# RAG Systems for FinTech

> RAG systems for FinTech that answer from filings, prospectuses, and policy with cited, auditable accuracy: retrieval-augmented generation built by Sentient Arc.

Cited answers from your filings, contracts, and policy, not a model's best guess at the regulation.

## The challenge in FinTech
Financial teams reason over documents that are long, structured, and unforgiving: 10-Ks, prospectuses, ISDA agreements, credit memos, and shifting regulatory text. A base model invents plausible figures and misreads a footnote, which is a reportable error rather than a rough draft. The cost of a wrong number here is not a bad answer. It is a compliance finding.

## Example workflows

### Regulatory filing analysis
- Ingest 10-K, 10-Q, and prospectus PDFs, preserving nested tables and cross-page footnotes
- Chunk and embed by section so disclosures, risk factors, and financials stay retrievable
- Retrieve the passages that answer the analyst question, ranked by relevance
- Generate a grounded answer constrained to retrieved text, with citations to page and section
- Run a reflection pass that verifies every citation before the analyst sees it

### Policy and disclosure lookup
- Index internal compliance policies, regulator guidance, and prior filings into one corpus
- Resolve an officer question to the controlling clause across that corpus
- Return the answer with the exact source passage and effective date attached

## Outcomes
- Analysts stop parsing 1,000-page filings by hand and query them in plain language
- Every answer carries a citation, so compliance can audit the source in one click
- Citation confidence high enough to stand behind in a regulated workflow
- New filings and policy versions enter the corpus without re-architecting retrieval

## FAQ
**Can a RAG system handle the tables in financial filings?**

Yes. We use multi-stage parsing and parent-document retrieval so nested tables and cross-page footnotes survive chunking. A single misread decimal in an SEC filing is treated as a failure to engineer out.

**How do you keep answers defensible for compliance?**

Every answer is constrained to retrieved passages and cited back to page and section, with a verification pass before it surfaces. For Finance-Mind this reached 99% citation confidence and cut manual research time by 92%.

**Does the model ever answer from outside our documents?**

No. Generation is grounded only in what retrieval returns from your corpus, with self-checks that block answers the sources do not support, no external leakage into a regulated response.

---
Canonical page: https://www.sentientarc.com/solutions/rag-systems-for-fintech