FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
This addresses the need for reliable and efficient database querying in finance, though it is incremental as it builds on existing LLM and function-calling methods.
The paper tackled the problem of natural-language querying over financial databases by introducing FinAI Data Assistant, which uses LLMs with the OpenAI Function Calling API to route queries to vetted, parameterized queries instead of generating SQL, resulting in lower latency and cost and higher reliability compared to a text-to-SQL baseline.
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.