AI-Driven Financial Forecasting

Abstract

In today's dynamic and complex financial landscape, accurate and reliable financial forecasting is crucial for businesses to make informed decisions, manage risks, and achieve long-term success. Artificial intelligence (AI) has emerged as a transformative force in the financial industry, offering a powerful set of tools and techniques for enhancing financial forecasting capabilities. This chapter delves into the realm of AI-driven financial forecasting, exploring the role of soft computing techniques in harnessing the power of AI for data-driven financial insights. Soft computing, a branch of computational intelligence, encompasses a suite of methodologies that mimic human reasoning and learning processes to handle complex and uncertain data. A comprehensive overview of AI-driven financial forecasting, highlighting the strengths and applications of soft computing techniques in this domain, has been envisaged beginning by introducing the fundamental concepts of AI in financial forecasting, including data pre-processing, feature selection, and model evaluation. It then delves into the specific applications of soft computing techniques in financial forecasting, exploring their use in various forecasting tasks, such as stock price prediction, exchange rate forecasting, and credit risk assessment. The transformative potential of AI-driven financial forecasting, empowered by the power of soft computing techniques, have also been underscored. The effectiveness of soft computing techniques has been shown through real-world examples and case studies, demonstrating their ability to outperform traditional forecasting methods in various financial scenarios. It also discusses the challenges and limitations of using AI in financial forecasting, emphasizing the importance of data quality, model interpretability, and ethical considerations.