Artificial Intelligence Authoring Financial Recommendations: Comparative Australian Evidence

Article

Neilson, Ben. 2023. "Artificial Intelligence Authoring Financial Recommendations: Comparative Australian Evidence." Journal of Financial Regulation. 9 (2), pp. 249-257. https://doi.org/10.1093/jfr/fjad004

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Article Title	Artificial Intelligence Authoring Financial Recommendations: Comparative Australian Evidence
ERA Journal ID	210875
Article Category	Article
Authors	Neilson, Ben
Journal Title	Journal of Financial Regulation
Journal Citation	9 (2), pp. 249-257
Number of Pages	9
Year	2023
Publisher	Oxford University Press
Place of Publication	United Kingdom
ISSN	2053-4833
	2053-4841
Digital Object Identifier (DOI)	https://doi.org/10.1093/jfr/fjad004
Web Address (URL)	https://academic.oup.com/jfr/article/9/2/249/7163206
Abstract	This article explores the use of artificial intelligence software to create financial recommendations. It seeks to define the impacts and outline the benefits of using ChatGPT software in the highly regulated Australian financial sector. ChatGPT was asked to create a financial recommendation, which was evaluated against the relevant regulations and the recommendations of existing professionals and market contributors. The data generated was analysed using Qualtrics analysis techniques. The findings suggest that ChatGPT significantly reduces the time required to carry out creation, input, and editing services for simple financial advice recommendations. However, ChatGPT fails to operate effectively with complex financial advice and requires professional guidance to ensure regulatory adherence. In future, it may be possible to develop ChatGPT as a complementary tool to decrease the time and cost required to create financial recommendations.
Keywords	ChatGPT; artificial intelligence; regulation; financial recommendation
Is part of	Addressing Barriers To Accessing Financial Advice: Identifying Benefits Through Process Improvements
ANZSRC Field of Research 2020	3899. Other economics
Public Notes	This article is part of a UniSQ Thesis by publication. See Related Output.
Byline Affiliations	University of Southern Queensland

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