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Publicação
Opportunities and Challenges of Multi Agent Systems in the Financial Services Industry
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Orientador(es)
Resumo(s)
The rapid advancement of artificial intelligence, particularly large language models (LLMs), has
accelerated the adoption of multi-agent systems (MAS) within the financial services industry.
Despite growing interest, existing research remains fragmented, either addressing the
managerial aspects of AI implementation or offering domain-specific overviews of MAS,
without providing an integrated, practical perspective. Using a design science research
approach, the study first creates an evaluation matrix, based on Analytical Hierarchy Process
(AHP), to systematically assess AI use cases. It then develops and tests a MAS prototype for
regulatory change analysis, leveraging retrieval-augmented generation (RAG) and hierarchical
agent collaboration. Finally, the research identifies key opportunities and challenges arising
from the implementation process. The research combines a comprehensive literature review,
expert interviews, and quantitative assessments to identify high-value use cases and evaluate
system performance against industry-relevant criteria. Findings highlight both the
transformative potential and significant hurdles associated with MAS adoption, including
issues of scalability, compliance, and organizational alignment. By synthesizing theoretical and
practical insights, this thesis contributes a holistic framework for the implementation and
evaluation of MAS in financial services, bridging the gap between academic research and real-world application.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Large Language Model Multi-Agent System Generative Artificial Intelligence Retrieval-Augmented Generation Financial Services Industry SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure