Adaptation of Accounting and Auditing to the Challenges of the Digital Economy: the Impact of Artificial Intelligence

Abstract

The article is devoted to the study of the adaptation of the accounting and auditing system to the challenges of the digital economy under the influence of artificial intelligence technologies. The subject of the research is defined as the impact of digitalization and artificial intelligence on accounting and auditing practices. The aim of the study is to substantiate the directions of transformation of accounting and auditing activities in order to enhance their efficiency and reliability in the context of the rapid development of artificial intelligence. The study employs methods of comparative and systems analysis, statistical generalization, as well as expert evaluation of current trends. Auditing activities undergo significant changes due to the capability to process 100 % of accounting transactions instead of traditional sample‑based testing. The use of machine learning and big data analytics facilitates the detection of anomalies, atypical transactions, and potential fraud, thereby increasing the accuracy and depth of audits. An illustration of this trend is provided by the Big Four – KPMG Clara, Deloitte Omnia, EY Helix, and PwC Halo – which integrate AI modules for the automated execution of analytical procedures and the generation of reports on potentially problematic areas. Simultaneously, the need for «algorithm auditing» is growing – which entails verifying the correctness of models, identifying biases, and assessing compliance with regulatory requirements, particularly in the context of the adoption of the European AI Act. The key challenges have been identified – data quality, cybersecurity, algorithmic transparency, the need to retrain models, and overcoming resistance to change among personnel. The conducted analysis indicates that the successful implementation of AI in accounting and auditing requires a comprehensive approach that combines technological infrastructure, the updating of internal policies, the development of competencies, and the establishment of control systems for automated processes. It is proposed that educational programs integrate training in data management, basic programming, and the principles of AI system operation, while professional organizations should refine accounting and auditing standards to accommodate automated tools. Promising directions for further research include the development of methodologies for assessing the economic efficiency of AI implementation, the study of behavioral aspects of decision‑making involving algorithms, and the examination of legal dilemmas related to liability for AI errors in financial reporting.