Abstract

Artificial Intelligence (AI) is increasingly transforming internal control mechanisms and enhancing financial accuracy within organizations. By leveraging machine learning, natural language processing, and predictive analytics, AI enables the automation of monitoring processes, detection of anomalies, and real-time validation of financial data. This study reviews advances in AI applications for internal control, emphasizing their contribution to error reduction, fraud detection, and operational efficiency. Through a systematic analysis of literature, industry case studies, and technological implementations, the study identifies key AI-driven strategies and tools that improve financial integrity. The findings propose a conceptual model integrating AI applications with internal control frameworks, highlighting benefits, challenges, and future research directions for enhancing financial accuracy in corporate settings.