Lloyd'sBroker

From triple data rekeying to sub-five-minute FNOL intake for a Lloyd’s broker

Insurance — Lloyd’s broker

Value in weeks

June 26, 2026

About

How a leading Lloyd’s broker used Nolana to automate FNOL intake and market submission — collapsing a 30-minute, triple-rekeyed process into a sub-five-minute pipeline and freeing senior brokers to act as client advocates.

Industry

Insurance — Lloyd’s broker

The situation: the same claim, keyed three times before it reached the market.

A leading Lloyd’s broker’s operations handle a high volume of First Notice of Loss (FNOL) intakes that require navigating a deeply fragmented claims infrastructure. Because data sits disconnected across multiple systems, claim information is rekeyed three separate times before it ever reaches the market’s Electronic Claim File (ECF).

With rising operational costs and the risk of data errors, the broker wanted to automate intake, remove tedious rekeying steps, and accelerate submissions to the market. Nolana partnered with them to explore how AI could automate the FNOL intake and market submission journey — helping their claims teams work faster and focus on client advocacy.

The recurring bottlenecks

Manual cross-referencing — brokers receive FNOLs by email and must locate policies, check a mud map to identify impacted markets, and key information into an internal tool.
Repetitive data rekeying — the same data is rekeyed by an outsourced team into a workflow tool, and again into Eclipse, before reaching ECF.
High operational costs — outsource providers charge per transaction, so every manual handoff introduces direct, recurring cost.
Data errors and E&O risk — manual rekeying introduced over 1,000 incorrect UCRs across 22,000 claims, damaging client relationships and generating E&O exposure.
Misallocated expert resources — senior brokers, the firm’s most expensive assets, spend up to 30 minutes on a routine intake that should take five.

The broker needed a solution that could plug into existing operations, automate FNOL intake, and bypass outsourced rekeying steps without compromising claim data quality.

<5 min

Intake processing time per claim, down from 30 minutes.

90%+

First-pass accuracy on market mapping and UCR creation.

Outsourced rekeying handoffs — and their per-transaction fees — remaining.

What we shipped — and what changed for the brokers.

We worked closely with the broker’s claims team to design a focused, practical AI workflow targeting three core use cases:

AI FNOL intake & policy matching — automatically parses incoming FNOL emails to extract structured claim data (date, cause, location, insured details) and matches it to the relevant policy to confirm coverage.
Market identification & UCR generation — using the matched policy and mud map data, automatically identifies all impacted markets, layers, and excess positions, then generates the UCR and drafts a narrative for broker review.
Eclipse/ECF submission pipeline — the completed claim entry is pushed directly to ECF via Eclipse/DRI, entirely bypassing the legacy outsource rekeying steps.

Rapid turnaround — intake processing time plummeted from 30 minutes per claim to under five.
90%+ first-pass accuracy — the AI maps markets with over 90% accuracy, creating correct Unique Claim References (UCRs) and requiring minimal edits on narratives.
Outsource dependency eliminated — bypassing manual handoffs removed the transaction fees associated with outsourced data rekeying.

Why it matters

Intake bottlenecks and repetitive data errors stall the processing of crucial claims, straining client relationships and generating unnecessary E&O exposure. Eliminating fragmented system handoffs and transactional outsourcing lets a broker scale effortlessly without ballooning headcount or costs.

With AI handling the heavy lifting of parsing, data entry, and market identification, McGill’s expert brokers can spend their valuable time acting as true client advocates rather than manual data processors.