Data Science in Finance: Challenges and Opportunities

被引:2
|
作者
Zheng, Xianrong [1 ]
Gildea, Elizabeth [2 ]
Chai, Sheng [3 ]
Zhang, Tongxiao [4 ]
Wang, Shuxi [5 ]
机构
[1] Old Dominion Univ, Informat Technol & Decis Sci Dept, Norfolk, VA 23529 USA
[2] Old Dominion Univ, Sch Cybersecur, Norfolk, VA 23529 USA
[3] Northwest Missouri State Univ, Sch Comp Sci & Informat Syst, Maryville, MO 64468 USA
[4] Northeastern Univ Qinghuangdao, Sch Comp & Commun Engn, Qinhuangdao 066004, Peoples R China
[5] Univ Int Business & Econ, Dept Artificial Intelligence, Beijing 100029, Peoples R China
关键词
data science; financial technologies; algorithmic trading; fraud detection;
D O I
10.3390/ai5010004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:55 / 71
页数:17
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