AI in Finance: Challenges, Techniques and Opportunities

TL;DR

This paper provides a comprehensive review of AI applications in finance, highlighting historical challenges, techniques, and future opportunities across decades of research and practice.

Contribution

It offers a dense roadmap of AI research in finance, categorizing techniques, analyzing challenges, and discussing future directions in a comprehensive manner.

Findings

Categorization of AI techniques used in finance

Analysis of classic vs. modern AI approaches

Identification of open issues and future opportunities

Abstract

AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.