A Comprehensive Review of Generative AI in Finance

David Kuo Chuen Lee, Chong Guan, Yinghui Yu and Qinxu Ding ()
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David Kuo Chuen Lee: School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
Chong Guan: SUSS Academy, Singapore University of Social Sciences, Singapore 408601, Singapore
Yinghui Yu: School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
Qinxu Ding: School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore

FinTech, 2024, vol. 3, issue 3, 1-19

Abstract: The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

Keywords: generative AI; large language models; finance; topic modeling; BERTopic (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2024
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