Data Science in Finance: Challenges and Opportunities

AI Pub Date : 2023-12-22 DOI:10.3390/ai5010004
Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang
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引用次数: 0

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.
金融领域的数据科学:挑战与机遇
由于包括生成式人工智能、大数据、深度学习等在内的新兴技术的出现,数据科学变得越来越流行。它可以从数据中提供从人类角度难以确定的见解。金融领域的数据科学有助于为客户提供更个性化、更安全的体验,并为公司开发最前沿的解决方案。本文探讨了将数据科学应用于金融业所面临的挑战和机遇。它对金融技术、算法交易和欺诈检测进行了最新回顾。此外,本文还确定了两个研究课题。一个是如何在算法交易中使用生成式人工智能。另一个是如何将其应用于欺诈检测。最后,本文还讨论了生成式人工智能带来的挑战,如伦理考虑、潜在偏见和数据安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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AI
AI
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