ARTIFICIAL INTELLIGENCE IN FINANCIAL AUDITING: RISKS AND BENEFITS

Abstract

The application of artificial intelligence [AI] in financial statement auditing represents a significant change in modern auditing practices. Traditional audit procedures, which rely on manual document analysis and statistical techniques, are increasingly being supplemented or replaced by AI-based systems. These systems use machine learning algorithms and big data analytics to improve efficiency, increase accuracy, and detect irregularities in financial data. This paper explores the role of AI in financial statement auditing, analyzing its advantages and challenges. The research methodology includes a review of relevant literature, a comparative analysis of traditional and AI-supported audit methods, and case studies of software tools such as IBM Watson Audit Assistant and MindBridge AI Auditor. The results show that AI significantly improves audit efficiency by automating data analysis, reducing human error, and enabling faster fraud detection. AI also allows auditors to analyze 100% of transactions, instead of relying on samples, which increases the reliability and objectivity of audit findings.
In addition, the integration of AI in audit processes changes the role of the auditor. Instead of focusing on routine data verification, auditors now must interpret the results generated by AI systems and ensure their accurate application. This change requires continuous education and adaptation to modern technologies. The development of new software solutions and advanced algorithms also brings certain challenges, including the problem of the “black box” of algorithms, where it is not always clear how AI makes decisions. In addition, the question of ethical and regulatory aspects of the application of AI in finance arises, because the lack of a clear legal framework can affect trust in such systems. Another challenge is data security, because AI systems require access to large amounts of confidential information. Therefore, it is necessary to establish strict data protection standards and ensure compliance with regulations such as the GDPR (General Data Protection Regulation). The successful implementation of AI in auditing depends on the balance between automation and human control, where auditors will take on a more strategic role in interpreting results and making decisions. The conclusion of the paper is that AI has the potential to improve the audit of financial statements by increasing efficiency and accuracy, but successful implementation depends on resolving technological and regulatory obstacles. Future research should focus on developing ethical frameworks for AI and improving algorithmic transparency to build trust in automated audit processes. It is also necessary to further explore how audit firms can integrate AI into their processes in a way that improves audit quality while preserving the professional integrity of auditors. The first part of the paper describes how the authors obtained the data for this paper and explains how the same data were systematized. In the second part of the paper, the authors write about the advantages of applying artificial intelligence in auditing, while the third part of the paper talks about the challenges that auditors face when applying artificial intelligence, as well as the risks that the application of artificial intelligence in auditing entails.