There have been AI solutions galore for coding, writing, and mathematics, but a technical domain equally as challenging that could use AI assistance, yet is often overlooked, is finance — until now.
On Tuesday, Anthropic launched the Financial Analysis Solution, which instantly pulls financial data from different data providers, both market feeds and internal. The Claude 4 models can then use that information to assist with your financial workloads, including everything from market analysis to research and investment decisions.
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“The Financial Analysis solution is a comprehensive AI solution that really aims at transforming how finance professionals analyze markets, conduct research, and make investment decisions,” said Nicholas Lin, head of product, FSI, at Anthropic, to ZDNET.
How it works
Partnerships with data providers, such as Box, Daloopa, Databricks, FactSet, Morningstar, S&P Global, Snowflake, and Palantir, allow users to pull information from multiple sources into Claude for analysis without having to context switch manually.
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Claude can then perform tasks using the information and provide direct hyperlinks to the sources of the financial data, which users can check to verify the responses for themselves — a practice Lin encouraged.
“We really do believe that having humans in the loop to understand where the model is producing its responses and getting its data sources is really important,” said Lin. “Especially for high-impact and high-stakes use cases like financial services, it is really important for end users to verify that information.”
The solution builds on Anthropic’s existing AI services, including Claude Code, a powerful feature that lets professionals use the coding assistant directly in their workspace to write or manage code, and Claude for Enterprise, Anthropic’s enterprise-level plan that offers higher security, with expanded usage limits.
Users can also access pre-built MCP connectors for financial data providers, such as FactSet and Pitchbook, to pull in timely external market data. MCP connectors offer users an open standard for securely connecting AI assistants to data systems.
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As financial data is highly sensitive, Anthropic reassures users that the data is not used to train generative models and adheres to the data protection and regulatory compliance required of financial institutions.
“From an enterprise-grade security and compliance perspective, we’re SOC 2 Type 2 certified, and we do not train our generative models with any of the data from our motion customers,” said Lin.
What are some use cases?
Anthropic shares that the use cases for its Financial Analysis Solution span a wide spectrum of tasks, including market research, competitive benchmarking, portfolio analysis, financial modeling, investment memos, pitch decks, and more.
“I’m really excited about the ability for Claude to actually start creating work production and outputs for users, so this includes things like creating PowerPoint documents and Excel from scratch,” said Lin.
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Lin added that the target audience for this solution at launch is analysts, portfolio managers at hedge funds, private equity analysts, investment banking analysts, and wealth management advisors. Some financial institutions cited as users include AIG, AIA Labs at Bridgewater, Commonwealth Bank of Australia, and NBIM.
How to get started
If you are interested in using the Financial Analysis Solution, you should contact the Anthropic sales team to learn more or schedule a demo. Anthropic said Claude for Enterprise and the Financial Analysis Solution are now available on AWS Marketplace.
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While Anthropic has an enterprise and education solution for its chatbot, this is the first time that Anthropic has released a domain-specific version of Claude, and, according to Lin, it won’t be the last.
“At Anthropic, we’re really enterprise-focused at heart, and this is our first foray into a specific domain,” said Lin.
“What interests us in these domains is, fundamentally, we are a research lab at heart, so everything we do needs to push forward, not only the product functionalities, but also the model intelligence.”
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