Abstract

Digital transformation over the past decade has profoundly reshaped the accounting profession and paved the way for the emergence of digital accounting. With the growing volume and complexity of financial data, the need for intelligent tools for financial processing, analysis, and decision-making has become increasingly evident. In this context, the present study aims to examine the application of artificial intelligence in digital accounting and analyze its impact on the accuracy, speed, and quality of financial reporting through a proposed AI model.

In this research, the theoretical literature was first reviewed to explain the role of machine-learning–based technologies, neural networks, and natural language processing in enhancing accounting systems. Subsequently, a proposed algorithmic AI model was designed in which financial data, after preprocessing, were analyzed using hybrid algorithms such as XGBoost and Isolation Forest to identify anomalous patterns. The findings indicate that the use of these models significantly increases the accuracy of detecting financial errors and fraud while reducing information-processing time.

The results further suggest that the successful implementation of artificial intelligence in accounting—beyond technological infrastructure—requires training digital accountants, establishing data-security policies, and fostering trust in interpretable algorithms.

Overall, artificial intelligence should be viewed not as a replacement for accountants, but as a powerful tool for enhancing efficiency, transparency, and intelligent decision-making in the digital financial system.