Intermediate10 minute read

Private vs Public Financial Data RAG

How RAG differs for private financial data vs public market data — architecture decisions, security requirements, and tool selection.

Financial institutions work with two fundamentally different types of data: public market data available from external sources, and private internal data that is proprietary and confidential. Each type requires a different RAG architecture, security model, and tool selection strategy.

Public Financial Data RAG Sources

Public financial data includes SEC EDGAR filings (10-K, 10-Q, 8-K), earnings call transcripts from financial data providers, market data from Bloomberg Terminal and FactSet, economic indicators from government sources, and financial news from公开 feeds. This data can be processed in cloud environments and is often accessed via MCP servers like SEC EDGAR MCP, Alpha Vantage MCP, and Financial Modeling Prep MCP.

Private Financial Data RAG Sources

Private financial data includes internal research reports from your analyst team, client portfolio holdings and performance data, proprietary financial models and valuation workbooks, due diligence documents from M&A processes, and internal compliance and risk management records. This data requires on-premise RAG deployment with strict access controls, data encryption, and comprehensive audit trails.

Architecture Differences

Public financial RAG can be deployed in the cloud using managed services like Pinecone and LangChain. Private financial RAG requires on-premise or private cloud deployment using Weaviate self-hosted and Haystack or Contextual AI for enterprise-grade security. The key difference is that public RAG optimizes for data breadth and update frequency, while private RAG prioritizes security, access control, and data governance.

Hybrid RAG Architecture

Many financial institutions need both public and private data in a single RAG system. A hybrid architecture uses separate vector database collections for public and private data with different access controls, then merges retrieved results at query time based on the user's authorization level. Public data can be stored in cloud Pinecone with broad access, while private data stays in self-hosted Weaviate with role-based permissions.

Licensing Considerations

When building RAG over Bloomberg or FactSet data, ensure your data agreement permits AI model usage. Financial data providers have specific licensing terms for AI applications — some restrict the use of their data for model training or automated analysis. Always verify your agreement covers RAG use cases before building production pipelines.

Related RAG Tools

Related Data Tools

Weaviate Pinecone SnowflakeBloomberg-terminalFactset

Related Use Cases

FAQ

What is the difference between public and private financial RAG?

Public financial RAG uses freely available data like SEC filings, earnings transcripts, and market data that can be processed in cloud environments. Private financial RAG uses proprietary internal documents requiring on-premise deployment, strict access controls, and data governance.

How do I build RAG over Bloomberg or FactSet data?

Bloomberg and FactSet provide API access to market data that can be ingested via MCP servers or direct API calls, embedded, and stored in a vector database. The key challenge is licensing — ensure your Bloomberg or FactSet agreement permits AI model usage of the data.

Can I combine public and private financial data in one RAG system?

Yes — hybrid RAG architectures use separate vector database collections for public and private data with different access controls, then merge retrieved results at query time based on the user's authorization level.