The Impact of Artificial Intelligence Technology on Enhancing the Efficiency of Auditing Processes and Increasing Financial Transparency

Abstract

This study was conducted with the aim of investigating the innovations of artificial intelligence (AI) technology in improving auditing processes and enhancing financial transparency. Given the increasing complexity of data and financial reports, the application of advanced machine learning algorithms and natural language processing enables more accurate analysis and the automation of repetitive auditing tasks. Utilizing an analytical-applied approach and relying on valid financial data, this study employed algorithms such as decision trees, random forests, artificial neural networks, and gradient boosting to detect financial fraud and assess the transparency of auditing processes. The principal innovation of this research lies in the integration of multiple AI algorithms and the application of advanced feature selection methods (based on variance, correlation, and mutual information), which led to increased precision and sensitivity in fraud detection and transparency analysis. The results indicate that the random forest algorithm achieved an accuracy rate of 92%, and the gradient boosting algorithm attained an accuracy of 95%, demonstrating outstanding performance in fraud identification. Meanwhile, artificial neural networks, with an accuracy of 90%, succeeded in detecting more complex patterns, although they were accompanied by computational challenges. This study emphasizes that AI, as an innovative technology, plays a key role in enhancing financial transparency and trust in audit reports by reducing human errors and increasing efficiency. However, it also requires high-quality data and the development of methods for interpreting results.