AI Data Analytics in Action: NLSQL, LSEG LCH and MCP for Financial Data
AI data analytics is changing how organisations access and analyse complex enterprise and financial data. By combining natural language interfaces with trusted APIs and AI tools, users can explore data without having to manually write complex queries or navigate multiple systems.
NLSQL brings this approach to structured data by enabling users to ask questions in natural language and translate those questions into real time API requests and actionable analytics.
AI Data Analytics with MCP
The Model Context Protocol (MCP) provides a standardised way for AI applications to discover and interact with external tools, APIs and data sources.
For financial services, this is particularly valuable because AI needs access to trusted, contextualised and governed data. LSEG is using MCP to make its licensed financial data and analytics accessible to AI-powered workflows, with an emphasis on data quality, context, governance and traceability.
Combining NLSQL, MCP and financial APIs creates a powerful AI data analytics workflow:
Natural-language question → AI → MCP → API → trusted financial data → analytics
Reference Rates API
The Reference Rates API provides live risk-free-rate (RFR) fixings for key benchmarks:
SOFR - Federal Reserve Bank of New York
€STR - European Central Bank
SONIA - Bank of England
These are the RFR benchmark indices cleared through SwapClear, providing relevant benchmark-rate data for financial analytics and workflows.
Legal Entities API
The Legal Entities API provides access to the GLEIF Global LEI Index for counterparty and member lookups, registration status and corporate hierarchy.
This allows AI-powered analytics to understand not only individual legal entities but also relationships between them.
For example:
LCH Limited → LCH Group Holdings Limited → London Stock Exchange Group plc
Corporate hierarchy and entity relationships are important elements of financial data analytics, particularly when AI needs to understand counterparties and organisational structures.
Connecting AI to Trusted Financial Data
The combination of NLSQL, MCP and LSEG/LCH APIs demonstrates how AI can become an intelligent interface to enterprise data.
Instead of relying solely on an AI model's internal knowledge, AI data analytics workflows can connect to authoritative external sources, retrieve relevant information and use that data to answer business questions.
This approach brings together natural language to API/SQL, AI analytics, financial APIs and trusted data to make complex financial information easier to access and analyse.
As AI becomes increasingly integrated into financial workflows, connecting models to high-quality, governed data will be essential for delivering accurate, explainable and useful analytics. LSEG's current AI strategy similarly emphasises trusted, AI-ready financial content delivered through MCP and other integrations.