Moonrock selects mea Operations to run an AI-first MGA
mea Platform and Moonrock Insurance have partnered to implement mea Operations across Moonrock's business. Built as a digital-first MGA, Moonrock is skipping legacy systems and standing up a modern, automated core to scale cleanly, expand globally, and serve brokers with less friction.
Through this deployment, Moonrock will run a unified AI layer across submission intake, data ingestion, document processing, claims handling, and financial operations. The goal is simple: faster cycle times, clearer oversight, and consistent execution across the value chain.
Simon Ritterband, CEO of Moonrock Insurance, said, "From day one, our ambition has been to build an MGA designed for the future of specialty insurance. Partnering with mea allows us to scale using AI as a core operating capability. This gives us the agility to build on our global growth, differentiate against slower incumbents, and deliver a more efficient, modern experience for brokers and partners."
Martin Henley, CEO of mea Platform, added, "Moonrock represents the next wave of MGAs-firms architecting their businesses around AI from the outset. By building on mea Operations across underwriting, claims, and finance, Moonrock is creating an operating model that supports rapid growth, strong governance, and sustained market relevance."
Why this matters for Operations
- One AI layer across core workflows reduces swivel-chair work and manual rekeying.
- Structured intake and automated document processing improve data quality at the source.
- Human-in-the-loop reviews keep controls tight while cutting cycle times.
- End-to-end visibility across underwriting, claims, and finance improves accountability and reporting.
Where mea Operations plugs in
- Submission intake: Normalize broker emails, PDFs, and portals into a single queue; auto-classify and route to underwriters.
- Data ingestion: Extract and validate key fields from binders, endorsements, and schedules; flag missing or conflicting data.
- Document processing: OCR and entity extraction with audit trails for policy docs, COIs, and bordereaux.
- Claims handling: Intake, triage, FNOL to settlement workflows, and improved reserving signals via cleaner data.
- Financial ops: Premium and claims bordereaux checks, reconciliation, and faster close with fewer exceptions.
Operational outcomes to target
- Submission-to-quote time cut by 30-60%.
- Straight-through processing rate lifted across low-complexity submissions and endorsements.
- Exception queue volume reduced via validated intake and automated checks.
- Data completeness and accuracy improved for audit, regulatory returns, and market reporting.
- Expense ratio pressure eased through lower manual handling and fewer reworks.
Practical implementation notes
- Integration first: Map broker channels, policy admin systems, and claims platforms; connect via APIs and secure file drops.
- Define golden fields: Lock a shared data model (classes, limits, clauses, geos) and enforce at intake.
- Set guardrails: Use confidence thresholds and mandatory checks for pricing, coverage changes, and payments.
- Phase the rollout: Start with one product and region; expand once SLAs and exceptions are stable.
- Change management: Train underwriters, claims handlers, and finance teams on new queues, alerts, and approval paths.
- Measure weekly: Track cycle times, STP rate, defect rate, and rework hours; adjust prompts, rules, and routing.
Context for MGAs and AI adoption
Building an MGA on AI from day one avoids retrofitting process debt later. It sets a clean baseline for governance and helps brokers get faster answers with fewer handoffs.
For those new to MGA structures, see the definition of a Managing General Agent here. For document automation fundamentals used in insurance ops, a primer on OCR is here.
Next steps for Ops leaders
- Pick 2-3 high-volume workflows where delays hurt brokers most; define current SLAs and defect rates.
- Stand up a pilot queue with AI-assisted intake and human approval for final decisions.
- Publish new operational metrics to the floor: what moves, who owns it, and how exceptions close.
If your team is building AI capability across operations, you can explore role-specific training resources here: AI courses by job and automation-focused content here: Automation.
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