Artificial intelligence application in auditing

Abstract

Artificial intelligence (AI) is a rapidly evolving technology that has gained prominence in various industries, including auditing. AI enhances the effectiveness of auditors by automating routine tasks and improving data analysis. Major audit companies, such as EY and PwC, have integrated AI into their practices to save time, increase accuracy, and provide better services to clients. AI applications in auditing include anomaly detection, fraud prevention, revenue analysis, risk assessment, and financial data analysis. Machine learning algorithms, a subset of AI, play a crucial role in analyzing large volumes of financial data, identifying patterns, and making predictions.AI implementation in auditing involves different stages, including pre-planning, planning, contracting, control risk assessment, and substantive tests. The benefits of AI for auditors and clients include improved data analysis, reduced human error, increased efficiency, and enhanced audit quality. However, the successful implementation of AI in auditing requires a clear understanding of its strengths, limitations, and challenges, as well as interdisciplinary collaboration and the development of specialized frameworks.AI has the potential to transform audit procedures, but human expertise and understanding are still essential for its proper use. It is important to recognize that while AI can automate certain tasks, it cannot replace auditors’ judgment and critical thinking skills.There are also challenges associated with AI implementation in auditing. These include the need for extensive data preparation, ensuring data quality and integrity, managing complex algorithms, and addressing regulatory and compliance issues. Furthermore, auditors must stay updated with the latest advancements in AI and continuously adapt their skills to leverage the technology effectively.
 JEL classification: M420