Sarcouncil Journal of Economics and Business Management

Abstract: Financial forecasting is a key part of strategic planning in banking, investment, and corporate finance. Traditional statistical methods often fall short when modeling the complex, interconnected, and ever-changing nature of financial markets. This study explores how machine learning techniques can overcome these limitations through a detailed literature review and comparison of predictive models. It assesses three main machine learning approaches: Lasso regression, gradient boosting machines, and Long Short-Term Memory networks. Each approach is examined for its theoretical basis, forecasting performance, and organizational impact. Two case studies are used to demonstrate real-world applications and results. The first case highlights JP Morgan's successful use of gradient boosting machines and LSTM networks, which improved forecast accuracy by 8-12% and enhanced directional forecasting capabilities. This deployment gave the organization more strategic flexibility and a competitive edge in financial decision-making. The second case analyzes a regional U.S. bank's less effective use of Decision Trees without ensemble techniques or proper validation, resulting in minimal performance improvements and increased risk. The study finds that machine learning models can notably improve prediction accuracy and operational agility, but challenges remain in interpreting models, establishing governance, and deploying in real-time. Significant gaps are identified in explainable AI, causal inference, and scalable operations. The study concludes that machine learning-based predictive analytics could revolutionize financial forecasting. It recommends combining machine learning techniques with traditional econometric models within a strong data infrastructure and governance framework.