Data Science in Finance: Challenges and Opportunities

Author:

Zheng Xianrong1,Gildea Elizabeth2,Chai Sheng3,Zhang Tongxiao4,Wang Shuxi5

Affiliation:

1. Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA

2. School of Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA

3. School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA

4. School of Computer and Communication Engineering, Northeastern University at Qinghuangdao, Qinghuangdao 066004, China

5. Department of Artificial Intelligence, University of International Business and Economics, Beijing 100029, China

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.

Publisher

MDPI AG

Subject

Industrial and Manufacturing Engineering

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