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FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling

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  • Avinash Kumar Singh
  • Bhaskarjit Sarmah
  • Stefano Pasquali

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.

Suggested Citation

  • Avinash Kumar Singh & Bhaskarjit Sarmah & Stefano Pasquali, 2025. "FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling," Papers 2510.01887, arXiv.org.
    • Handle: RePEc:arx:papers:2510.01887
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    File URL: http://arxiv.org/pdf/2510.01887
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