Rearchitecting financial services customer experience for the MCP generation
AI is transforming how financial institutions serve customers, support employees, manage financial services, and operate across increasingly complex technology environments. But one question stands above the rest: Who controls the future of your customer and employee experiences, your institution or your technology vendors?
At Avaya, we believe the answer should be you. That is why we are embracing the open future of AI, building the Avaya Infinity® platform to support Model Context Protocol (MCP).
It is the right move for financial institutions that need to innovate without abandoning trusted systems, sensitive financial data, established workflows, or the security and governance requirements that protect customers and the institution.
What is MCP?
MCP stands for Model Context Protocol, an emerging open standard that enables AI models such as GPT, Claude, or Gemini to securely and reliably interact with external tools, data sources, APIs, and user context in a structured way.
For financial services, this can give AI access to the context needed to understand a customer, account, transaction, service request, financial relationship, or interaction across multiple systems. Instead of operating in isolation, AI can work with relevant financial context, established workflows, permissions, and operational intent.
The result is AI that can become significantly more useful across customer service, fraud operations, lending, payments, card services, wealth management, collections, branch support, and other financial environments where accuracy, security, continuity, and trust matter.
"This is not a 'wait-and-see' moment. Avaya believes the time to be intentional about building the definitive open orchestration engine for the modern enterprise is now."
David Funck, Chief Technology Officer, Avaya
How does MCP work?
MCP is built on a client → model → server architecture:
- The Client gathers context such as customer or employee information, role, current task, permissions, interaction history, or service environment.
- The Model such as Claude or GPT receives the context and a list of available tools or actions.
- The Server hosts approved tools, APIs, databases, and financial systems and executes authorized actions requested by the model.
All communication follows structured schemas so interactions can be interpretable, traceable, and governed, an essential requirement when AI is interacting with sensitive financial information, regulated processes, and high-value customer relationships.
Avaya Infinity with MCP: Achieving AI-powered financial communications intelligence
Financial institutions can analyze customer experience data, identify trends, and create real-time information views beyond traditional dashboards. Authorized teams—including CX leaders, supervisors, fraud, operations, and branch support—can access necessary insights while safeguarding financial systems and data.
Removing friction from financial AI with MCP
For too long, bringing AI into financial services customer experience has meant choosing between two difficult options:
- Lock in to a single provider's closed ecosystem, limiting flexibility as AI models, customer expectations, regulatory requirements, security priorities, and business strategies evolve
- Create costly, one-off integrations across core financial systems, CRM, payments, lending, fraud, card services, wealth platforms, knowledge systems, identity services, and other applications
This false choice slows innovation and places additional pressure on already complex financial technology environments. It can also fragment the customer experience, especially when information is distributed across legacy and modern platforms.
Enter MCP, an open, vendor-agnostic standard designed to help AI models interact with tools, data, and operational logic securely and at scale.
Think of MCP as a universal connection layer for AI. Instead of building a different connection between every model and every financial system, organizations can create a standardized way for approved AI capabilities to access the context and tools they need.
As the AI ecosystem evolves, this approach can give financial institutions greater freedom to innovate while maintaining control over the systems, data, workflows, and customer relationships that make their institution unique.
MCP enables AI systems to operate with dynamic awareness of customer, employee, session, transaction, and financial context.
For Avaya Infinity, this means AI models can deliver experiences informed by relevant information across complex financial environments. A customer interaction could draw upon approved account, CRM, payment, fraud, lending, knowledge, and interaction data while respecting the permissions governing each source.
That level of contextual intelligence can be especially valuable for financial institutions managing thousands of employees, products, systems, workflows, integrations, transactions, and customer journeys across multiple channels and lines of business.
Shaping the future of open AI for financial services customer experience
Avaya is not just talking about open AI. We are actively helping shape open standards while bringing native MCP support to Avaya Infinity for secure, model-agnostic, enterprise-ready orchestration.
For financial institutions, that creates three important advantages:
- Control: Gain more control over AI models and tools so you can select capabilities based on the requirements of each financial use case instead of the limitations of a single vendor.
- Governance: Strengthen interoperability and oversight across customer experience systems, financial data, workflows, and AI so capabilities can be introduced responsibly.
- Future-readiness: Build an AI architecture designed for complex financial environments where security, resilience, transparency, auditability, and long-term flexibility are essential.
Collaboration with Databricks
As part of our commitment to secure, scalable, and open AI, Avaya is collaborating with Databricks to bring enterprise-grade governance and data privacy capabilities to the MCP implementation within the Avaya Infinity platform.
For financial institutions, this approach can help establish controlled access between AI and the structured and unstructured data distributed across the enterprise.
With fine-grained access controls, audit logging, and governed integration with enterprise data sources, financial institutions can explore AI-powered customer and employee experiences while maintaining greater visibility into how information is accessed and used.
"Generative AI offers tremendous potential to transform customer experiences, and we are thrilled to collaborate with Avaya to help organizations quickly unify their data, simplify data and AI governance and security, and ultimately deliver AI that understands their business."
Heather Akuiyibo, VP of GTM Integration, Databricks
From fragmented financial systems to freedom with MCP
A typical financial institution may have dozens or hundreds of tools and data sources, including core financial platforms, CRM, payment systems, lending applications, fraud detection, card platforms, wealth management systems, identity services, knowledge bases, document repositories, analytics environments, and workforce applications. At the same time, multiple AI models, copilots, applications, and agents may need controlled access to those systems.
