AI-Driven Financial Forecasting: Enhancing Predictive Accuracy in Volatile Markets

Abstract

In an era marked by increasing market volatility and economic uncertainty, traditional financial forecasting models often struggle to deliver accurate and timely predictions. Artificial Intelligence (AI), with its capacity for advanced data processing, pattern recognition, and real-time learning, has emerged as a transformative tool in financial forecasting. This paper investigates the application of AI-driven techniques—including machine learning, deep learning, and natural language processing—in enhancing predictive accuracy across volatile market environments. By analyzing recent advancements and empirical studies, the research demonstrates how AI models outperform conventional statistical methods in adapting to rapid market fluctuations, extracting insights from unstructured data, and improving risk assessment. The paper also explores the challenges of overfitting, interpretability, and ethical implications associated with AI in finance. Through a comprehensive review, it provides a roadmap for future research and practical implementation strategies for financial institutions aiming to harness AI's potential.