FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling

• URL: http://arxiv.org/abs/2510.01887v1
• Date: Thu, 02 Oct 2025 10:55:11 GMT
• Title: FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling
• Authors: Avinash Kumar Singh, Bhaskarjit Sarmah, Stefano Pasquali,
• Abstract summary: We introduce a curated financial dataset (FINCH) comprising 292 tables and 75,725 natural language-based pairs.<n>We benchmark reasoning models and language models of varying scales, providing a systematic analysis of their strengths and limitations.<n>Finally, we propose a finance-oriented evaluation metric (FINCH Score) that captures nuances overlooked by existing measures.
• Score: 1.8679829796354372
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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