Artificial intelligence in financial statement preparation: Enhancing accuracy, compliance, and corporate performance

Abstract

This study investigates the integration of Artificial Intelligence (AI) into financial statement preparation and its impact on accuracy, compliance, and corporate performance. The research aims to provide insights into how AI-driven financial reporting systems enhance efficiency, fraud detection, and regulatory adherence. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The study synthesizes empirical findings from indexed databases, analyzing AI applications such as Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) in financial reporting. The results indicate that AI-powered financial reporting significantly improves the accuracy and timeliness of financial disclosures, strengthens corporate governance, and enhances decision-making capabilities. AI-based fraud detection models outperform traditional auditing techniques, achieving higher accuracy and efficiency. The study also highlights key challenges, including concerns over algorithmic transparency, data privacy, and the cost of AI implementation, particularly for SMEs. AI has the potential to revolutionize financial statement preparation, improving regulatory compliance and corporate performance. However, challenges related to ethical considerations and cost barriers must be addressed to maximize AI’s benefits in financial reporting. The findings provide strategic insights for regulators, financial professionals, and policymakers to optimize AI adoption while ensuring compliance and accountability. Future research should focus on explainable AI models, long-term governance impacts, and regulatory frameworks for AI-driven financial reporting.