The Use of Predictive Analytics in Financial Forecasting and Decision-Making

This study investigates The Use of Predictive Analytics in Financial Forecasting and Decision-Making. Survey design was adopted and used in the study. Sample size of 150 was used and the data collected were analysed using 4-point Likert rating. From the analysis of the data collected, Findings reveal that predictive analytics significantly enhances financial forecast accuracy, reduces errors, and supports timely, data-driven decision-making. Respondents strongly agree that these tools improve consistency, increase stakeholder confidence, and enable early risk identification and better resource allocation. However, the study also highlights key implementation challenges, including the shortage of skilled personnel, high costs, system integration difficulties, regulatory constraints, and infrastructural inadequacies. Despite these limitations, machine learning and AI are recognized as transformative forces in modern financial forecasting, offering improved adaptability, efficiency, and deeper insights through advanced data processing and scenario analysis. The research concludes that while predictive technologies offer substantial benefits, addressing implementation barriers is essential to fully realize their potential in financial decision-making. Based on these findings, the study recommends to maximize the potential of predictive analytics and AI in financial forecasting, organizations should invest in capacity-building initiatives to upskill financial and technical staff. There is also a need for strategic infrastructure upgrades to support real-time analytics capabilities. Policymakers and regulatory bodies should consider developing adaptive compliance frameworks that support innovation while safeguarding financial integrity. Keyword: Predictive analytics in finance,Financial forecasting using AI,Machine learning for financial decision-making,Data-driven financial forecasting,AI and predictive models in finance