Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation
Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology,...
Abstract
Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology, and acronyms complicate effective retrieval and synthesis. This paper introduces an agentic RAG architecture designed to address these challenges through a modular pipeline of specialized agents. The proposed system supports intelligent query reformulation, iterative sub-query decomposition guided by keyphrase extraction, contextual acronym resolution, and cross-encoder-based context re-ranking. We evaluate our approach against a standard RAG baseline using a curated dataset of 85 question--answer--reference triples derived from an enterprise fintech knowledge base. Experimental results demonstrate that the agentic RAG system outperforms the baseline in retrieval precision and relevance, albeit with increased latency. These findings suggest that structured, multi-agent methodologies offer a promising direction for enhancing retrieval robustness in complex, domain-specific settings.
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Citation
Thomas Cook. "Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation." arXiv preprint. 2025-10-29. http://arxiv.org/abs/2510.25518v1
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References
- [1]ResearchCredibility: 9/10Thomas Cook. "Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation." arXiv.org. October 29, 2025. Accessed November 18, 2025.
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Original Source
This article is based on Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation (arXiv.org)
