On October 2, 2025, Snowflake unveiled Cortex AI for Financial Services, a comprehensive AI suite designed to help financial institutions unify their data ecosystems and deploy AI models, apps, and agents securely. This announcement marks a significant step forward in making enterprise AI accessible to highly regulated industries.
What is Snowflake Cortex AI?
Snowflake Cortex AI is Snowflake’s integrated AI platform that brings artificial intelligence capabilities directly to where enterprise data already resides-the AI Data Cloud. For financial services companies, this means the ability to deploy AI solutions without moving sensitive data outside their secure environment.
The new Cortex AI for Financial Services specifically addresses the unique challenges facing the finance industry:
- Fragmented data across multiple systems
- Stringent compliance requirements
- Need for robust security and governance controls
- Complex workflows requiring deep domain expertise
Key Components of Cortex AI for Financial Services
1. Snowflake Data Science Agent
The Data Science Agent acts as an AI coding assistant that automates time-consuming data preparation tasks. Financial services firms rely heavily on data scientists for:
- Risk modeling
- Forecasting
- Trading analytics
- Compliance monitoring
However, data scientists typically spend significant time on data cleaning and repetitive coding. The Data Science Agent automates:
- Data cleaning
- Feature engineering
- Model prototyping
- Validation processes
This automation accelerates the journey from raw data to production-ready models, streamlining critical workflows like quantitative research, fraud detection, customer 360 analytics, and underwriting.
2. Cortex AISQL and Document AI
Financial institutions possess vast amounts of unstructured data-market research reports, earnings call transcripts, transaction details, and customer communications. Cortex AISQL (in public preview) introduces AI-powered functions for:
- Document extraction
- Audio transcription
- Image analysis
These capabilities transform workflows across:
- Customer service operations
- Investment analytics
- Claims management
- Next-best-action recommendations
The Document AI features enable efficient processing of documents, audio, and images at scale, eliminating manual review bottlenecks.
3. Snowflake Intelligence
While Data Science Agent and Cortex AISQL serve technical teams, Snowflake Intelligence (in public preview) democratizes data access for business users. This conversational interface allows non-technical stakeholders to:
- Query data using natural language
- Access insights from both structured tables and unstructured documents
- Leverage third-party data and apps
- Make faster business decisions without technical overhead
Snowflake MCP Server: Connecting the AI Ecosystem
Beyond financial services, Snowflake introduced its managed Model Context Protocol (MCP) Server (now in public preview), solving a critical challenge in enterprise AI adoption: connecting AI agents to existing systems.
What is MCP?
The Model Context Protocol provides a standardized way for large language models (LLMs) to integrate with data, APIs, and services. Previously, enterprises needed custom integrations for each connection, slowing AI deployment.
How Snowflake MCP Server Works
The Snowflake MCP Server enables:
Connectivity with Snowflake-built tools:
- Connects Cortex Analyst and Cortex Search to external AI agents
- Unifies structured and unstructured data retrieval
- Eliminates custom integration requirements
Access to proprietary and third-party data:
- Remote agents can connect with Snowflake data
- Access third-party data shares from Snowflake Marketplace through Cortex Knowledge Extensions
- Maintains security and governance standards
Supported Platforms
Snowflake MCP Server integrates with leading agentic platforms:
- Anthropic
- CrewAI
- Cursor
- Devin by Cognition
- Salesforce Agentforce
- UiPath
- Windsurf
- Amazon Bedrock AgentCore
- Azure AI Foundry
- Glean
- Workday
- WRITER
Premium Data Partnerships
Cortex AI for Financial Services includes high-quality data from trusted providers:
Structured data partners:
- CB Insights
- Cotality™
- Deutsche Börse
- MSCI
- Nasdaq eVestment®
Unstructured data publishers:
- CB Insights
- FactSet
- Investopedia
- The Associated Press
- The Washington Post
These partnerships, delivered through Sharing of Semantic Views and Cortex Knowledge Extensions, enable financial firms to combine industry-specific data with proprietary information for deeper AI insights.
Real-World Use Cases
Market Analysis and Research
Financial analysts can leverage Cortex AI to process earnings reports, market news, and research documents, combining unstructured content with structured market data for comprehensive investment insights.
