How Is AI Transforming Access to Financial Data?

AI is changing how financial professionals reach data. Rather than navigating terminals and applications, they can increasingly ask questions in natural language and receive answers within their workflow. Model Context Protocol (MCP) for financial data is emerging as one way to connect AI assistants to access trusted, governed financial data. 

Together, AI assistants and MCP are changing financial data access from a destination-based experience into a more integrated part of everyday work. For financial institutions, this creates new opportunities to improve how people find, interpret, and use information. It also creates a clear requirement: AI-enabled workflows need reliable data from a source they can verify.

AI is changing financial data access into a more integrated part of everyday work, with AI assistants and agents bringing information into the workflow.

Financial professionals are accessing and interacting with information in new ways. AI assistants, which carry out tasks based on users’ prompts, and increasingly AI agents, which can plan and execute more complex workflows, are creating a more direct interface with data. Rather than relying only on traditional search and navigation across systems, users can ask questions in natural language, receive contextual responses, and access information within the flow of their work.

This shift is part of broader financial data trends, with financial institutions exploring how AI can support productivity, analysis, and decision-making.  This is already visible across the financial industry, according to the Market Data Study 2026, conducted by Crisil Coalition Greenwich in partnership with SIX.

• 86% of respondents are using or evaluating AI in their investment and trading workflows.

• 88% of respondents agree that market data providers can help ensure the accuracy and reliability of AI.

• 58% of respondents are considering or already engaged with MCP.

• 57% of respondents are using or testing an MCP server, suggesting a move from interest towards practical experimentation.   

Cloud infrastructure and APIs make financial data easier to access, integrate, and embed in professional workflows. Information no longer needs to remain tied to a single application or interface, and this creates a new expectation around financial data access.

Increasingly, professionals want information in the right context, at the right time, and in a format that supports the task they are performing. This shift is significant, because the interface itself is changing. A terminal asks users to navigate to information, while an AI assistant can interpret a question and help bring relevant information into the workflow.

The agentic AI market is also expanding. Deloitte analysis cited by Forbes projects the agentic AI market could reach USD 45 billion by 2030, up from USD 8.5 billion in 2026. This points to a wider move towards AI systems that can do more than generate text. They can interpret requests, use tools, and interact with external systems. The World Economic Forum's AI Playbook for Financial Services sets out how institutions are approaching AI adoption. 

For financial institutions, this makes access to reliable financial information an increasingly important part of the AI architecture.

These developments are part of a wider financial data landscape in which new technologies and business models are changing how financial services are delivered. SIX works with fintech ventures, providing another perspective on how this ecosystem is evolving.

AI systems are only as dependable as the information they use, and this matters particularly in financial services. AI can help professionals interpret information, identify patterns, and support decisions, but financial workflows often involve operational, financial, and compliance implications.

Generic web content may be useful for broad information needs, but it doesn’t provide the traceability, licensing, and controls that many regulated financial workflows require. These foundations are important for AI confidence. 

AI assistants need access to reliable information that they can interpret and use within a defined context. For financial applications, this includes:

• Coverage: authoritative market data across the instruments and markets a workflow requires.

• Reference data and consistent identifiers: to enable systems to match and distinguish instruments reliably.

• Standardized data enables AI to reason consistently across internal and external systems, creating a unified context for decision-making.

• Provenance and licensing: clear source information and usage rights.

The data needs to arrive in a form that systems can process reliably. Consistent structures help AI applications distinguish trusted financial information from less reliable sources.

This makes the role of financial data providers increasingly important. Providing information is no longer only about making data available. It is also about providing data that can be integrated into new AI-enabled workflows.

Trusted financial data therefore remains the foundation of successful AI adoption. As institutions move from experimentation towards broader deployment, data quality and reliability will support confidence in AI-generated outputs.

For organizations such as SIX that provide financial information to market participants, AI reinforces the importance of these foundations.

