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    • Abbreviated Title: J. Econ. Bus. Manag.
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    • DOI: 10.18178/JOEBM
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HOME > Archive > 2026 > Volume 14 Number 3 (2026) >
JOEBM 2026 Vol.14(3): 180-183
DOI: 10.18178/joebm.2026.14.3.933

AI in Financial Applications: Utopia and Reality

Yi Zhou
Yi Zhou
Carey Business School, Johns Hopkins University, Washington, D.C., USA
Email: yzhou286@jh.edu

Manuscript received April 30, 2026; accepted May 30, 2026; published September 2, 2026.

Abstract—Generative AI, in particular, is increasingly reshaping financial markets by transforming information processing, altering data-generating processes, and redefining decision-making structures. However, its implications remain inherently dual, combining efficiency gains with emerging risks. This paper examines the application of AI in finance through a “utopia versus reality” framework, focusing on the gap between its theoretical potential and practical outcomes. Using a literature review approach, it synthesizes existing research on AI’s role in information infrastructure, financial data transformation, and market behavior. The findings suggest that generative AI enhances information efficiency and expands the scope of usable data, enabling new forms of analysis and decision-making. At the same time, it introduces structural challenges, including diminished signal reliability, instability in data-generating processes, shifts in labor demand, and rising concerns over trust stemming from bias, opacity, and limited verifiability. Besides, the emergence of self-evolving and agentic AI transforms these risks from isolated model errors into dynamic and systemic concerns. Overall, while AI enhances efficiency and expands the scope of financial analysis, its inherent limitations and risks highlight a persistent gap between its theoretical potential and practical outcomes.

Keywords—artificial intelligence, generative ai, financial markets

Cite: Yi Zhou, "AI in Financial Applications: Utopia and Reality," Journal of Economics, Business and Management, vol. 14, no. 3, pp. 180-183, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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