International Journal of Scientific Research and Engineering Development

This paper investigates the application of machine learning (ML) techniques for forecasting Key Performance Indicators (KPIs) within finance and operations teams. In the face of increasingly complex business environments and the growing need for real-time, data-driven decisions, accurate KPI forecasting is essential for enhancing operational efficiency and driving profitability. We explore the effectiveness of various machine learning models in predicting critical KPIs, such as revenue growth, operational efficiency, and cost management. By integrating supervised learning algorithms with domain-specific financial and operational data, we propose an approach that improves forecasting accuracy and enables actionable insights. This research emphasizes the role of machine learning in enhancing traditional forecasting methods, offering real-time predictions that empower finance and operations teams to make proactive, informed decisions. Our findings underline the importance of seamlessly incorporating ML tools into business processes for optimized performance and better alignment with organizational goals. Ultimately, the application of machine learning for KPI forecasting proves to be a valuable asset in the dynamic business landscape, helping teams stay ahead of market trends and improve decision-making across various functions.