A Rumsfeldian Framework for Understanding How to Employ Generative AI Models for Financial Analysis

Abstract

This paper explores the application of generative artificial intelligence (AI) models in financial analysis within the Rumsfeldian framework of “known knowns, known unknowns, and unknown unknowns.” It highlights the advantages of using AI models, such as their ability to identify complex patterns, automate processes, and enhance decision-making in the financial sector. However, it also addresses the uncertainties associated with generative AI, including accuracy concerns and ethical considerations. By acknowledging the known unknowns and unknown unknowns, the Rumsfeldian framework provides a comprehensive approach to understanding of the opportunities and challenges of AI applications within the financial services sector. The paper discusses the strengths and weaknesses of popular AI models, such as ChatGPT, Bard AI, and Bing AI, and their potential impact on financial analysis. Strategies are offered for mitigating drawbacks, including ensuring accuracy and transparency, addressing data quality, and navigating ethical and compliance considerations. Furthermore, emphasis is placed on the importance of integrating human expertise with AI to achieve accurate and reliable results in financial analysis. Overall, this research contributes to the understanding of the evolving landscape of AI in finance and guides financial professionals in making informed decisions about using generative AI models even in a world of known and unknown unknowns.

Keywords: artificial intelligence, Rumsfeldian framework, financial services, risk mitigation, financial analysis

JEL Classification: G10, G17, G18, G24, G29