Development of AI Multi-Agent Frameworks for Financial Services

Chen Li

doi:10.70393/6a69656173.333337

Authors

Chen Li The University of Texas at Dallas

DOI:

https://doi.org/10.70393/6a69656173.333337

ARK:

https://n2t.net/ark:/40704/JIEAS.v3n6a01

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

11

Keywords:

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

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.

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Author Biography

Chen Li, The University of Texas at Dallas

The University of Texas at Dallas, USA.

References

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