Context is cash: Why MCP is the missing layer in GenAI for banking

Unlocking structured intelligence for safer, scalable and compliant AI in finance
 
5 min read
Anh  Pham

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Anh Pham
Practice Director, Custom Applications, Digital Business Services
5 min read
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Context is cash: Why MCP is the missing layer in GenAI for banking

In the rapidly evolving world of , the banking industry has emerged as one of the most fertile grounds for innovation. From virtual assistants to automated underwriting and regulatory copilots, to streamline operations and elevate customer experience. However, as pilot projects scale, a deeper challenge is becoming evident: GenAI systems often lack the business context needed to deliver safe, personalized and enterprise-grade outcomes. Imagine a compliance bot summarizing a regulatory update without understanding which jurisdictions the bank operates in. This failure isn’t about bad models it’s about missing context.

In banking, context isn't just helpful it is essential. Whether it’s knowing a customer’s account type, a banker’s role, a region’s regulations or a product’s eligibility criteria, GenAI models must be contextually aware to function safely and effectively. However, most current GenAI applications use static prompts, hardcoded logic or one-off integrations to inject context. This approach doesn't scale. It leads to inconsistent behavior, governance risks and high maintenance overhead. Without a standardized way to handle context, banks can’t trust GenAI in production-critical environments.

That’s where the Model Context Protocol (MCP) comes in. MCP is an emerging open-source standard introduced by Anthropic in Nov 2024 to inject structured, reusable and governed context into GenAI systems. MCP is the missing link between large language models and enterprise-grade intelligence such as tools, databases and APIs, enabling banks to shift from generic AI responses to trusted, context-aware financial copilots.

Model context protocol 

Before MCP, GenAI applications often followed a monolithic design pattern: prompts were handcrafted, tools were embedded directly into business logic and integrations with systems of record (like core banking platforms and CRM tools) were implemented in a one-off, brittle manner. Each application required extensive custom engineering to align the model’s behavior with the business’s domain knowledge, leading to siloed pilots that couldn’t scale across teams or geographies.

With MCP, the architecture becomes significantly more decoupled and maintainable. MCP introduces a client-server structure in which AI models act as “clients” that request structured resources such as datasets, tool definitions or pre-engineered prompt templates from an MCP server. These resources are categorized into three types:

  • Resources: Structured or queryable data, such as product catalogs, customer profiles or compliance rules
  • Tools: Executable functions or APIs that the model can call, such as “get customer balance” or “validate KYC status”
  • Prompts: Reusable, context-specific prompt templates that can be dynamically filled based on the task and role

This model transforms GenAI systems from static responders into adaptive agents capable of navigating complex, real-world environments.

Unlike prompt-based architectures, MCP introduces a clean separation between the model’s reasoning and the business logic that governs its behavior. With a modular client-server setup, the model interacts with structured contexts such as tools, data sources and prompts through a consistent, standardized interface. This decouples model behavior from application logic, allowing teams to evolve tooling, data and task-specific guidance independently from the underlying AI model.

MCP also encourages reusability and maintainability by externalizing context into portable, versioned components. This means context can be updated, reused across applications or governed centrally without rewriting prompts or rebuilding entire pipelines. By aligning GenAI application design with principles of modularity, reusability and service abstraction, MCP brings GenAI applications closer to the reliability and maintainability standards of enterprise software.

Why MCP matters more in banking 

Unlike consumer-facing applications where a generic chatbot can suffice, banking demands GenAI systems that understand granular context such as product eligibility rules, jurisdictional compliance, customer segmentation and internal entitlements. In this environment, getting the context wrong isn't just inconvenient, it can be costly, non-compliant or reputationally damaging. Here's why MCP is a game-changer:

  1. Reusable context layers

    and strict governance frameworks. Embedding business logic or context directly into AI prompts or duplicating it across each use case leads to fragile implementations and inconsistent decision-making. MCP addresses this by providing a centralized, reusable context layer that ensures AI systems consistently behave within defined operational, legal and customer-specific boundaries.

  2. Governance and explainability

    Regulations like the GDPR, require AI-driven decisions to be explainable to ensure fairness, accountability and user trust. MCP enhances explainability by creating a clear audit trail of the context provided to the model at inference time. Each context pack can be versioned, tracked and tied to specific tasks or users. This makes it possible to trace the reasoning behind an AI-generated recommendation or response, which is critical for compliance reviews, internal audits and regulatory disclosures.

  3. Security and access control

    MCP allows context to be dynamically scoped based on the user’s role, region or task. This ensures that models only receive the information they are authorized to use, reducing the risk of data leakage or inappropriate responses. By externalizing access logic from the model itself, MCP strengthens enterprise-grade security and simplifies compliance with data governance policies.

  4. Scalability across use cases

    One of the biggest challenges in enterprise GenAI adoption is the ability to scale from isolated pilots to cross-functional solutions. MCP unlocks this scalability by offering a standardized method for integrating context across a wide range of banking workflows. Whether building a GenAI assistant for credit managers, a regulatory summarizer for compliance teams or an onboarding guide for customers, the same context packs and integration patterns can be reused and extended.

How can banks implement MCP to realize its benefits? 

Implementing MCP is not a full-stack overhaul it’s an architectural enhancement that can be phased in alongside existing GenAI initiatives. Here’s how banks can get started and progressively unlock the full value of MCP:

  1. Map context requirements

    Identify such as customer support, regulatory analysis or fraud prevention and outline each required context. This includes user roles, customer segments, jurisdiction-specific regulations and tool access permissions. A clear map ensures you’re designing MCP context packs around real business needs.

  2. Develop reusable context packs

    Structure context into modular, MCP-compliant packs. These can include static definitions (e.g., product rules), real-time data sources (e.g., APIs for customer info) and task-specific templates (e.g., onboarding workflows). Reusability across apps is key to reducing duplication and maintaining consistency.

  3. Integrate MCP into GenAI stack

    Embed the MCP client into your GenAI orchestration layer so that models can dynamically fetch the right context at runtime. This step connects your language models with the broader enterprise environment tools, data, policies without re-engineering model behavior.

  4. Apply governance and access controls

    Ensure MCP usage aligns with compliance and security standards. Set role-based access rules, maintain version histories, and log context usage for traceability. capability can help establish comprehensive governance frameworks.

  5. Scale across functions and teams

    Once validated, scale MCP by reusing common context components across departments. Standard context like user roles or compliance flags can serve multiple GenAI use cases, from internal bots to customer-facing assistants, enabling faster rollout and unified behavior. can provide best practices for implementation.

Enterprise-ready GenAI models

MCP allows banks to inject context securely, scalably and systematically into every GenAI interaction. It doesn’t just make models smarter, it makes them enterprise-ready. By decoupling business logic from prompts and structuring context into reusable, governed components, MCP enables the creation of intelligent AI agents that can operate reliably across complex workflows and organizational boundaries.

As GenAI moves from the to the front lines of banking, MCP will be the invisible backbone that powers a new generation of autonomous, context-aware AI agents turning isolated intelligence into coordinated, trustworthy action at scale.

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