Emily Prince
The introduction of the Model Context Protocol (MCP) open standard pioneered by Anthropic is creating a new era of data-driven innovation. Financial institutions – long recognised for their data intensity – are now able to unlock deeper value from their existing data and insights, seamlessly integrating them into daily workflows.
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In a Q&A with Nicholas Lin, Head of Product, Financial Services at Anthropic, LSEG Group Head of AI Emily Prince explored what’s next for MCP and the tangible benefits it’s delivering to organisations today.
Emily Prince: Nicholas, Let’s start with the basics. For those unfamiliar with it, what is the Model Context Protocol (MCP), and why does it matter?
Nicholas: Thanks, Emily. MCP is an open standard we developed at Anthropic to help AI models connect to external data and systems. It makes it easier for AI applications to connect to external data sources and take actions inside of compatible applications. In financial services, it enables AI Agents to access enterprise data in a structured, governable way. This helps organizations ensure AI Agents meet enterprise security standards while having the data and action-taking capabilities to perform.
Emily Prince: What’s your take on how LSEG is approaching this?
Nicholas Lin: By adopting MCP, you’re making financial data available to AI models in a structured format. This enhances the model’s ability to make sense of the data and utilize it for tasks. And you’re expanding the reach and value of licensed data in the process. MCP will be the foundational protocol for AI Agents to transfer data and take actions. This move positions LSEG data to become a core part of AI workflows within the financial services industry.
Emily Prince: Let’s talk use cases. Where do you see MCP having the biggest impact in financial services?
Nicholas Lin: There’s a number of areas in financial services where MCP is already having a major impact. In research, MCP makes it easier for analysts to connect multiple data sources (market data feeds, internal research databases, and proprietary models, etc.) to Claude. This has dramatically improved productivity and research quality.
Emily Prince: Final question. If you had one piece of advice for firms looking to adopt MCP, what would it be?
Nicholas Lin: Start with your data. MCP works best when the underlying content is structured, contextual, and rights-managed. LSEG’s AI Ready Content is a great example—it’s not just data, it’s data designed for AI. If firms can get that foundation right, MCP can help them scale AI safely and effectively.
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