Fraud Detection
By analyzing transaction patterns, customer communications, and external data sources, Cortex AI accelerates fraud detection workflows while maintaining compliance with regulatory requirements.
Customer Service and Support
Cortex AISQL enables automated processing of customer inquiries, transcription of service calls, and extraction of insights from support tickets-improving response times and customer satisfaction.
Claims Management
Insurance companies can automate document processing, extract relevant information from claims forms and supporting documentation, and accelerate claims adjudication.
Quantitative Research
The Data Science Agent streamlines model development for quantitative trading strategies, risk assessment, and portfolio optimization.
Why Cortex AI Matters for Financial Services
According to Baris Gultekin, VP of AI at Snowflake: “By bringing AI directly to where their data already lives and enabling secure interoperability with remote agents, Snowflake is making it easier for highly-regulated industries like financial services to power business-critical use cases.”
The key advantages include:
- Security and Compliance: AI operates on data within Snowflake’s governed environment, maintaining regulatory compliance without data movement.
- Unified Data Ecosystem: Eliminate data silos by accessing proprietary and third-party data in one platform.
- Enterprise-Grade Scalability: Built on Snowflake’s cloud infrastructure, Cortex AI scales with organizational needs.
- Faster Time-to-Value: Pre-built integrations and managed services reduce development time from months to weeks.
Industry Validation
The launch has garnered strong support from ecosystem partners:
Anthropic’s Jonathan Pelosi noted: “Our partnership with Snowflake helps solve [data connectivity] by using MCP to connect each organization’s governed data directly to Claude. Customers can now use Claude’s advanced reasoning on both structured analytics and unstructured documents.”
Ramp’s Ian Macomber shared: “With Snowflake Cortex AI, we can securely tap into and analyze our unstructured customer data, allowing teams across Ramp to ask questions in plain English and get instant answers.”
Optimizing Your Snowflake Cortex AI Investment with Seemore
While Snowflake Cortex AI provides powerful capabilities for financial services and enterprise AI, managing the cost and performance of these AI workloads is critical to maximizing ROI.
As you scale your Cortex AI implementations-processing documents, running Data Science Agent workflows, executing Cortex AISQL queries, and powering Cortex Search-your Snowflake warehouse consumption can increase significantly. Without proper optimization, AI workloads can lead to:
- Unexpected compute costs
- Over-provisioned warehouses
- Inefficient resource allocation
- Difficulty predicting monthly spend
How to Monitor Snowflake Cortex AI Cost?
Snowflake Cortex AI is transforming how teams work with unstructured data, but it comes with a new cost management challenge: a single query can consume thousands of credits without warning. Unlike traditional warehouse costs, Cortex AI charges are based on token consumption and serving compute, making them difficult to predict and monitor. This guide breaks down the cost structure of every Cortex service, reveals hidden charges like “serving compute” that bill even when idle, and provides actionable frameworks to prevent budget overruns.
To learn more about Snowflake Cortex AI cost breakdown >>
Why Warehouse Optimization Matters for Cortex AI
When running Cortex AI workloads-especially document processing, model training with Data Science Agent, or large-scale Cortex Search operations-warehouse configuration directly impacts both performance and cost:
- Under-provisioned warehouses slow down AI insights
- Over-provisioned warehouses waste budget on unused capacity
- Static configurations can’t adapt to variable AI workload patterns
Learn more ways to optimize Snowflake warehouse performance and cost>>
Get Started with Cost-Efficient Cortex AI
As you deploy Snowflake Cortex AI for Financial Services, partner with Seemore to ensure your AI infrastructure operates at peak efficiency. Learn more about how you can monitor Snowflake Cortex AI costs while maintaining performance at seemoredata.io.
Conclusion
Snowflake Cortex AI for Financial Services represents a major advancement in enterprise-ready AI for regulated industries. By combining powerful AI capabilities with robust security, governance, and third-party data partnerships, Snowflake enables financial institutions to deploy AI at scale.
The introduction of the managed MCP Server extends these benefits across industries, creating an interoperable AI ecosystem where agents can securely access enterprise data without custom integrations.
For organizations embarking on their Cortex AI journey, pairing Snowflake’s AI capabilities with Seemore Data delivers both innovation and cost efficiency—unlocking the full potential of AI on the AI Data Cloud.
