{"ID":2861422,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.01887","arxiv_id":"2510.01887","title":"FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling","abstract":"Text-to-SQL, the task of translating natural language questions into SQL queries, has long been a central challenge in NLP. While progress has been significant, applying it to the financial domain remains especially difficult due to complex schema, domain-specific terminology, and high stakes of error. Despite this, there is no dedicated large-scale financial dataset to advance research, creating a critical gap. To address this, we introduce a curated financial dataset (FINCH) comprising 292 tables and 75,725 natural language-SQL pairs, enabling both fine-tuning and rigorous evaluation. Building on this resource, we benchmark reasoning models and language models of varying scales, providing a systematic analysis of their strengths and limitations in financial Text-to-SQL tasks. Finally, we propose a finance-oriented evaluation metric (FINCH Score) that captures nuances overlooked by existing measures, offering a more faithful assessment of model performance.","short_abstract":"Text-to-SQL, the task of translating natural language questions into SQL queries, has long been a central challenge in NLP. While progress has been significant, applying it to the financial domain remains especially difficult due to complex schema, domain-specific terminology, and high stakes of error. Despite this, th...","url_abs":"https://arxiv.org/abs/2510.01887","url_pdf":"https://arxiv.org/pdf/2510.01887v1","authors":"[\"Avinash Kumar Singh\",\"Bhaskarjit Sarmah\",\"Stefano Pasquali\"]","published":"2025-10-02T10:55:11Z","proceeding":"q-fin.CP","tasks":"[\"q-fin.CP\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}