Development of AI Multi-Agent Frameworks for Financial Services

Chen Li

doi:10.70393/6a69656173.333337

OPEN ACCESS |Research Article ||3 December 2025

Development of AI Multi-Agent Frameworks for Financial Services

Chen Li

The University of Texas at Dallas

chenli.stephen@gmail.com

The University of Texas at Dallas, USA.

* Corresponding Author¹: Chen Li, E-Mail: chenli.stephen@gmail.com

Publication

Accepted 2025 November 19 ; Published 2025 December 3

Journal of Industrial Engineering and Applied Science, 2025, 3(6), 3005-6071.

Abstract

Large-language-model-based multi-agent architectures and distributed AI components are rapidly reshaping financial services. They enable autonomous decision-making, collaboration in problem-solving, and automation of complex workflows across risk management, trading, compliance, fraud detection, and customer interaction. However, existing frameworks face significant tensions between scalability, regulatory requirements, and real-time performance in highly dynamic markets. This paper revisits the current landscape of AI multi-agent frameworks for finance and proposes a refined perspective emphasizing framework architecture, quantitative evaluation, and governance. We briefly reference related developments in small-sample prompt-based classification and hybrid edge–cloud frameworks.

Keywords

Large Language Models (LLMs) , Multi-agent Architectures , Financial Services , Hybrid Edge–cloud Frameworks .

Metadata

DOI:

10.70393/6a69656173.333337

ARK:

ark:/40704/JIEAS.v3n6a01

Pages: 1-5

References: 11

Disciplines: Artificial Intelligence Technology

Subjects: Machine Learning

Cite This Article

APA Style

Li, C. (2025). Development of ai multi-agent frameworks for financial services. Journal of Industrial Engineering and Applied Science, 3(6), 1-5. https://doi.org/10.70393/6a69656173.333337

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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