Building a Compliance-Ready RAG Platform for Financial Services

The Challenge

FinServe Analytics, a financial compliance consulting firm, had a critical problem: their analysts spent 40% of their time searching through regulatory documents, SEC filings, and internal compliance guidelines to answer client questions.

With 100,000+ documents across multiple regulatory frameworks (SEC, FINRA, CFTC), finding relevant information was slow and error-prone. They needed a system that could:

• Deliver accurate answers with source citations for audits
• Search across multiple document types (PDFs, Word docs, HTML)
• Handle complex financial terminology and acronyms
• Meet strict audit and compliance requirements (SOC 2, SEC)
• Provide data residency options for sensitive client data

Critical Constraints:

• Zero tolerance for hallucinations or incorrect citations
• Must maintain complete audit trail of all queries
• Needed on-premises deployment option for sensitive data
• Had to explain its reasoning process to compliance officers

Our Approach

We designed a multi-stage RAG (Retrieval-Augmented Generation) system with strong emphasis on accuracy, auditability, and explainability.

Phase 1: Document Processing & Embedding (Weeks 1-3)

Challenge: Financial documents are complex—nested tables, footnotes, cross-references, and domain-specific terminology.

Solution:

• Built custom document parsers for PDFs, Word docs, and HTML using PyMuPDF and python-docx
• Chunking strategy: 512-token chunks with 50-token overlap, preserving document structure
• Created a financial domain-specific embedding model by fine-tuning sentence-transformers on regulatory text corpus
• Indexed 100K+ documents into Pinecone vector database with metadata (document ID, section, date, source)

Phase 2: Retrieval & Reranking (Week 4)

Challenge: Vector search alone wasn't precise enough for regulatory queries—we needed hybrid search.

Solution:

• Implemented hybrid search combining:
  • Vector similarity (embeddings) for semantic matching
  • BM25 keyword search for exact term matching (critical for regulations with specific identifiers)
  • Metadata filtering (date ranges, document types, agencies)
• Added a reranking layer using cross-encoder model to refine top 20 results down to top 5
• Achieved 92% recall@5 on test queries

Phase 3: Generation & Citation (Week 5)

Challenge: LLMs can hallucinate. In finance, that's unacceptable.

Solution:

• Used GPT-4 with strict prompt engineering:
  • "Only answer based on provided context. If unsure, say so."
  • Required inline citations using [Source: Doc ID, Section X] format
• Implemented citation verification: every statement in the response was mapped back to source chunks
• Added confidence scoring: flagged low-confidence answers for human review

Phase 4: Audit & Compliance (Week 6)

Challenge: SEC auditors need to trace every answer back to its source.

Solution:

• Built complete audit trail in PostgreSQL:
  • Query text, timestamp, user ID
  • Retrieved documents with relevance scores
  • Generated response with inline citations
  • User feedback (thumbs up/down)
• Created admin dashboard showing:
  • Query volume and patterns
  • Most-cited documents
  • Low-confidence responses flagged for review
• Implemented role-based access control (RBAC) with Okta SSO integration

Phase 5: Production Deployment (Weeks 7-8)

Deployed as a containerized microservice architecture:

• FastAPI backend with async processing
• React frontend with chat interface
• PostgreSQL for audit logs and user data
• Pinecone for vector search (SaaS)
• Optional: Self-hosted vector DB (Weaviate) for on-prem deployments

Architecture

┌──────────────────┐
│  Document Store  │
│  (S3 / On-Prem)  │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  Doc Processing  │
│  (Chunking +     │
│   Embedding)     │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐       ┌─────────────────┐
│  Vector DB       │       │  PostgreSQL     │
│  (Pinecone /     │◄──────┤  (Metadata +    │
│   Weaviate)      │       │   Audit Logs)   │
└────────┬─────────┘       └─────────────────┘
         │
         ▼
┌──────────────────┐
│  RAG API         │
│  (FastAPI)       │
│                  │
│  1. Query        │
│  2. Hybrid Search│
│  3. Rerank       │
│  4. LLM Generate │
│  5. Verify Cites │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  Web App         │
│  (React)         │
└──────────────────┘

Results

After 8 weeks of development and 2 weeks of testing:

• 85% time savings: Analysts found answers in seconds instead of hours
• 100K+ documents indexed: Sub-second retrieval across entire corpus
• Passed SEC audit: Complete audit trail met all regulatory requirements
• 92% user satisfaction: Internal survey showed high confidence in answers
• Zero hallucination incidents: Citation verification caught all potential errors

Usage Statistics (First 3 Months):

• 15,000+ queries processed
• Average response time: 3.2 seconds
• 94% of answers required no human correction
• Most common query types: SEC filing interpretations, FINRA rule lookups

Key Technical Decisions

Why Hybrid Search?

Pure vector search missed exact regulation codes (e.g., "Rule 10b-5"). Combining BM25 keyword search with vector similarity gave us best of both worlds.

Why Reranking?

Initial retrieval is fast but imprecise. Reranking with a cross-encoder model improved relevance significantly with minimal latency cost.

Why PostgreSQL for Audit Logs?

We needed ACID transactions and complex queries for audit reports. NoSQL wasn't suitable.

Why Not Train a Custom LLM?

GPT-4 with strong prompting and retrieval was more cost-effective than training a domain-specific model. We invested in retrieval quality instead.

Security & Compliance Features

• Data Encryption: At-rest (AES-256) and in-transit (TLS 1.3)
• Access Controls: Role-based permissions with SSO via Okta
• Audit Logging: Every query, response, and user action logged
• Data Residency: On-premises deployment option for sensitive data
• Citation Verification: Automated checks to prevent hallucinations
• PII Redaction: Automatic detection and masking of sensitive information

Lessons Learned

1. Chunking strategy matters: We iterated multiple times to balance context window size with retrieval precision.

2. Hybrid search is essential for regulatory text: Pure semantic search missed important exact-match queries.

3. Citation verification is non-negotiable: Automated checks caught several edge cases where the LLM embellished beyond the source text.

4. User feedback loops improve quality: We collected thumbs up/down feedback and used it to refine prompts and retrieval parameters.

5. Compliance requirements drive architecture: Audit trail and access controls weren't afterthoughts—they shaped the entire system design.

Technology Stack

• LLM: OpenAI GPT-4, GPT-3.5-Turbo
• Embeddings: Fine-tuned Sentence-Transformers (all-mpnet-base-v2)
• Vector DB: Pinecone (SaaS), Weaviate (on-prem option)
• Search: BM25 via Elasticsearch, Pinecone for vector search
• Backend: Python, FastAPI, LangChain, Pydantic
• Database: PostgreSQL, Redis (caching)
• Frontend: React, TypeScript, TailwindCSS
• Deployment: Docker, Kubernetes, AWS (EKS) / On-Premises

Conclusion

Building a production RAG system for financial services requires more than just plugging an LLM into a vector database. It demands:

• Domain-specific embedding models
• Hybrid search combining semantic and keyword approaches
• Rigorous citation verification
• Complete audit trails for compliance
• Security and access controls from day one

By focusing on these fundamentals, we delivered a system that not only passed regulatory scrutiny but became an indispensable tool for compliance analysts.

Need a compliance-ready RAG system? Let's talk about your requirements.