Machine Learning for Business Forecasting: How AI is Reshaping Predictive Analytics in Financial Organisations

Financial organisations have long relied on statistical forecasting to support planning and risk management, but the arrival of machine learning has materially changed what is achievable and, more importantly, what is expected. This paper examines how ML-based predictive analytics has been adopted across core business forecasting functions in financial services — credit risk, revenue forecasting, fraud detection, and market volatility estimation — and what this shift means in practice for how decisions are made. Drawing on a review of empirical studies published between 2018 and 2024, the paper identifies consistent performance advantages of ML over traditional statistical baselines, along with a set of organisational and interpretability challenges that complicate adoption. A particular focus is placed on the gap between model performance in controlled evaluation settings and actual decision-making impact in production environments. The paper concludes that ML has become the dominant paradigm for financial forecasting not simply because it is more accurate, but because the volume, velocity, and variety of data that financial organisations now handle has made traditional approaches structurally inadequate.
Keywords: machine learning, business forecasting, predictive analytics, financial services, credit risk, fraud detection, business intelligence, decision support.