SOURCE-LINKED INTELLIGENCE
Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements
The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity, substantial length, technical language, and inhomogeneity of both textual and numerical content across different jurisdictions and institutions. We introduce FinRAG-QA, a novel benchmark dataset for financial question answering, which comprises 999 practitioner-curated questions on 10 standardised indicators, grounded in 209 annual and Pillar 3 reports from 24 major European and U.S. banks spanning 2019-2023. Unlike prior financial QA benchmarks
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T10:52:56.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.