Abstract
This review paper explores the role of predictive analytics in financial planning for Human Resources (HR), focusing on expense forecasting. It examines internal and external factors influencing HR expenses, including workforce size, economic conditions, and regulatory changes. The paper discusses methods and techniques in predictive analytics for HR expense forecasting, such as regression analysis, machine learning algorithms, common data sources, and software tools. Challenges in implementation, including data quality issues and ethical considerations, are addressed alongside future trends and opportunities, such as the integration of AI and real-time analytics. By overcoming challenges and embracing emerging trends, organizations can harness the full potential of predictive analytics to optimize HR expenses and drive organizational success. Keywords: Predictive Analytics, HR Expense Forecasting, Data Sources, Analytical Techniques.
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