Snowflake Cortex AI for Financial Services is a new suite of AI tools designed to help financial institutions build and deploy agentic AI applications.
These tools allow financial institutions to leverage their private, proprietary data with third-party data and cutting-edge large language models (LLMs) to automate complex tasks, from fraud detection to market analysis.
By bringing AI capabilities directly to your data, Snowflake helps organizations overcome the common challenges of AI adoption. This includes accelerating complex workflows with tools like the Data Science Agent and ensuring secure, governed and seamless integration through standards like the Model Context Protocol (MCP).
Leaders across the financial services industry are investing heavily in AI, with spending projected to reach $93 billion by 2027 — a 29% compound annual growth rate since 2023. This investment is driven by the potential to improve customer experiences, boost efficiency and cut costs. However, many organizations struggle to scale AI from a proof-of-concept to a reliable, commercially viable solution. A significant gap exists between powerful foundational models and the domain-specific context needed to unlock real business value.
Financial institutions rely on vast amounts of proprietary data — from trading records to client information — that foundational LLMs, trained on public data, simply don’t have access to. This creates a critical need for an AI solution that can securely integrate with an organization’s internal data while maintaining enterprise-grade governance.
To address this challenge, we are excited to announce Snowflake Cortex AI for Financial Services, a new collection of AI capabilities built to power agentic workflows. These tools enable complex tasks like market analysis, quantitative research, fraud detection and claims management by providing a secure, unified environment for data access, processing and retrieval.
Snowflake Cortex AI for Financial Services offers a robust ecosystem of financial data and knowledge providers that organizations can integrate with their agentic applications. By combining your internal proprietary data with external data from leading institutions and trusted publishers — including structured data providers like CB Insights, Cotality™, Deutsche Börse, MSCI, and Nasdaq eVestment® through Sharing of Semantic Views (generally available soon); and unstructured data publishers like CB Insights, FactSet, Investopedia, The Associated Press, and The Washington Post through Cortex Knowledge Extensions (now generally available) — you can unlock richer, more context-driven insights.
For the hundreds of financial services customers who already trust Snowflake as their AI data cloud, this launch provides building blocks for a successful AI strategy:
Financial enterprises can now effortlessly ingest and transform data, including unstructured and multimodal data types, to make it AI-ready.
Unstructured data ingestion: Snowflake Openflow is built on open-source Apache NiFi. When coupled with Cortex AISQL’s parsing and pre-processing capabilities, it streamlines data ingestion and enables teams to effortlessly move data across traditionally siloed environments, including customer documents, call center transcripts and claims reports.
Unstructured data transformation: Cortex AISQL embeds AI features directly into SQL functions, allowing data analysts to process and transform unstructured data with ease. For example, a wealth management platform can use Cortex AISQL to transcribe (in public preview) earnings calls, summarize thousands of analyst reports and analyze market mood in real time — turning hours of manual work into seconds.
AI-ready third-party data access: Financial services professionals can extend the context of their AI agents beyond their organization’s boundaries. The Sharing of Semantic Views and Cortex Knowledge Extensions facilitates secure access to AI-ready third-party data through the Snowflake Marketplace. An insurance underwriter, for instance, can leverage Cotality’s Owner Transfer dataset to streamline claims management workflows.
Instead of moving sensitive data to external models, Snowflake allows you to bring performant, state-of-the-art LLMs — from Anthropic, OpenAI, Mistral, Meta and more — directly to Snowflake’s secure environment. This approach maintains your data security and governance, reduces data egress costs, and provides scalable compute, leading to faster, more reliable insights.
Agentic AI systems can autonomously reason, plan and execute multi-step tasks with minimal human intervention. This capability is crucial for financial services companies seeking to automate complex, end-to-end workflows that were previously cost-prohibitive.
Cortex Agents: Now in public preview, Cortex Agents orchestrate across structured and unstructured data sources to deliver precise insights. They break down complex queries, retrieve relevant data and generate more accurate answers, providing efficiency and governance at every step.
Data Science Agent: This autonomous agent brings agentic AI to predictive ML. Using a simple natural language prompt, the Data Science Agent breaks down a modeling problem into distinct steps — from data analysis to training — and provides a validated modeling pipeline. This allows financial teams to build models for tasks like credit risk and fraud detection in minutes, accelerating development and freeing up teams to focus on strategy.
Embedding AI in financial services requires a dual approach: making the technology deeply understand the business and making it easily accessible to everyone while maintaining trust and security.
Model Context Protocol (MCP) provides a standardized way for LLMs to integrate with data, APIs and services. With the recent introduction of Snowflake’s managed MCP Server (in public preview), enterprises can achieve connected and interoperable AI. This allows customers to connect Snowflake AI capabilities, like Cortex Analyst and Cortex Search, to external agents, simplifying application architecture and eliminating the need for custom integrations. One example of this is our announcement with Anthropic to integrate Cortex AI within Claude for Financial Services.
Additionally, Snowflake Intelligence, also in public preview, offers a unified conversational interface that allows business users to go from data to insights without writing code. Powered by intelligent data agents, it brings together:
State-of-the-art LLMs from Anthropic and OpenAI
Structured and unstructured data within your Snowflake environment
Data from third-party vendors, such as news from The Associated Press
Different file types, including PDFs and Excel
A best-in-class data retrieval engine via Cortex Analyst and Cortex Search
Enterprise-grade security and governance controls
Together, Snowflake MCP Server and Snowflake Intelligence enable customers to ask complex, domain-specific questions in natural language by unifying and integrating all of the organization’s information, from structured tables to unstructured documents and from third-party tools and applications to internal enterprise systems. Ultimately, with Snowflake, customers can enable business-critical use cases, like:
A portfolio manager generating investment ideas based on analytics derived from earnings call transcripts, news, company fundamentals, and other market data.
A mortgage banker making informed lending decisions based on risk intelligence from loan documents, property data, financial data and customer communications.
An insurance underwriter making a risk evaluation based on AI-generated risk summaries.
Public trust and regulatory oversight are paramount in financial services. AI observability tools allow teams to make their AI applications more trustworthy and transparent by continuously monitoring for model drift and data quality issues, including:
Risk mitigation: Observability tools help monitor for model drift, which can lead to inaccurate decisions in critical tasks like fraud detection and credit scoring.
Explainability and transparency: Observability provides the necessary audit trails to justify decisions supported by AI.
Operational resilience: Real-time anomaly detection helps ensure systems are reliable and efficient, reducing downtime.
From role-based access controls to end-to-end AI observability, Snowflake offers enterprise-ready security and governance capabilities for both data and AI.
The race for AI in finance won’t be won with models alone; it will be won by seamlessly integrating those models with your enterprise data. Snowflake provides a complete AI solution that unifies your structured and unstructured data to power deep, actionable insights. With Snowflake MCP Servers providing a foundation for connectivity, trust and governance, financial institutions can start driving real business impact.
Snowflake AI is where your enterprise data meets agentic AI to redefine what’s possible in financial services. Request a personalized demo or speak with our sales team to see how agentic AI can accelerate your financial services business.
Learn how Snowflake’s MCP servers connect AI agents to your data.
Read our press release here.
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