Abstract

Artificial Intelligence in finance largely encompasses the utilization of Artificial Intelligence based techniques within economic enterprises. This field has attracted interest for many years, as evidenced by the general application of traditional AI techniques—like decision trees and linear regression—and modern techniques—like deep learning and reinforcement learning—across progressively larger spheres of the economy, society, and finance. Rather than just listing specific issues, features, and prospects in finance that have profited from these specific AI techniques—especially those arising from the fields of new-generation AI and data science (AIDS)—this review attempts to offer an extensive and detailed road map of the challenging obstacles, methods, and prospects encountered in AI research in finance over the last few decades. The review begins by describing the circumstances and difficulties specific to financial data and processes. After that, it provides a thorough classification and concise overview of the development of AI research in finance over the previous few decades. This comprehensive classification and synopsis ought to engender trust in the data provided. It then organises and clarifies the processes of learning and data-driven analytics in financial operations. There is a comparison, analysis, and discussion of traditional versus contemporary AI methods designed for the banking industry. Finally, the review takes a forward-thinking approach by delving into the open issues and opportunities that are balanced to shape the trajectory of future AI-empowered finance and the intersection of finance-driven AI research. This exploration of future opportunities should inspire you to think ahead and consider the potential of AI in finance.