The insurance industry faces a pivotal transformation moment. Model Context Protocol (MCP) has moved from experimental technology to production infrastructure, with 16,000+ active servers deployed across enterprises and millions of weekly SDK downloads. For InsurTech leaders, the question is no longer whether to adopt MCP, but how to implement it securely and effectively. Arcade's platform provides the MCP runtime for secure, multi-user authorization so AI agents can act on behalf of users across policy systems, claims platforms, and customer data., directly addressing the core authorization challenge that determines whether AI projects succeed or stall.
Key Takeaways
- InsurTech implementations demonstrate 95% reduction in quote-to-bind time and 80% decrease in customer service response times when MCP enables AI agents to complete end-to-end workflows
- Multi-user authorization,not simple authentication,is the make-or-break factor determining which AI projects reach production versus those that remain demos
- MCP transforms AI from conversation engines into operational systems that autonomously execute policy bindings, process claims, and coordinate underwriting decisions across legacy infrastructure
- Security specifications evolve rapidly: OAuth 2.1 became mandatory in March 2025, with authorization server decoupling introduced in June 2025, requiring platforms that abstract compliance complexity
- 95%+ Fortune 500 companies use serverless architectures that MCP doesn't natively support, creating deployment friction that demands infrastructure-agnostic solutions
- Early adopters achieve $2M annual savings in compliance costs and 30% reduction in operational costs, but only when multi-user authorization is solved at the platform level
- AI/ML teams ship real, tool-using agents faster with safe tool calls; Security teams get enforceable, auditable multi-user authorization; general business teams see faster quote-to-bind, lower handling times, and cleaner handoffs
Critical Use Cases: Where MCP Transforms Insurance Operations
Understanding MCP's impact requires seeing it in action across insurance workflows where AI must coordinate multiple systems with appropriate permissions for each user.
Autonomous Claims Processing
Sure's production deployment demonstrates how MCP enables AI agents to access claim history, validate policy coverage, analyze submitted documents, escalate anomalies to human adjusters, and trigger payment workflows,all while maintaining proper authorization scopes for each customer. This coordination delivered an 80% decrease in customer service response times by eliminating manual handoffs between systems.
The technical advantage lies in MCP's ability to pull the right information at the right time with user-specific permissions. When processing first notice of loss (FNOL), the agent accesses telematics data for one customer, property records for another, and medical provider networks for a third,each with scoped credentials that prevent unauthorized data access. Arcade's multi-user authorized integrations handle this complexity, allowing AI to act on behalf of users without exposing tokens to LLMs.
Real-Time Conversational Distribution
Root Platform's implementation shows how MCP enables new distribution channels where customers interact with AI through natural language. When a prospect asks "How much to insure my car?" The AI queries pricing engines, retrieves live quotes, and responds conversationally,then handles follow-up questions like "What about a newer model?" or "Can I bundle it with home insurance?" without breaking context.
This approach creates distribution efficiency impossible with traditional API integrations. The universal protocol allows AI to interact with backend rating systems, underwriting rules, and policy administration platforms through a single interface rather than brittle, custom connectors. For insurers launching embedded insurance products or voice-first experiences, MCP provides the infrastructure that makes these channels viable.
AI-Powered Agent Co-Pilots
Call center optimization represents immediate ROI for most carriers. MCP-enabled co-pilots pull customer policy details, fetch claims history, and generate case summaries before agents answer calls. Root Platform's implementation reduced average claims handling time while improving first-contact resolution rates.
The differentiation comes from context awareness. Traditional systems require agents to navigate multiple screens and applications. MCP-based co-pilots synthesize information from policy administration, billing, claims, and customer service platforms into unified views,with proper authorization ensuring agents only access data they're permitted to see based on their role and the customer's consent.
Continuous Underwriting and Risk Assessment
Usage-based insurance and just-in-time coverage models require real-time data access that MCP uniquely enables. AI agents query credit bureaus, pull property details, access telematics feeds, and retrieve IoT sensor data on demand,maintaining fresh risk assessments that enable dynamic pricing adjustments.
For green insurance programs, this means autonomous telematics data access that adjusts premiums based on actual driving behavior. For commercial lines, it enables real-time risk scoring that incorporates weather data, supply chain disruptions, and emerging threat intelligence. The key is that each query executes with appropriate authorization,the AI accesses only the data sources and customer records it's permitted to use for that specific underwriting decision.
Document Intelligence and Compliance Verification
Insurance operations generate massive document volumes requiring extraction, validation, and routing. MCP-enabled AI agents coordinate document processing workflows across optical character recognition, data validation, fraud detection, and compliance verification systems.
