MGA Knowledge 2026-06-05 6 min read

Every submission arrives incomplete. The client discloses the minimum, the broker fills in what they know, and the underwriter decides with less information than they would like. The difference between a profitable book and a mediocre one almost always lives in that gap.

Enrichment is the work before the price

Enriching a submission means completing it with data the MGA fetches itself, without asking the client for anything more: public registries, company history, address geolocation, fleet data, sector loss experience, economic activity indices. Every variable removes uncertainty — and uncertainty is what you end up charging for as loading.

Every data point you do not have at quote time you pay for later, in claims or in lost premium.

Three direct effects on margin

• Better selection — bad risks are declined before they enter, and good risks get a competitive price instead of defensive loading.
• Finer pricing — with more variables, the model separates segments that used to pay the same average rate. That is where the book gains margin without losing volume.
• Shorter cycle — a triaged, complete submission is a submission answered the same day, and response speed is what brokers value most.

Where AI actually helps

The empty promise is "AI that underwrites". The real use is more prosaic and more valuable: triaging submissions against appetite, extracting data from the PDFs and spreadsheets brokers send, checking consistency, and flagging which factors drove the price. The decision stays with the underwriter — they simply make it with the material already prepared.

Intelligence is the loop, not the report

Data only becomes intelligence when the outcome returns to the model. Claim notified, reserve set, claim paid: every event has to feed the next quarter’s price review. Programs that close that loop improve at every renewal; programs that only produce a report discover the problem once it has already cost them.

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