Integration of Artificial Intelligence in Accounting Information Systems to Improve the Accuracy and Efficiency of Financial Reporting

Abstract

AI integration supports the automation of complex accounting processes such as transaction classification, anomaly detection, and predictive analytics. This study aims to analyze the integration of Artificial Intelligence (AI) in Accounting Information Systems (AIS) to improve the accuracy and efficiency of financial reporting. The research employs a Systematic Literature Review (SLR) method by collecting, selecting, and analyzing relevant peer-reviewed articles from reputable databases such as Scopus, Web of Science, and Google Scholar, published between 2015 and 2025. The selected studies are synthesized thematically to identify key patterns, benefits, and challenges related to the implementation of AI in accounting practices. The results indicate that AI significantly enhances the accuracy of financial reporting by reducing human errors, improving anomaly detection, and strengthening internal controls, while also increasing efficiency through automation of routine tasks, real-time data processing, cost reduction, and advanced analytical capabilities. However, the study also highlights challenges such as data security risks, implementation costs, ethical concerns, and workforce transformation. Overall, the findings confirm that AI plays a crucial role in transforming AIS into a more accurate, efficient, and strategic financial reporting system.