FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling

ArXiv ID: 2510.01887 “View on arXiv”

Authors: 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.

Keywords: Text-to-SQL, Financial Dataset, Natural Language Processing, Large Language Models, Evaluation Metrics, Multi-Asset (Data Infrastructure)

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Street Traders
  • Why: The paper is highly data- and implementation-heavy, focusing on dataset curation, model benchmarking, and a new evaluation metric for a practical Text-to-SQL task, with minimal advanced mathematics.
  flowchart TD
    A["Research Goal:<br/>Address lack of financial Text-to-SQL<br/>resources and evaluation"] --> B["Methodology:<br/>Curated Financial Dataset Creation"]
    B --> C["Data Input:<br/>FINCH Dataset<br/>292 tables / 75,725 NL-SQL pairs"]
    C --> D["Computational Process:<br/>Benchmarking Models<br/>&<br/>Developing Finance Score"]
    D --> E["Key Findings:<br/>1. Systematic Model Analysis<br/>2. FINCH Evaluation Metric<br/>3. SOTA Baselines"]