Building AI-powered solutions in financial services is not easy, and one of the biggest challenges is scaling them safely across the enterprise. This is where Model Context Protocol (MCP) comes in, providing a universal interface for enterprise AI and standardized ways for large language models (LLMs) to securely interact with tools, data, memory and policies.
Developed as an open standard, MCP creates a unifying control layer that makes AI-native systems composable, secure and observable across the enterprise. Instead of scattered pilots and duct-taped APIs, MCP offers a consistent foundation on which to build intelligent, compliant and scalable products – fast. In this article, we explore MCP and how it enables enterprise AI at scale, before offering a deeper dive into financial services MCP strategies, benefits and risks.
Historically, AI solutions have been developed through disconnected pilot solutions using, for example, a few prompts hardcoded into applications and individually-integrated API calls to LLMs. Most organizations have developed AI tools in silos across technology teams, operations teams and business leaders.
These early implementations have often lacked foundational capabilities – such as reliability and reusability – that are critical for scalable deployment. Many AI projects have also been put in place without robust centralized governance, observability and policy enforcement, and have therefore introduced operational risk and complexity.
The result has been a proliferation of AI innovation without the infrastructure to scale, and experimentation without the guardrails to ensure oversight. MCP offers a way to begin addressing this industry challenge.
What is MCP?
MCP is a standardized framework that enables seamless interaction between AI models and external tools, data sources, or environments by providing them with structured context for better responses and integrations. It is a context-aware operating layer that can connect agents to the enterprise systems securely, consistently and transparently using the following key capabilities:
- Function calling with access control. MCP standardizes API metadata – such as schemas, parameters and authentication – into a structured format that language models can easily understand. This enables accurate API calling, smart tool selection, and seamless integration of new APIs without retraining the model.
- Memory lifecycle & scoped context. This MCP feature helps ensure that each agent operates with only the context it needs – no memory leaks, no compliance gaps.
- Prompt & model management. MCP also offers version-controlled access to prompt libraries, with built-in governance hooks – avoiding the need to hardcode prompts into applications.
MCP works alongside agent frameworks such as LangChain or LangGraph and offers a unified way to plug these frameworks into an organization’s existing infrastructure.
When implemented correctly across the enterprise, MCP simplifies and secures AI adoption at scale. For example, it allows IT & AI teams to declaratively connect models to data, tools and memory without writing glue code.
Risk and compliance teams, meanwhile, can leverage built-in logging, access control and audit trails for every model interaction. Finally, product and business teams can rapidly deliver intelligent, context-aware AI-driven products, over which they maintain full observability and control.
Example use case: real-time loan origination
In many financial institutions, AI has emerged through scattered, reactive efforts. While one department experiments with prompt-based chatbots, another rolls out agentic workflows for document handling. Risk and compliance teams are often left to catch up – layering governance in after deployment, if at all.
This fragmented approach leads to a troubling trade-off: automation advances in limited areas, while the deployment of innovation across the enterprise stalls. Product teams hesitate to scale intelligent solutions due to inconsistent tooling, and risk teams block progress due to the lack of observability and controls.
Without a shared layer to govern how models interact with data, tools and logic, AI initiatives remain siloed, uncoordinated and difficult to scale. Business leaders see isolated wins, but no enterprise transformation. MCP helps to solve this by acting as the control layer for the AI estate of financial institutions, with real-time loan origination offering one example application.
The diagram shows how a conceptual AI-powered Loan Request Agent would utilize MCP to process loan requests, validate data and interact with internal tools and databases. The flow is initiated when a customer applies for a loan through an app, triggering an MCP-governed agent.
MCP and real-time loan origination
Every decision and exception is logged centrally and, where necessary, cases are escalated to human review with full memory traceability. The outcome is faster, more personalized lending with built-in compliance and operational control, making it safer to utilize AI within a critical banking process.
As our table summarizes, the infusion of AI agents using MCP in this fashion can help deliver both direct and indirect benefits across loan origination in areas such as product testing, processing and governance.
| Impact Type | Potential Benefit |
|---|---|
| Direct | Faster product testing |
| Direct | Reduction in processing time |
| Indirect | Improved load offer acceptance |
| Indirect | Real-time, context-driven decisioning |
| Indirect | Improved governance & traceability |
Risk & governance: MCP reshapes risk
In financial services, MCP offers powerful capabilities for integrating enterprise AI. However, it can also introduce several critical risks if not properly managed – particularly concentration risks.
For example, a centralized failure in MCP configuration can cause errors across multiple workflows. Over-centralization of privileges poses another threat, where a single privileged user could unintentionally disrupt essential AI functions such as fraud detection.
The rapid evolution of MCP components can lead to specification drift, causing failures in dependent agents due to uncoordinated schema changes. The lack of proper governance and revision control can allow AI agents to drift into non-compliant behavior without triggering alerts.
Meanwhile, any ambiguities in policy enforcement will further complicate matters. For instance, inconsistent rules across business lines may result in conflicting data retention practices. To manage these and other risks, financial institutions should:
- Treat MCP as mission-critical technology infrastructure
- Apply compliance rigor to the control layer itself
- Blend runtime observability with preventive guardrails.
The risks should be mitigated through robust mitigation strategies, including canary testing, zero-trust architectures, schema version control, and policy-as-code frameworks. AI-native finance requires not just secure agents, but a secure governance layer to ensure oversight, compliance and resilience.
Building AI with backbone
MCP makes it easier and quicker for financial institutions to innovate and improve their operational efficiency using new AI technologies. It also enables businesses to rapidly deploy AI-driven products and go to market with new service offerings.
At the same time, MCP alters the nature of the risks posed by scaled AI deployments. It must be used with care to maximize the considerable benefits, while also minimizing concentration and other risks.
Capco is helping many financial services clients to infuse AI capabilities into their internal- and external-facing digital solutions. We can assist our clients to deploy MCP and other emerging technology frameworks to solve real business challenges in innovative ways. Contact us to learn more about how we can help.