Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics

Abstract

The transition from traditional rule-based forecasting systems to machine learning (ML) approaches represents a fundamental shift in enterprise financial planning methodologies. This review examines the evolution of financial forecasting techniques, analyzing how ML algorithms have transformed predictive analytics in corporate environments. Traditional rule-based models, while offering interpretability and deterministic outputs, often struggle with complex non-linear patterns and dynamic market conditions. In contrast, ML methods including deep learning (DL), ensemble techniques, and hybrid models demonstrate superior performance in capturing intricate relationships within financial data. This paper synthesizes recent literature on ML applications in enterprise financial forecasting, evaluating methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, gradient boosting machines, and transformer architectures. The review explores implementation challenges including data quality requirements, model interpretability concerns, regulatory compliance, and organizational change management. Empirical evidence suggests that ML-based forecasting systems can achieve accuracy improvements of 15-40% compared to traditional approaches, though success depends heavily on data infrastructure, talent capabilities, and strategic integration. The paper concludes by identifying emerging trends including explainable artificial intelligence (AI), automated machine learning (AutoML), and federated learning approaches that address current limitations while maintaining the predictive advantages of ML systems.