Before MCP, connecting each system to each AI model could require its own point-to-point integration. As the number of systems and AI capabilities grows, so does the development effort, maintenance burden, complexity, and technical debt. MCP changes that paradigm by introducing a standardized intermediary layer. Instead of solving an increasingly complex N x M integration problem, financial institutions can move toward a more scalable N + M model. Each financial tool can expose approved capabilities through an MCP server, while AI models and applications connect through MCP clients. Once components can communicate through MCP, financial institutions gain a more flexible foundation for connecting AI with existing and future systems. That can dramatically reduce redundant integration work while protecting investments already made in the technologies and workflows that support customers today.
This matters because financial services customer journeys rarely live in one application. A single interaction may touch communications, CRM, core financial systems, payments, lending, fraud, cards, identity, knowledge, authentication, and analytics. Avaya Infinity with MCP can help connect those technologies, insights, channels, and workflows so financial institutions can modernize the experience without requiring the entire technology environment to be replaced first.
The potential ROI of MCP for financial institutions
With MCP, Avaya Infinity users can unlock deeper business value by giving AI access to more relevant context and approved enterprise capabilities.
For financial institutions, that can mean faster customer service, more informed fraud interactions, more efficient employee workflows, more natural conversations, greater workforce productivity, and better continuity throughout the financial journey.
MCP creates the foundation for generative and agentic AI that is not simply intelligent, but situationally aware of the customer, employee, account, transaction, financial relationship, and permissions surrounding an interaction.
| Before MCP | After MCP | |
| Integration | Custom connectors between individual AI models and financial systems | A standardized MCP interface can work across models and approved financial tools |
| Deployment time | Longer implementation cycles driven by bespoke integration and engineering | 50–70% faster, lower costs via reuse (quiq.com, Palma AI, Humanloop, aibase.com) |
| Data access | AI operates with limited visibility into customer and financial context | AI can access approved real-time context across core financial platforms, CRM, payments, lending, fraud, and other systems |
| Risk management | Employees manually search multiple systems to understand customer needs and determine next steps | Instant cross-system checks reduce fraud by 30% |
The future of financial AI is open
Financial institutions have spent decades building the systems, processes, data, expertise, security controls, and customer relationships that define their institutions. They should not have to surrender control of those investments to participate in the AI era.
The world's leading AI technology providers are increasingly embracing open, contextual approaches to AI. Avaya shares that vision. By bringing MCP to enterprise customer experience, Avaya can help financial institutions connect AI with the systems they already trust, adopt the models that best serve each use case, preserve greater control over their architecture, and continuously innovate as the AI ecosystem evolves.
The future of financial services customer and employee experience should not be defined by a closed technology stack. It should be defined by what your institution can make possible.
Learn how Model Context Protocol reinforces Avaya’s open AI strategy
Glossary
Full glossary of terms below.
| Term | Definition | Why It Matters for Financial Services |
| MCP (Model Context Protocol) | An open protocol that standardizes how AI models interact with tools, APIs, and memory via structured context objects. | Creates a scalable way to connect AI with core financial platforms, CRM, payments, lending, fraud, knowledge, and other financial systems. |
| Context | A bundle of structured information passed to a model—such as user ID, session history, roles, time, or tool availability. | Helps AI understand the customer, employee, account, transaction, relationship, and situation surrounding an interaction. |
| Model | The generative AI (e.g., Claude, GPT-4, Gemini) that receives the MCP context and generates responses. | Gives financial institutions flexibility to select different AI models based on security, performance, risk, cost, or use-case requirements. |
| Client | The application or interface (e.g., chat window, agent framework, customer portal) that gathers and sends context to the model. | Can include mobile banking applications, customer portals, employee desktops, virtual assistants, and contact center interfaces. |
| Server | The logic layer that hosts tools, APIs, functions, and data sources the model can use via MCP. | Creates a governed way for AI to interact with financial systems without giving models unrestricted access to sensitive financial resources. |
| Tools | Discrete functions or APIs (e.g., “search knowledge base,” “check ticket status,” “update CRM”) exposed to the model via MCP. | Allows AI to move beyond conversation and assist employees and customers with real financial tasks. |
| Schemas | The structured definitions (in JSON or YAML) for how tools and context should be represented and passed to models. | Creates consistency between AI models and financial systems, making integrations easier to understand, govern, audit, and troubleshoot. |
| Memory | Persistent context retained across sessions (user history, preferences, past actions) provided to the model. | Can enable more continuous customer experiences so customers do not have to repeatedly explain the same issue or relationship. |
| Ephemeral Context | Short-lived, session-specific information (e.g., current task, temporary variables). | Helps AI remain focused on the immediate financial interaction without unnecessarily retaining temporary information. |
| Agent | An AI system that can take actions, invoke tools, and make decisions based on MCP context — often used in autonomous or semi-autonomous workflows. | Provides the foundation for controlled agentic AI that can assist with multi-step financial processes and workflows. |
| Orchestration | Coordinating multiple steps, tools, and model outputs to achieve a higher-order business goal. | Helps financial institutions connect customer interactions with the many systems and processes required to complete complex financial journeys. |
| Observability | The ability to inspect what context was passed, what decisions the model made, and how tools were used. | Supports transparency, governance, troubleshooting, risk management, and oversight when AI interacts with sensitive financial information. |
| Interoperability | The ability for different AI models and platforms to work with a shared structure of tools and context. | Gives financial institutions greater flexibility to evolve their AI strategies without replacing established systems or becoming dependent on a single AI provider. |