Metadata Matters: A Hybrid Retrieval Framework for Structured Financial Document Analysis

Abstract

Large Language Models are deployed in financial applications such as research synthesis and risk analysis, yet their effectiveness is constrained by the limitations of conventional retrieval methods. Existing approaches rely primarily on semantic similarity or token-level matching, which fails in structured domains like finance where relevance depends on precise alignment across entity, temporal and document-type dimensions. This paper proposes the Financial Knowledge Integration Framework (FKIF), a hybrid metadata-aware retrieval system that integrates dense semantic similarity, sparse lexical matching, and structured metadata signals into a unified ranking function. Unlike conventional hybrid retrieval approaches, FKIF treats metadata as a first-class relevance signal rather than an auxiliary feature. Tested on a held-out set of 22 queries drawn from a corpus of 57 SEC filings, FKIF achieves an MRR@10 of 0.98 against TF-IDF’s 0.35, BM25’s 0.17, and dense retrieval’s 0.13. The results demonstrate that metadata-aware retrieval significantly enhances retrieval accuracy and provides a foundation for reliable financial Retrieval-Augmented Generation (RAG) systems.