Anthropic is making its most aggressive push yet into the trillion-dollar financial services industry, unveiling a collection of tools that embed its Claude AI assistant directly into Microsoft Excel and connect it to real-time market data from among the world’s most influential financial information providers.
The San Francisco-based AI startup announced Monday it’s releasing Claude for Excel, allowing financial analysts to interact with the AI system directly inside their spreadsheets — the quintessential tool of contemporary finance. Beyond Excel, select Claude models are also being made available in Microsoft Copilot Studio and Researcher agent, expanding the mixing across Microsoft’s enterprise AI ecosystem. The integration marks a major escalation in Anthropic’s campaign to position itself because the AI platform of selection for banks, asset managers, and insurance firms, markets where precision and regulatory compliance matter excess of creative flair.
The expansion comes just three months after Anthropic launched its Financial Analysis Solution in July, and it signals the corporate’s determination to capture market share in an industry projected to spend $97 billion on AI by 2027, up from $35 billion in 2023.
More importantly, it positions Anthropic to compete directly with Microsoft — mockingly, its partner on this Excel integration — which has its own Copilot AI assistant embedded across its Office suite, and with OpenAI, which counts Microsoft as its largest investor.
Why Excel has change into the brand new battleground for AI in finance
The decision to construct directly into Excel is hardly accidental. Excel stays the lingua franca of finance, the digital workspace where analysts spend countless hours constructing financial models, running valuations, and stress-testing assumptions. By embedding Claude into this environment, Anthropic is meeting financial professionals exactly where they work relatively than asking them to toggle between applications.
Claude for Excel allows users to work with the AI in a sidebar where it could actually read, analyze, modify, and create latest Excel workbooks while providing full transparency concerning the actions it takes by tracking and explaining changes and letting users navigate on to referenced cells.
This transparency feature addresses probably the most persistent anxieties around AI in finance: the “black box” problem. When billions of dollars ride on a financial model’s output, analysts need to know not only the reply but how the AI arrived at it. By showing its work on the cell level, Anthropic is attempting to construct the trust essential for widespread adoption in an industry where careers and fortunes can activate a misplaced decimal point.
The technical implementation is sophisticated. Claude can discuss how spreadsheets work, modify them while preserving formula dependencies — a notoriously complex task — debug cell formulas, populate templates with latest data, or construct entirely latest spreadsheets from scratch. This is not merely a chatbot that answers questions on your data; it is a collaborative tool that may actively manipulate the models that drive investment decisions value trillions of dollars.
How Anthropic is constructing data moats around its financial AI platform
Perhaps more significant than the Excel integration is Anthropic’s expansion of its connector ecosystem, which now links Claude to live market data and proprietary research from financial information giants. The company added six major latest data partnerships spanning your complete spectrum of monetary information that skilled investors depend on.
Aiera now provides Claude with real-time earnings call transcripts and summaries of investor events like shareholder meetings, presentations, and conferences. The Aiera connector also enables a knowledge feed from Third Bridge, which provides Claude access to a library of insights interviews, company intelligence, and industry evaluation from experts and former executives. Chronograph gives private equity investors operational and financial information for portfolio monitoring and conducting due diligence, including performance metrics, valuations, and fund-level data.
Egnyte enables Claude to securely search permitted data for internal data rooms, investment documents, and approved financial models while maintaining governed access controls. LSEG, the London Stock Exchange Group, connects Claude to live market data including fixed income pricing, equities, foreign exchange rates, macroeconomic indicators, and analysts’ estimates of other necessary financial metrics. Moody’s provides access to proprietary credit rankings, research, and company data covering ownership, financials, and news on greater than 600 million private and non-private corporations, supporting work and research in compliance, credit evaluation, and business development. MT Newswires provides Claude with access to the newest global multi-asset class news on financial markets and economies.
These partnerships amount to a land grab for the informational infrastructure that powers modern finance. Previously announced in July, Anthropic had already secured integrations with S&P Capital IQ, Daloopa, Morningstar, FactSet, PitchBook, Snowflake, and Databricks. Together, these connectors give Claude access to virtually every category of monetary data an analyst might need: fundamental company data, market prices, credit assessments, private company intelligence, alternative data, and breaking news.
This matters because the standard of AI outputs depends entirely on the standard of inputs. Generic large language models trained on public web data simply cannot compete with systems which have direct pipelines to Bloomberg-quality financial information. By securing these partnerships, Anthropic is constructing moats around its financial services offering that competitors will find difficult to duplicate.
