Model Context Protocol (MCP) has the potential to transform the insurance value chain by empowering AI assistants to take real-world actions - like retrieving insurance quotes, pulling policy details, or initiating claims.
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Imagine a customer asking their phone’s voice assistant: "How much would it cost to insure my car?" and then receiving an insurance quote that they could instantly accept to bind cover.
Until recently, this wasn’t possible.
The primary reason is that even the smartest AI assistants operated in a vacuum. AI assistants are really good at providing answers, generating ideas, or summarising information, but they couldn’t take action. They couldn’t send an email, retrieve internal system data, or - to the example above - obtain an insurance quote.
This is what the Model Context Protocol (MCP) is designed to change.
In this post, I’ll explain what MCPs are, why they matter, and explore how they could reshape how insurance businesses operate, distribute products, and serve customers in a world increasingly powered by AI.
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is a new open standard for connecting AI models to external tools, data sources, and applications.
It’s recently been referred to as a “USB-C for AI apps”.
Essentially, MCP is a universal interface that allows large language models (LLMs), like Claude or ChatGPT, to move beyond static Q&A and actually interact with the systems that power real-world tasks.
AI that can actually take action
Today’s AI assistants are rapidly becoming the interface layer for how users interact with information, but their usefulness has been limited to the content of their training data or whatever context you manually feed them.
MCP changes that.
It gives AI the ability to operate more like a smart agent, understanding your intent, gathering necessary context from connected systems, and performing the task you’ve asked for. It’s the difference between a helpful colleague giving you advice, and that same colleague actually taking action on your behalf.
This evolution has huge implications for the insurance industry.
MCP: The future of insurance?
Taking a glimpse into the near future, we can conceptualise a number of applications of MCP in the insurance value chain:
1. Conversational Distribution & Chatbots
With MCP in place, AI assistants can respond to prompts like "How much would it cost to insure my car?" by:
- Collecting the relevant product details
- Querying the insurer’s pricing engine or distributor’s product API
- Returning a real quote based on live data
All in the flow of a natural-language conversation.
It doesn’t stop there. Follow-up questions like "What about a newer model?" or "Can I bundle this with home contents?" could trigger new requests via the same protocol.
This creates a new surface for embedded distribution by meeting customers where they are, in the tools they already use.
Here’s a live demonstration by our co-founder Louw Hopley, showing how MCP can be used for insurance quote generation:
2. Agent co-pilots and call centres
From an insurance operations perspective, MCP can supercharge agent productivity.
Imagine a contact centre AI assistant that can:
- Pull up a customer’s policy details in real time
- Fetch recent claims data
- Generate a summary for the agent before they even answer the call
Today, these actions are possible but only with deep, often brittle integrations. MCP introduces a universal way for AI to interact with backend systems in a consistent, governable way. This could significantly reduce average claims handling time and improve first-contact resolution.
3. Claims Automation with Real-Time Context
Claims processing is data-heavy and rules-bound.
An MCP-enabled AI agent could access claim history and relevant policy data, validate supporting documents, escalate anomalies or missing info for human intervention and even trigger payment workflows if conditions are met.
The key is not just automation - rather contextual automation.
MCP lets AI pull the right information at the right time, reducing handoffs and speeding up time to resolution.
4. Continuous underwriting and product personalisation
Traditional underwriting is periodic and discrete. Your risks are underwritten when you apply for a quote to determine your premium, and then only re-assessed if you make changes to your policy or when your policy is re-rated.
With MCP, AI agents could query live data like credit checks, property details, vehicle telematics all on demand, enabling:
- Risk-based pricing updates
- Context-specific product recommendations
- Real-time eligibility checks
This could unlock new product formats like usage-based or just-in-time coverages, and make insurance feel more responsive and tailored.
A particularly useful application could be in green insurance, for example pay-as-you-drive vehicle cover.
A pay-as-you-drive policy would offer premium discounts to drivers who, according to their vehicle telematics, drive fewer miles than the average. Autonomously accessing and using live telematics data to update pricing would allow for more accurate risk-based pricing, saving consumers money.
5. Developer Simplicity and Governance
From a technical perspective, MCP offers a structured, secure, and standardised way to expose system functionality to AI assistants.
Instead of building bespoke plugins or brittle integrations, teams can define a single interface that works across models and vendors.
This is especially valuable in regulated industries like insurance, where traceability, access control and data privacy are paramount. Visibility and oversight between and across different systems is a critical security and privacy concern where distributed value chains exist in insurance, like in delegated authority models (read here to find out why).
MCP allows you to define precisely what an AI can see and do and nothing more.
What’s Next?
As with any emerging standard, the ecosystem around MCP is still forming.
In the same way that APIs transformed software interoperability, MCP has the potential to reshape how businesses operationalise AI. For insurance, this has the potential for faster processes, more personalised products, and radically improved user experiences, both for customers and internal teams.
Given the current rapid rate of development of AI and LLM, it is unlikely that MCP will be the final iteration of this technology.
At the very least, for engineers, product teams and business leaders alike it would be worthwhile exploring which parts of your tech stack could - and should - be made MCP-ready.
