Within six months of MCP's existence, major financial data providers (S&P, Faxet, Pitchbook) have published functional MCPs and are receiving strong customer feedback, which is remarkable given that getting even an API from these organizations historically took many years.

Anthropic is rolling out their financial analyst to BCI of Canada (managing $200 billion) and Norges of Norway (managing over $2 trillion, the largest sovereign wealth fund in the world).

Anthropic is also rolling out Claude to all 450,000 of Deloitte's global employees.

Anthropic demoed their first AI financial analyst in July that built a fully auditable discounted cash flow model live on stage in two minutes without needing to be prompted to create an investment memo with proper citations.

Anthropic focuses on building domain-specific language expertise in the model intelligence layer, similar to how coding has specific programming languages, while finance has accounting and other domain-specific frameworks.

Downstream from research, financial analysts must put information into spreadsheets to build discounted cash flow models, perform sensitivity analyses, and present findings in investment memos or pitch decks, but these downstream steps aren't captured by current benchmarks.

BCI has $200 billion in assets but only 200 employees, meaning they have a high data-to-staff ratio and need AI tooling like MCP servers to analyze investment data spread across the world.

Norges has approximately 2,000 employees, which is roughly 10x larger than BCI, and therefore likely has a larger technical team and more public market quants and traders who can build custom workflows.

A major obstacle to AI adoption in financial services is that many institutions have not built great data lakes, making it necessary to invest in data infrastructure before applying AI capabilities.

BCI (British Columbia Investment Management) has $200 billion in assets under management and manages approximately 200 employees, giving them high data-analysis needs relative to staff size.

Approximately six months ago, research found that about 1% of bankers had access to AI tools at work, and Anthropic estimates today (present conversation) that access remains in the single digits—indicating early-stage adoption despite accelerating deployment.

In July, Anthropic demoed their first AI financial analyst that built a fully auditable discounted cash flow model live on stage in two minutes without prompting, and created an investment memo with proper citations.

In just six months since MCP's introduction, major financial data providers like S&P, Factset, and Pitchbook have built and published functional, working MCP servers that are receiving good feedback from customers—a remarkable speed compared to how long API releases normally take.

Microsoft has announced that Claude is now available inside the Office suite and users can toggle between different foundational model companies, giving Microsoft leverage to distribute Claude to billions of Office users.

Claude tweaks approximately 18 inputs in the backend of a spreadsheet model to achieve a target outcome, whereas an analyst manually ripping apart an Excel model would find it extremely time-consuming without proper architecture.

While 55% accuracy on FinBench is not excellent, it represents the best current performance and only three months of focused work, meaning these are 'the worst the tools have ever been or will ever be' and substantial improvement is ahead.