Across seven European insurers, automating Solvency II and related regulatory reporting with RPA cut compliance FTE effort by about 35% and shortened reporting cycles by around 40%, and one multi-line carrier that added AI-based report generation cut late adjustments by 70% and regulator queries by 40%. Together, these reductions translate into seven-figure annual savings for mid-to-large EU insurers with heavy reporting workloads.
Best Practices: Building Secure, Scalable MCP Infrastructure
Success with MCP requires addressing multi-user authorization, legacy system integration, and compliance requirements that define enterprise insurance IT.
Recreating delegated, scoped multi-user authorization across dozens of tools internally is a multi-quarter effort with high risk; Arcade makes this a governed, repeatable MCP runtime capability. Implement one production use case first (e.g., claims FNOL or quote-to-bind), then scale horizontally.
Solving Multi-User Authorization at Scale
The core challenge isn't authenticating users,it's ensuring AI agents execute actions with the correct permissions and scopes for each individual user at runtime. When an AI processes claims for thousands of policyholders simultaneously, each interaction must use that customer's specific authorization credentials, not shared service accounts or admin tokens.
Arcade’s multi-user authorization and token/secret management model addresses this by managing session-scoped identities with non-predictable session identifiers. When an agent needs to send an email through Gmail or update a policy record in Salesforce, Arcade handles the just-in-time authorization flow,requesting user consent, managing token lifecycle, and ensuring zero token exposure to LLMs. This architecture aligns with enterprise security requirements while enabling the autonomy that makes AI agents valuable.
For insurance operations, this means:
- Claims adjusters authorize AI to access specific customer records without granting blanket database access
- Underwriting agents delegate pricing calculations to AI with read-only access to rating engines
- Customer service AI retrieves policy details with permissions scoped to the authenticated customer's account
- Compliance officers audit every agent action with complete visibility into which user authorized which operation
Integrating Legacy Insurance Systems
Most carriers operate policy administration platforms built on mainframe infrastructure, COBOL codebases, and SOAP APIs that predate modern integration standards. MCP doesn't natively connect to these systems,it requires middleware that translates between MCP's protocol and proprietary insurance platforms.
The strategic approach wraps legacy APIs as MCP tools through custom connectors. Arcade’s MCP framework enables teams to build multi-user authorized integrations for policy administration systems from Duck Creek, Guidewire, and legacy providers. These connectors handle the complexity of ACORD message standards, transaction logging, and backward compatibility while exposing clean MCP interfaces to AI agents.
For hybrid architectures common in insurance, this means deploying MCP infrastructure in VPCs with secure connectivity to on-premises systems. Arcade supports enterprise networking patterns, including VPC isolation for regulated insurance workloads, while modernizing AI capabilities.
Implementing Production-Grade Security
Insurance AI must meet regulatory standards that commercial applications don't face. HIPAA for health insurers, state insurance department requirements, and data residency regulations create deployment constraints that generic MCP implementations don't address.
Security architecture requires multiple layers:
- Token encryption at rest: All credentials stored encrypted, never exposed in logs or passed to LLM providers
- Audit trails: Complete logging of every agent action, which user authorized it, what data was accessed, and what changes were made
- Least-privilege access: AI agents granted minimum permissions needed for each specific task
- Session-based authentication: Time-limited tokens that expire after workflows complete, preventing credential reuse
With SOC 2 Type 2 certification, Arcade.dev becomes the authorized path to production with these key points: Just-in-time authorization validated by independent auditors. Tool-level access controls that inherit from existing identity providers. Complete audit trails for every agent action. VPC deployment options for air-gapped environments.
Tool Evaluation and Quality Assurance
Insurance AI decisions must meet accuracy thresholds that conversational AI doesn't require. An underwriting error costs money. A claims processing mistake damages customer relationships and invites regulatory scrutiny. Testing and validation become non-negotiable.
Arcade's evaluation capabilities automate benchmarking of LLM-tool interactions. For insurance workflows, this means creating test suites that validate:
- Underwriting AI correctly interprets risk factors and applies pricing rules
- Claims processing follows proper escalation protocols for suspicious patterns
- Policy binding AI verifies coverage limits and regulatory compliance
- Customer service agents retrieve accurate information without data leakage
Evaluation frameworks should test not just accuracy but authorization behavior. Does the AI properly handle scenarios where customers haven't consented to data access? Does it correctly escalate when permissions are insufficient? Does it maintain separation between customer records?
Orchestrating Multi-Tool Insurance Workflows
End-to-end insurance processes span multiple systems that must coordinate with proper state management. A policy renewal workflow might touch rating engines, policy administration, billing, payment processing, document generation, and email delivery,each requiring different authorization credentials and error handling.
LangGraph, a stateful orchestration framework built on LangChain, models tool-using agents as graphs (nodes, edges, memory/state) so you can coordinate multi-step workflows while preserving context.
