AI-Driven Financial Forecasting Using Neural Network Models

Abstract

Artificial Intelligence (AI) and neural network models have significantly transformed the field of financial forecasting by improving prediction accuracy, automation, and decision-making capabilities. Traditional financial forecasting methods often struggle to handle large-scale, nonlinear, and dynamic financial data. In contrast, AI-driven approaches, particularly deep learning and neural network models such as Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, provide advanced analytical capabilities for predicting financial trends and market behavior. This review paper examines recent developments in AI-based financial forecasting, focusing on the applications of neural network models in stock market prediction, risk management, fraud detection, and investment analysis. The study also highlights the integration of big data analytics and explainable AI techniques in modern financial systems. Furthermore, the paper identifies current research gaps related to data quality, model transparency, computational complexity, and real-time forecasting challenges. Finally, future research directions are proposed to enhance the reliability, interpretability, and scalability of AI-driven financial forecasting systems.