What is financial RAG?
Financial RAG is retrieval-augmented generation designed for financial questions and evidence. It retrieves relevant filing text, tables, footnotes, transcripts or structured API records, supplies that evidence to a language model, and attaches traceable citations to the answer. The objective is not merely semantic similarity: it is to retrieve the minimally sufficient, period-correct evidence needed to support each claim.
Financial RAG architecture: from filing to answer
Route questions before retrieval
A narrative-risk question, a single-table lookup and a multi-period ratio should not use the same path. Classify entity, form, period, task type and required evidence. Exact facts may route to a structured financial-data API; accounting explanations may need filing text; a variance question may need both.
Table-aware ingestion for financial documents
Fixed token chunks can sever a number from its row label, column period, unit or footnote. Preserve the document hierarchy—form, section, subsection, page, table and note—and create serialized table facts that travel with enough context to remain interpretable.
Narrative chunk
Table fact
Metadata is part of the evidence
Attach company identifier, form type, filing date, period end, fiscal year, currency, scale, consolidation scope, amendment status and access-control labels. Apply filters before similarity search so an excellent semantic match from the wrong company or period cannot outrank the requested evidence.
Hybrid retrieval for SEC filings and financial reports
Keyword retrieval
Strong for exact accounting terms, line items, form sections, defined metrics and names.
Semantic retrieval
Strong for paraphrases, narrative themes, similar risks and natural-language questions.
Structured retrieval
Strong for exact values, time series, ratios and entity-period filtering through APIs.
Merge and deduplicate candidate evidence, then rerank against the question and required sub-questions. Multi-hop queries should produce an evidence plan: for example, retrieve two comparable revenue values, confirm units and periods, then send them to a calculation tool. Top-k alone does not guarantee coverage.
Financial RAG citations must support the claim
Supported citation
The cited table cell or passage contains the claimed fact, correct entity, period and unit; derived values link to every input and formula.
Related but unsupported
The source mentions the topic but not the claimed number, direction, cause or certainty. Similarity is not entailment.
Validate numeric tokens as first-class objects
Extract every amount, percentage, date and period from the draft. Confirm it appears in cited evidence or comes from an approved calculation with traceable inputs. Check unit, sign, scale, currency and arithmetic. If evidence conflicts, present the conflict or abstain rather than silently selecting one value.
How to evaluate a financial RAG system
Build a human-verified set from real user tasks, not only synthetic questions. Include simple lookups, table questions, cross-period calculations, multi-document evidence, ambiguous requests, missing answers and adversarial instructions. Score retrieval and generation separately so a fluent answer cannot hide missing evidence.
| Metric | What it asks | Failure example |
|---|---|---|
| Context recall | Was all required evidence retrieved? | One period missing |
| Context precision | How much retrieved content was relevant? | Wrong segment crowds prompt |
| Citation support | Does evidence entail each claim? | Topic match only |
| Numeric accuracy | Are values, units and calculations correct? | Millions treated as dollars |
| Completeness | Were all requested facets answered? | Cause omitted |
| Abstention quality | Does the system stop when evidence is insufficient? | Invented estimate |
- Freeze test documents and expected evidence.
- Version parser, index, embeddings, reranker, prompt and model.
- Run regression tests after every component change.
- Manually audit high-impact and failed cases.
Financial RAG with QVeris and FMP
QVeris can help an agent discover and call authorized FMP APIs when a question is better answered by structured financial data than document similarity. The retrieval router can combine those API records with filing passages and transcript evidence, while preserving source identifiers and tool responses.
Endpoint availability, fields, history, entitlements and rate limits depend on current FMP documentation and subscription. QVeris does not guarantee source completeness, retrieval accuracy or investment outcomes; validation and accountable review remain required.
LLM for financial analysis · What is a 13F filing? · QVeris documentation
Financial RAG FAQ
What is financial RAG?
It is retrieval-augmented generation adapted to financial evidence, combining document and structured-data retrieval with citations and numerical validation.
How should financial tables be chunked?
Keep row label, value, unit, period, table title, scope and relevant footnote together, with page coordinates and document metadata.
Is vector search enough for financial RAG?
Usually not. Exact terminology, structured values and entity-period constraints benefit from keyword search, metadata filters and API retrieval.
How do you validate financial RAG citations?
Check whether each citation logically supports the exact claim, including entity, period, unit, direction and certainty.
How do you evaluate financial RAG?
Measure context recall and precision, citation support, numeric accuracy, answer completeness, reproducibility and abstention quality.
Does financial RAG eliminate hallucinations?
No. Parsing, retrieval, citation and generation can each fail. Validation, monitoring and human review are still necessary.
References and methodology sources
- ACL Anthology — FinMRAGBench
- T²-RAGBench — financial text and table QA
- FMP — Financial data API documentation
Educational content only; not investment advice. Validate retrieved evidence and calculations before use.