Model Context Protocol (MCP) is an open standard that allows AI assistants and agents to reach data sources, applications and tools through a common interface.

MCP does: 

• Connect AI agents to data sources through one interface.

• Extend APIs into agent-driven environments.

MCP does not:

• Replace APIs, feeds or files.

APIs have transformed how developers consume financial data. They provide structured, programmatic access to information and make it possible to integrate data into applications, platforms, and business processes. They enable applications to:

• Request information on demand.

• Connect data directly to workflows.

• Apply entitlement controls when a request is made.

• Support multiple applications without unnecessary duplication.

AI agents are now creating a different interaction model. Rather than requesting predefined data through an application, an agent can interpret a user's intent and use tools or data sources to complete a task. This creates a new requirement. AI applications need consistent ways to connect with enterprise systems. The growing interest in agentic AI is also driving discussion around technical standards for agentic AI, as Financial Data Exchange outlines.

MCP for financial data is still an emerging technology, and organizations are exploring how it could help AI assistants and agents interact with enterprise systems and authoritative data sources. 

For teams exploring MCP for financial data, the challenge is understanding where MCP adds value alongside APIs, feeds, files and applications, and which use cases each is best suited for. 

It does not replace the financial data ecosystem already in place, however. APIs, files, feeds, applications, and other delivery channels continue to serve established use cases.

This is why the industry is paying attention at present. If AI agents become a more common way to interact with enterprise systems, standards that simplify those connections could become an important part of the technology landscape.

The table below compares how files, APIs and agent interfaces are typically used to deliver financial data.

At SIX, we are working with clients to explore AI and MCP use cases, test workflows, and better understand how these technologies could support real financial processes.

The future of AI-enabled financial data access is still taking shape. Financial institutions are exploring where AI and agentic workflows can create practical value. The most useful applications will depend on specific business processes, data requirements, governance models, and user needs, which is why collaboration matters.

This approach focuses on experimentation rather than assuming that one model will work for every organization. Client discussions help identify the tasks where AI can add value, the information those tasks require, and the controls needed around them. The approach also helps bring practical questions into the discussion early.

There are questions that technology alone cannot answer, because they depend on how financial institutions work and what their clients need. Working directly with clients helps bring those requirements into the development and testing process.

This collaborative approach also reflects the current stage of the market. AI capabilities are developing quickly, while many financial institutions are still identifying the workflows where they can create the most value.

The next generation of financial data access is likely to combine trusted data, AI capabilities, and seamless integration into everyday workflows. AI assistants and agents could make it easier for professionals to work with information from multiple sources. Internal data, external information, and reliable financial datasets could increasingly come together within the same workflow.

APIs, feeds, files, cloud delivery, and emerging agent interfaces will continue to play different roles:

• APIs: on-demand access for applications and real-time workflows.

• Feeds: ongoing delivery of data into existing systems and workflows.

• Files: scheduled delivery for batch and high-volume workflows.

• Cloud delivery: data that’s easier to access, integrate and embed in professional workflows.

• Agent interfaces: natural-language and agentic workflows, through a common interface such as MCP.

MCP may become another way for AI systems to interact with these capabilities. For financial institutions, the opportunity is not simply to introduce another interface. It is to make reliable information more accessible while maintaining the governance, licensing, and controls that financial workflows require. 

The next stage of MCP for financial data will depend on more than capable models. It will require verified financial data, consistent access, appropriate governance, and integration into the workflows where decisions are made. SIX is exploring these developments with clients to understand where AI can create practical value while maintaining the data quality, controls, and transparency that financial workflows require.

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How are financial institutions approaching AI and emerging technologies such as MCP? What are they looking for from trusted financial data providers?

In this video, Laurent Lefèvre, Head Customer Interfaces, and Magdalena Tuta, Innovation Manager at SIX, share their perspectives on the conversations SIX is having with clients, the use cases emerging from those discussions, and what SIX is learning through experimentation.