FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval

• URL: http://arxiv.org/abs/2509.12042v1
• Date: Mon, 15 Sep 2025 15:25:26 GMT
• Title: FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
• Authors: Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, Tiejun Ma,
• Abstract summary: FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval) is a retrieval framework tailored to financial documents.<n>It aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection.<n>It improves F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems.
• Score: 8.717064717809974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Question Tree), and a two-stage cross-encoder reranker. This design aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection. Evaluated on full 10-Ks with queries aligned to the FinQA dataset, FinGEAR delivers consistent gains in precision, recall, F1, and relevancy, improving F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems, while also increasing downstream answer accuracy with a fixed reader. By jointly modeling section hierarchy and domain lexicon signals, FinGEAR improves retrieval fidelity and provides a practical foundation for high-stakes financial analysis.

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