Document Type

Article

Publication Date

2024

DOI

10.3390/ai5010004

Publication Title

AI

Volume

5

Issue

1

Pages

55-71

Abstract

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is how to apply it to fraud detection. Last but not least, the paper discusses the challenges posed by generative AI, such as the ethical considerations, potential biases, and data security.

Rights

© 2023 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Comments

Data availability statement: Article states: "Not applicable."

Original Publication Citation

Zheng, X., Gildea, E., Chai, S., Zhang, T., & Wang, S. (2024). Data science in finance: Challenges and opportunities. AI, 5(1), 55-71. https://doi.org/10.3390/ai5010004

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