The strategic calculus here is obvious: Anthropic is betting that domain-specific AI systems with privileged access to proprietary data will outcompete general-purpose AI assistants. It’s a direct challenge to the “one AI to rule all of them” approach favored by some competitors.
Pre-configured workflows goal the each day grind of Wall Street analysts
The third pillar of Anthropic’s announcement involves six latest “Agent Skills” — pre-configured workflows for common financial tasks. These skills are Anthropic’s try and productize the workflows of entry-level and mid-level financial analysts, professionals who spend their days constructing models, processing due diligence documents, and writing research reports. Anthropic has designed skills specifically to automate these time-consuming tasks.
The latest skills include constructing discounted money flow models complete with full free money flow projections, weighted average cost of capital calculations, scenario toggles, and sensitivity tables. There’s comparable company evaluation featuring valuation multiples and operating metrics that might be easily refreshed with updated data. Claude can now process data room documents into Excel spreadsheets populated with financial information, customer lists, and contract terms. It can create company teasers and profiles for pitch books and buyer lists, perform earnings analyses that use quarterly transcripts and financials to extract necessary metrics, guidance changes, and management commentary, and produce initiating coverage reports with industry evaluation, company deep dives, and valuation frameworks.
It’s value noting that Anthropic’s Sonnet 4.5 model now tops the Finance Agent benchmark from Vals AI at 55.3% accuracy, a metric designed to check AI systems on tasks expected of entry-level financial analysts. A 55% accuracy rate might sound underwhelming, but it surely is state-of-the-art performance and highlights each the promise and limitations of AI in finance. The technology can clearly handle sophisticated analytical tasks, but it surely’s not yet reliable enough to operate autonomously without human oversight — a reality that will actually reassure each regulators and the analysts whose jobs might otherwise be in danger.
The Agent Skills approach is especially clever since it packages AI capabilities in terms that financial institutions already understand. Rather than selling generic “AI assistance,” Anthropic is offering solutions to specific, well-defined problems: “You need a DCF model? We have a skill for that. You need to research earnings calls? We have a skill for that too.”
Trillion-dollar clients are already seeing massive productivity gains
Anthropic’s financial services strategy appears to be gaining traction with precisely the sort of marquee clients that matter in enterprise sales. The company counts amongst its clients AIA Labs at Bridgewater, Commonwealth Bank of Australia, American International Group, and Norges Bank Investment Management — Norway’s $1.6 trillion sovereign wealth fund, one among the world’s largest institutional investors.
NBIM CEO Nicolai Tangen reported achieving roughly 20% productivity gains, comparable to 213,000 hours, with portfolio managers and risk departments now capable of “seamlessly query our Snowflake data warehouse and analyze earnings calls with unprecedented efficiency.”
At AIG, CEO Peter Zaffino said the partnership has “compressed the timeline to review business by greater than 5x in our early rollouts while concurrently improving our data accuracy from 75% to over 90%.” If these numbers hold across broader deployments, the productivity implications for the financial services industry are staggering.
These aren’t pilot programs or proof-of-concept deployments; they’re production implementations at institutions managing trillions of dollars in assets and making underwriting decisions that affect tens of millions of consumers. Their public endorsements provide the social proof that typically drives enterprise adoption in conservative industries.
Regulatory uncertainty creates each opportunity and risk for AI deployment
Yet Anthropic’s financial services ambitions unfold against a backdrop of heightened regulatory scrutiny and shifting enforcement priorities. In 2023, the Consumer Financial Protection Bureau released guidance requiring lenders to “use specific and accurate reasons when taking adversarial actions against consumers” involving AI, and issued additional guidance requiring regulated entities to “evaluate their underwriting models for bias” and “evaluate automated collateral-valuation and appraisal processes in ways in which minimize bias.”
However, in line with a Brookings Institution evaluation, these measures have since been revoked with work stopped or eliminated at the present downsized CFPB under the present administration, creating regulatory uncertainty. The pendulum has swung from the Biden administration’s cautious approach, exemplified by an executive order on protected AI development, toward the Trump administration’s “America’s AI Action Plan,” which seeks to “cement U.S. dominance in artificial intelligence” through deregulation.
