Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation

2025-10-29

AI Summary

In financial technology domains, dense terminology and pervasive acronyms degrade retrieval and generation performance in Retrieval-Augmented Generation (RAG) systems. To address this, we propose a modular multi-agent collaborative RAG framework that integrates context-aware acronym resolution, keyword-guided iterative subquery decomposition, intelligent query rewriting, and cross-encoder re-ranking—enabling end-to-end precise retrieval and generation of domain-specific knowledge. Evaluated on an enterprise-scale financial knowledge base across 85 expert-curated question-answer pairs, our framework significantly outperforms conventional RAG baselines: retrieval accuracy improves by 23.6%, and relevance scores increase by 19.4%. These results validate the efficacy of the multi-agent collaboration paradigm in highly specialized domains. Although introducing moderate latency, the substantial gains in accuracy demonstrably outweigh the computational overhead.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Community question answering

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.

Problem

• Addresses retrieval limitations in fintech RAG systems
• Proposes agentic architecture for domain-specific challenges
• Enhances retrieval precision through multi-agent methodology

Innovation

• Agentic RAG architecture with modular pipeline
• Query reformulation and sub-query decomposition
• Cross-encoder-based context re-ranking system