Bias Mitigation and Fairness in AI-Based HR Tools

Forecasting has long served as a cornerstone of strategic decision-making in financial services. Traditionally grounded in econometric models, statistical inference, and time series analysis, financial forecasting has been employed to anticipate market movements, project economic trends, and guide investment strategies. Early models such as the Autoregressive Integrated Moving Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), and Vector Autoregressions (VAR) formed the bedrock of quantitative finance, offering structured approaches to interpreting historical data and identifying trends. These methods, while rigorous, often rely on assumptions of linearity, stationarity, and normality that may not hold in complex, volatile, and non-linear market environments. The increasing complexity of global financial systems, proliferation of real-time data, and interconnected markets have exposed limitations in traditional forecasting methodologies. As a result, there has been a growing demand for more adaptive, robust, and data-intensive approaches that can respond dynamically to rapid market fluctuations and hidden patterns within large datasets. This paradigm shift has paved the way for integrating artificial intelligence (AI) and machine learning (ML) into financial forecasting processes.