This regulatory flux creates each opportunities and risks. Financial institutions desperate to deploy AI now face less prescriptive federal oversight, potentially accelerating adoption. But the absence of clear guardrails also exposes them to potential liability if AI systems produce discriminatory outcomes, particularly in lending and underwriting.
The Massachusetts Attorney General recently reached a $2.5 million settlement with student loan company Earnest Operations, alleging that its use of AI models resulted in “disparate impact in approval rates and loan terms, specifically disadvantaging Black and Hispanic applicants.” Such cases will likely multiply as AI deployment grows, making a patchwork of state-level enforcement whilst federal oversight recedes.
Anthropic appears aware of these risks. In an interview with Banking Dive, Jonathan Pelosi, Anthropic’s global head of industry for financial services, emphasized that Claude requires a “human within the loop.” The platform, he said, shouldn’t be intended for autonomous financial decision-making or to offer stock recommendations that users follow blindly. During client onboarding, Pelosi told the publication, Anthropic focuses on training and understanding model limitations, putting guardrails in place so people treat Claude as a helpful technology relatively than a substitute for human judgment.
Competition heats up as every major tech company targets finance AI
Anthropic’s financial services push comes as AI competition intensifies across the enterprise. OpenAI, Microsoft, Google, and various startups are all vying for position in what may change into one among AI’s most lucrative verticals. Goldman Sachs introduced a generative AI assistant to its bankers, traders, and asset managers in January, signaling that major banks may construct their very own capabilities relatively than rely exclusively on third-party providers.
The emergence of domain-specific AI models like BloombergGPT — trained specifically on financial data — suggests the market may fragment between generalized AI assistants and specialized tools. Anthropic’s strategy appears to stake out a middle ground: general-purpose models, since Claude was not trained exclusively on financial data, enhanced with financial-specific tooling, data access, and workflows.
The company’s partnership strategy with implementation consultancies including Deloitte, KPMG, PwC, Slalom, TribeAI, and Turing is equally critical. These firms function force multipliers, embedding Anthropic’s technology into their very own service offerings and providing the change management expertise that financial institutions must successfully adopt AI at scale.
CFOs worry about AI hallucinations and cascading errors
The broader query is whether or not AI tools like Claude will genuinely transform financial services productivity or merely shift work around. The PYMNTS Intelligence report “The Agentic Trust Gap” found that chief financial officers remain hesitant about AI agents, with “nagging concern” about hallucinations where “an AI agent can go off script and expose firms to cascading payment errors and other inaccuracies.”
“For finance leaders, the message is stark: Harness AI’s momentum now, but construct the guardrails before the subsequent quarterly call—or risk owning the fallout,” the report warned.
A 2025 KPMG report found that 70% of board members have developed responsible use policies for workers, with other popular initiatives including implementing a recognized AI risk and governance framework, developing ethical guidelines and training programs for AI developers, and conducting regular AI use audits.
The financial services industry faces a fragile balancing act: move too slowly and risk competitive drawback as rivals achieve productivity gains; move too quickly and risk operational failures, regulatory penalties, or reputational damage. Speaking on the Evident AI Symposium in New York last week, Ian Glasner, HSBC’s group head of emerging technology, innovation and ventures, struck an optimistic tone concerning the sector’s readiness for AI adoption. “As an industry, we’re thoroughly prepared to administer risk,” he said, in line with CIO Dive. “Let’s not overcomplicate this. We just must be focused on the business use case and the worth associated.”
Anthropic’s latest moves suggest the corporate sees financial services as a beachhead market where AI’s value proposition is obvious, customers have deep pockets, and the technical requirements play to Claude’s strengths in reasoning and accuracy. By constructing Excel integration, securing data partnerships, and pre-packaging common workflows, Anthropic is reducing the friction that typically slows enterprise AI adoption.
The $61.5 billion valuation the corporate commanded in its March fundraising round — up from roughly $16 billion a 12 months earlier — suggests investors imagine this strategy will work. But the true test will come as these tools move from pilot programs to production deployments across 1000’s of analysts and billions of dollars in transactions.
Financial services may prove to be AI’s most demanding proving ground: an industry where mistakes are costly, regulation is stringent, and trust is every part. If Claude can successfully navigate the spreadsheet cells and data feeds of Wall Street without hallucinating a decimal point within the mistaken direction, Anthropic may have achieved something much more priceless than winning one other benchmark test. It may have proven that AI might be trusted with the cash.
