Agents for Finance
Building production agents for financial research, risk assessment, portfolio analysis, and report generation — with the numerical accuracy, audit trails, and regulatory compliance that finance demands.
Quick Reference
- →Never trust LLM-generated numbers — always extract from source documents and validate with deterministic code
- →RAG over SEC filings requires table-aware parsing; standard text chunking destroys financial tables
- →Every financial output needs a disclaimer and an audit trail linking conclusions to source data
- →Integrate real-time market data through tool calling, not through the model's training data
- →Fiduciary duty means your agent must never present opinions as recommendations without proper licensing
RAG Over Financial Documents
Financial documents — 10-Ks, earnings transcripts, analyst reports — are dense with tables, footnotes, and cross-references. Standard text chunking destroys the structure that makes these documents useful. You need a pipeline that handles tables as first-class objects and preserves the relationships between narrative text and the numbers it references.
Financial queries often mix semantic intent ('revenue growth drivers') with exact terms ('EBITDA margin Q3 2025'). Use hybrid search — vector similarity for semantic matches, BM25 for exact keyword hits — and combine the scores.