AI in Finance: Challenges, Techniques, and Opportunities
By Longbing Cao. ACM Computing Surveys, 2021
436 citations recorded by the source corpus.
AI in finance refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades, with both classic and modern AI techniques applied to increasingly broader areas of finance, economy, and society. In contrast to reviews on discussing the problems, aspects, and opportunities of finance benefited from specific or some new-generation AI and data science (AIDS) techniques or the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance over the past decades. The challenges of financial businesses and data are first outlined, followed by a comprehensive categorization and a dense overview of the decades of AIDS research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. A comparison, criticism, and discussion of classic versus modern AIDS techniques for finance follows. Finally, the open issues and opportunities to address future AIDS-empowered finance and finance-motivated AIDS research are discussed.
Subfields: Accounting, Management Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/6a85586fb2c87f303f2da32ba5f1863db2a31072
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
In Management Information Systems
In Management Science and Operations Research
Citations in the corpus
- Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “The success of these techniques and their application in critical areas, such as finance [9] and healthcare [10], among others, has made it necessary to understand these models’ underlying mechanisms and their often opaque outputs.”