Abstract: This research investigates the role of predictive analytics in enhancing the accuracy of financial reporting, with a specific focus on Cogent Innovation Pvt . Ltd. Traditional financial reporting methods often struggle with issues such as manual errors, delays, and inefficiencies, which can compromise the reliability of financial data used for strategic decision-making. To address these challenges, the study adopts a mixed-methods research design, utilizing both quantitative surveys and qualitative interviews conducted with financial analysts, industry professionals, and AI specialists. The collected data was analysed using SPSS software to derive statistically significant insights. The findings indicate that the integration of predictive analytics significantly improves the accuracy and timeliness of financial reporting. It also strengthens risk identification, streamlines compliance efforts, and supports more informed and agile decision-making processes. Nevertheless, the research also identifies persistent barriers to adoption, including concerns about data privacy, the high initial cost of implementation, and resistance to organizational change. Despite these limitations, the study emphasizes the growing relevance of AI-powered solutions in finance and their potential to transform traditional reporting systems. By offering practical recommendations such as investing in training, enhancing data governance, and implementing pilot projects, the study provides a roadmap for businesses aiming to adopt predictive analytics effectively. Ultimately, this research contributes to the broader discourse on financial innovation and offers valuable insights for organizations seeking to improve their financial transparency, accuracy, and strategic agility through advanced data-driven approaches.

Keywords: Predictive Analytics, Financial Reporting, Artificial Intelligence, Data Quality, Risk Management, Decision-Making

| DOI: 10.17148/IARJSET.2025.12520