, the integration of emerging the rapid integration of emerging technologies into business processes has become a significant source of competitive advantage for companies. One of the most prominent examples of this transformation is AI technologies. With the help of AI, companies can execute their operations with less labor and cost, in a faster and more efficient manner. In this context, the increasing application of AI not only in operational tasks but also in knowledge-intensive and expertise-based domains such as independent auditing is noteworthy. The aim of this study is to evaluate the applicability of AI technologies in independent audit processes and to examine the impact of this integration on audit quality. Accordingly, the effectiveness and efficiency of conducting key audit stages such as planning, evidence collection, and risk assessment through AI-supported systems were investigated in comparison to traditional audit methods. Within the scope of the research, AI-based audit applications and traditional methods were comparatively analyzed based on both qualitative and quantitative criteria such as processing time, accuracy, and consistency. The study is based on the audit data of 14 companies included in the portfolio of an independent audit firm operating in Antalya for the 2022–2024 period and utilizes the ChatGPT-4o model as its foundation. Findings indicate that AI-supported systems significantly reduce the processing time in audit procedures, while demonstrating certain limitations in terms of accuracy and consistency. However, the study focused only on selected audit processes and observed limitations such as the model's restricted sensitivity to sector-specific terminology and the lack of testing in real-time audit environments. Despite these limitations, it is concluded that AI technologies may provide substantial and structural contributions to audit quality if such constraints are addressed. Furthermore, the study aims to assess the potential contributions of AI components such as large language models, natural language processing, and machine learning to auditing, as well as their impact on the transformation of the profession and evolving competency requirements. The findings thus contribute to existing literature at a theoretical level and offer an innovative perspective on independent audit practices.