AI for Finance

Connect Claude to Oracle EPM. Safely. Practically. With an open-source MCP server.

Q: Is the FMEPM MCP server for Oracle EPM open source?

Yes. The public v1 of the MCP server is available on GitHub at github.com/fmepm with an MIT license. It includes 7 tools covering core Oracle EPM Cloud operations. The extended v2 managed service version includes 19 tools covering the full month-end close lifecycle and is available as a managed service.

Q: Is it safe to connect AI to Oracle EPM?

Safety depends on how the integration is designed. The FMEPM MCP server respects Oracle EPM's native security model, authenticates through standard Oracle identity, and only exposes the specific operations you configure. For enterprise deployments, enterprise-grade AI platforms such as Amazon Bedrock, Azure AI Foundry, and Google Vertex AI offer Zero Data Retention (ZDR) and data residency controls that make production use appropriate for regulated finance environments.

AI assistants are now good enough to help your finance team run Oracle EPM workflows. The question is how to connect them without compromising security, data residency, or the integrity of your planning model. That is what this service delivers.

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Credentials: Oracle ACE · Author, open-source Oracle EPM MCP server · Oracle EPM Cloud certified · Toronto, Canada. Serving clients worldwide.

Open source

The Oracle EPM MCP Server. Built, maintained, and used in production.

MCP (Model Context Protocol) is the emerging open standard for connecting AI assistants to external systems. FMEPM maintains the first public MCP server for Oracle EPM Cloud. The public v1 has 7 core tools for reading substitution variables, running business rules, exporting data slices, and more. A private v2 extends this to 19 tools covering the full month-end close lifecycle, available as a managed service.

github.com/fmepm →

AI for Finance

AI integration for Oracle EPM Cloud

Connect AI assistants to Oracle EPM through a secure, production-ready MCP server. Custom tools designed for your finance workflows. 2 to 8 weeks typical timeline.

MCP Claude Oracle EPM REST API Groovy Python AWS Bedrock Azure AI Foundry

Until recently, connecting AI to enterprise finance systems meant choosing between a brittle custom build or sending sensitive data to a public AI service. Neither fit regulated finance environments. MCP changes that. It creates a clean, governable boundary between the AI model and your Oracle EPM data, respecting your existing security and keeping control where it belongs.

What this looks like in practice: a CFO asks a question in natural language. The AI translates it into an EPM operation (a data slice, a business rule, a report), runs it through the MCP server with proper authentication, and returns the answer, already checked against your real planning model. No copy-paste. No spreadsheet drift. No AI hallucinations of numbers that don't exist.

When this makes sense for your finance team

Common situation

Your analysts spend hours generating the same variance reports each month. The data is in Oracle EPM, but extracting and formatting it is manual.

Common situation

Executives ask ad-hoc questions that require pulling specific EPM data. The answer takes a day because someone has to build the query.

Common situation

Your team wants to experiment with AI but cannot send EPM data to public AI services due to data residency or regulatory requirements.

Common situation

You have an idea for an AI-powered FP&A workflow but need someone who understands both Oracle EPM internals and how MCP actually works.

From recent work

Built and open-sourced the first public MCP server for Oracle EPM. Community adoption includes public attribution from other Oracle EPM practitioners building on the server. The extended private version, with 19 tools covering the full month-end close lifecycle, is in use for managed service engagements.

What's included in an engagement

MCP server deployment

The Oracle EPM MCP server deployed in your environment, authenticated against your Oracle EPM identity, with the tools your team needs.

Custom tool design

Additional tools built for workflows specific to your organization. Close checklists, variance explainers, rolling forecast assistants, whatever fits.

AI platform integration

Connection to your AI platform of choice. Claude Desktop for individuals, or enterprise platforms like Amazon Bedrock, Azure AI Foundry, or Google Vertex AI for production.

Security and governance

Configuration that respects your Oracle EPM security model, audit logging for AI-initiated actions, and review workflows for sensitive operations.

Team training

Hands-on sessions for both finance users (how to prompt effectively) and admins (how to extend and maintain the MCP server).

Documentation

A written record of your deployment: tools available, how they connect, how to troubleshoot, and what to do as MCP itself evolves.

Typical timeline: 2 to 8 weeks depending on custom tool scope and security review requirements.

Engagement model

How an engagement works

Four phases, consistent across every engagement. Fred works directly with your team throughout. No hand-offs.

Discovery

A working session to understand your Oracle EPM environment, the workflows you want to augment, and your AI platform constraints.

Architecture

Security model design, tool specification, and integration plan. Decided before any code is written, reviewed with your IT and security teams.

Build and test

MCP server deployment, custom tool development, integration with your AI platform, and testing against real EPM scenarios with your team.

Training and handover

Hands-on training for users and admins. Documentation for the long term. Optional managed service for ongoing tool expansion.

Common questions

AI for Finance. Frequently asked.

What is MCP and how does it connect AI to Oracle EPM? +

MCP (Model Context Protocol) is an open standard that lets AI assistants like Claude and ChatGPT safely interact with external systems such as Oracle EPM Cloud. The FMEPM open-source MCP server acts as a bridge, exposing Oracle EPM capabilities (running business rules, reading substitution variables, exporting data slices) as tools the AI can call directly. This turns conversational requests into real EPM actions, validated against your actual data.

Is the FMEPM MCP server for Oracle EPM open source? +

Yes. The public v1 is available at github.com/fmepm under the MIT license. It includes 7 tools covering core Oracle EPM Cloud operations: version discovery, listing applications, reading substitution variables, updating substitution variables, running business rules, exporting data slices, and job status polling. The extended v2 includes 19 tools covering the full month-end close lifecycle and is available as a managed service.

Which AI assistants can I use with Oracle EPM through MCP? +

Any AI assistant that supports the Model Context Protocol can connect. Claude (Anthropic) has the most mature MCP support today. As MCP adoption grows, more assistants are adding support. The server itself is AI-agnostic. It exposes Oracle EPM operations as MCP tools, and any compliant MCP client can use them.

Is it safe to connect AI to Oracle EPM in a regulated environment? +

Safety depends on the deployment. The FMEPM MCP server respects Oracle EPM's native security model (no privilege escalation), authenticates through standard Oracle identity, and only exposes the specific operations you configure. For regulated finance environments, enterprise-grade AI platforms such as Amazon Bedrock, Azure AI Foundry, and Google Vertex AI offer Zero Data Retention (ZDR) and data residency controls that make production use appropriate for regulated workloads. Part of the engagement is selecting the right deployment architecture for your compliance requirements.

What does an AI for Finance engagement typically include? +

A typical engagement covers: MCP server setup and configuration in your Oracle EPM environment, custom tool design for your specific workflows (month-end close, variance explanation, forecasting, ad-hoc reporting), integration with your chosen AI platform, security and governance setup, and team training for both finance users and admins. Ongoing support is available as part of a managed service tier.

How is this different from just using ChatGPT on exported EPM data? +

Copying EPM data into a public AI chat is brittle, manual, and often breaches data governance policies. MCP connects the AI directly to live EPM through a governed channel. The AI can pull data in real time, validated against your actual planning model, and take actions (like running a rule or exporting a slice) that the user has authorized. It is the difference between describing EPM to the AI and giving the AI the keys to operate EPM safely.