FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API

• URL: http://arxiv.org/abs/2510.14162v2
• Date: Tue, 21 Oct 2025 04:48:57 GMT
• Title: FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
• Authors: Juhyeong Kim, Yejin Kim, Youngbin Lee, Hyunwoo Byun,
• Abstract summary: FinAI Data Assistant is a practical approach for natural-ahead querying over financial databases.<n>System routes user requests to a small library of vetted, parameterized queries.<n>Result: Ticker-mapping accuracy is near-perfect for NASDAQ-100 and high for S&P500 firms.
• Score: 1.1985612872852671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Calling API. Rather than synthesizing complete SQL via text-to-SQL, our system routes user requests to a small library of vetted, parameterized queries, trading generative flexibility for reliability, low latency, and cost efficiency. We empirically study three questions: (RQ1) whether LLMs alone can reliably recall or extrapolate time-dependent financial data without external retrieval; (RQ2) how well LLMs map company names to stock ticker symbols; and (RQ3) whether function calling outperforms text-to-SQL for end-to-end database query processing. Across controlled experiments on prices and fundamentals, LLM-only predictions exhibit non-negligible error and show look-ahead bias primarily for stock prices relative to model knowledge cutoffs. Ticker-mapping accuracy is near-perfect for NASDAQ-100 constituents and high for S\&P~500 firms. Finally, FinAI Data Assistant achieves lower latency and cost and higher reliability than a text-to-SQL baseline on our task suite. We discuss design trade-offs, limitations, and avenues for deployment.

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