Artificial Intelligence and Predictive Analytics in Financial Reporting: Enhancing the Accuracy, Reliability, and Timeliness of Corporate Financial Decisions

Abstract

Artificial intelligence (AI) and predictive analytics are increasingly transforming corporate financial reporting by enabling automated data processing, anomaly detection, financial forecasting, and real-time analysis. This study examines how these technologies can enhance the accuracy, reliability, and timeliness of financial information while strengthening corporate financial decision-making. Drawing on existing literature, the study explores AI applications in accounting information systems, predictive financial modelling, automated auditing, and risk assessment. Previous research indicates that AI-driven forecasting and analytical techniques can improve the identification of financial irregularities, support proactive risk management, and facilitate timely, evidence-based decisions. However, their effectiveness depends on data quality, technological infrastructure, model interpretability, regulatory compliance, and appropriate human oversight. The study highlights the importance of integrating predictive analytics with robust internal controls and transparent AI governance to ensure credible financial reporting. It further identifies the need for empirical research examining the long-term organizational effects of AI adoption across different corporate contexts. The study concludes that responsible AI implementation can strengthen financial reporting quality and support more informed, efficient, and forward-looking corporate financial decisions.