LangGraph orchestration with Arcade MCP servers provides the coordination layer for these complex workflows. The orchestration framework manages state transitions, handles error recovery, and ensures data consistency across systems while Arcade handles the multi-user authorization for each tool invocation.
For insurance operations, this enables:
- Sequential workflows: Underwriting decision triggers policy issuance, which initiates billing setup, which sends confirmation email
- Parallel execution: Claims AI simultaneously requests medical records, pulls policy coverage, checks fraud databases, and validates repair estimates
- Conditional logic: Based on claim amount, route to automated approval, senior adjuster review, or special investigation unit
- Rollback handling: If payment processing fails, reverse policy activation and notify customer
Connecting to Insurance Data Infrastructure
AI agents need access to data lakes containing actuarial tables, claims history, customer profiles, and risk models. However, this data often includes sensitive PII that cannot be exposed to external LLM providers.
Arcade's real-time access model enables AI to query insurance data warehouses using managed tokens and secrets, without moving sensitive data outside secure environments. The platform manages authorization tokens that allow agents to execute database queries, retrieve analysis results, and access customer 360 views,all while maintaining data residency and compliance requirements.
For usage-based insurance, this means streaming telematics data from vehicles to risk models to pricing engines without exposing individual driving patterns to LLM training data. For health insurance, it enables AI to query claims databases for utilization patterns while maintaining HIPAA compliance through proper authorization controls.
Scaling for High-Volume Operations
Insurance has predictable traffic spikes during renewal periods, open enrollment windows, and catastrophe events. An MCP infrastructure that handles 100 requests per second during normal operations must scale to thousands during a hurricane when claims flood in simultaneously.
Worker management becomes critical. Arcade provides worker scaling options that absorb renewal- and CAT-driven traffic spikes without sacrificing multi-user authorization controls. The platform's rate limiting (1,000 requests per minute standard, higher limits for enterprise) ensures consistent performance while protecting backend systems from overload.
Cost optimization requires balancing hosted convenience with infrastructure control. Carriers processing millions of annual transactions typically implement hybrid architectures: cloud-hosted workers for variable workloads and self-hosted workers for baseline operations where predictable costs matter.
Meeting Regulatory Compliance and Audit Requirements
State insurance departments increasingly scrutinize AI-driven decisions for fairness, explainability, and compliance with anti-discrimination regulations. When AI underwrites policies or settles claims, regulators demand documentation showing how decisions were made and what data was used.
Audit trail architecture must capture:
- Decision provenance: Which AI model made the decision, what data it accessed, what rules it applied
- Authorization records: Which user or customer authorized the AI to take the action
- Data lineage: What systems provided input data, when it was accessed, how it was transformed
- Outcome tracking: What action the AI took, what changes resulted, what notifications were sent
Arcade's audit trails provide transaction observability that meets these requirements. Every agent action is logged with complete context, enabling compliance teams to demonstrate regulatory adherence and investigate anomalies when they occur.
Current Trends Reshaping Insurance AI
Understanding MCP's evolution helps InsurTech leaders anticipate infrastructure needs and competitive dynamics.
Enterprise Adoption Accelerating Despite Maturity Gaps
Major enterprises including Block, Bloomberg, Amazon deployed MCP production systems despite acknowledged security vulnerabilities and architecture limitations. Block enabled thousands of employees to use MCP-based tools within months, achieving up to 75% time reduction on daily engineering tasks. Bloomberg reduced time-to-production from days to minutes by connecting AI researchers to company infrastructure through MCP.
This "adopt while evolving" approach signals that competitive pressure outweighs risk concerns. For insurance, it means waiting for MCP to mature fully will cede ground to competitors already capturing efficiency gains. The strategic response is implementing MCP with proper authorization safeguards rather than delaying until standards stabilize.
Insurance-Specific MCP Applications Emerging
Vertical-focused implementations demonstrate MCP's flexibility for insurance workflows. Root Platform enables usage-based insurance with real-time telematics data access, Sure provides end-to-end policy lifecycle management through AI agents, and Sixfold focuses on underwriting decisioning that incorporates diverse data sources.
These purpose-built solutions share common patterns: they solve multi-user authorization through platform-level infrastructure, they integrate with legacy insurance systems through custom connectors, and they prioritize compliance and audit requirements from day one. The trend suggests successful insurance AI requires domain expertise embedded in the infrastructure layer, not just general-purpose MCP servers.
Security Evolution Through Rapid Specification Changes
MCP security standards evolved dramatically in 2025. OAuth 2.1 became mandatory in March, authorization and resource server decoupling was introduced in June, and tool poisoning vulnerabilities discovered in April led to new validation requirements. Third-party solutions including Okta's Cross-App Access emerged to address enterprise requirements not met by core specifications.
For insurance CIOs, this rapid evolution creates deployment uncertainty. Implementing MCP directly means tracking specification changes and updating implementations as standards evolve. The alternative is platforms that abstract compliance complexity,maintaining certification and security controls while shielding applications from protocol churn.
AI Operating System Model Extending Beyond MCP
Industry leaders frame MCP not as an endpoint but as a foundational layer in comprehensive "AI Operating Systems." This vision includes MCP for tool integration, complementary protocols like Google's A2A for agent-to-agent communication, and orchestration frameworks that coordinate multiple AI capabilities within unified environments.
For insurance, this suggests the near-term opportunity isn't individual MCP integrations but complete AI-native architectures where multiple specialized agents coordinate through standardized protocols. An underwriting agent might delegate risk scoring to an analytics agent, which calls a fraud detection agent, which escalates to a human underwriter,all communicating through MCP and A2A interfaces with proper authorization at each step.
Low-Code Integration Democratizing AI Implementation
Businesses are expected to use low-code or no-code platforms by 2025, creating unexpected synergy with MCP's standardized tool integration. Visual platforms enable business teams to assemble insurance workflows without deep technical expertise, dragging and dropping MCP tools into process flows.
This democratization accelerates AI adoption but intensifies authorization challenges. When business users build workflows connecting policy systems, claims platforms, and customer data, who ensures proper permission scoping? The answer requires platforms that enforce authorization guardrails regardless of how workflows are assembled,making multi-user authorization a platform capability rather than implementation detail.
FAQs on Enterprise MCP for InsurTech
How do we demonstrate MCP capabilities to executives without full production deployment?
The challenge is that executives need to see business value before approving implementation budgets, but production MCP deployments require significant infrastructure investment and security work. The solution is safe demonstration environments where teams can prototype insurance workflows using MCP without exposing production systems or customer data. These sandboxes should connect to test instances of policy administration systems, allow experimentation with multi-user authorization flows, and provide realistic performance metrics that inform ROI calculations. Arcade's 60-second agent setup enables teams to build working prototypes that showcase MCP-enabled workflows for stakeholder presentations, reducing the gap between concept and executive buy-in.
What's the relationship between MCP and our existing API infrastructure?
MCP doesn't replace REST APIs or enterprise service buses,it provides a standardized protocol layer that AI agents use to interact with those existing systems. Your policy administration platform still exposes the same APIs it always has. MCP servers act as translators that understand both the AI agent's requests and your system's API requirements. The advantage is that once you build an MCP connector for one system, any AI agent can use it through the standardized protocol rather than requiring custom integration work. This creates a reusable tool catalog where new AI capabilities can leverage existing integrations, dramatically reducing the "M×N problem" where M applications each need custom connectors for N systems.
How do we handle authorization when AI agents need to access data across multiple insurance business units?
Multi-business unit operations create complex authorization scenarios where the same AI might need claims data from one unit, policy records from another, and customer service history from a third,each with different permission models. The solution requires session-scoped multi-user authorization that carries user context across tool invocations. When a claims adjuster authorizes an AI to process a claim, that authorization should extend to pulling policy coverage from the policy administration unit and retrieving payment history from the billing unit,but only for that specific customer and only with permissions the adjuster actually has. Arcade's multi-user authorization management handles these scenarios by maintaining authorization state throughout workflows, requesting additional consent when needed, and ensuring each tool invocation uses appropriately scoped credentials rather than elevated service accounts that bypass access controls.
Can MCP work with our mainframe-based policy administration system?
Yes, but it requires middleware that translates between MCP and mainframe protocols. Most legacy policy administration platforms expose functionality through SOAP APIs, proprietary messaging formats, or direct database access. You build custom MCP servers that wrap these interfaces, handling the translation between AI agent requests and mainframe operations. The technical work involves understanding your mainframe's API surface, implementing proper error handling for legacy system quirks, and ensuring authorization flows work with your mainframe security model. Arcade's custom toolkit creation provides the SDK framework for building these connectors, but the domain expertise for your specific policy administration system must come from your team or implementation partners who understand both the legacy platform and MCP requirements.
What happens when our annual renewal period creates 10x normal traffic loads?
Insurance traffic spikes are predictable but dramatic. Renewal periods, open enrollment windows, and catastrophe events create surges that overwhelm static infrastructure. MCP architectures need elastic scaling that spins up workers during demand spikes and releases them when traffic normalizes. Cloud-hosted workers provide this elasticity but at variable costs that CFOs scrutinize. Self-hosted workers offer cost predictability but require capacity planning and infrastructure management. Most carriers implement hybrid models: baseline capacity on self-hosted infrastructure for predictable costs, with cloud bursting during peaks. The key is monitoring that predicts demand surges before they arrive, triggering scale-up operations that provision capacity ahead of the spike rather than reacting after performance degrades.
