For the CUO · Chief Underwriting Officer

Underwrite more submissions, at better selection, without hiring your way out.

You need to see more submissions, quote faster, and hold the pen on selection. FPTInsure builds underwriting AI that reads submissions, structures exposures, surfaces appetite fit, and gives your underwriters back the hours they lose to admin.

What you're up against

The problems we hear from every Chief Underwriting Officer we talk to. If two of these match your week, keep reading.

Submission overload

Brokers send more than you can quote. The best risks age out; the worst ones consume your senior UWs.

Inconsistent data extraction

SOVs, loss runs, and broker emails read differently every time. Your MI is only as good as the extraction.

Appetite fit is a shared drive

Rules live in someone's head and a 2019 PDF. Junior underwriters bind risks the appetite doesn't want.

Actuarial and UW are still two teams

Rating feedback is quarterly, not daily. Portfolio drift is caught after it costs money.

What good looks like

3–5×
Submission triage throughput per underwriter
50–80%
Structured-field extraction from broker submissions
Hours
Not weeks — from submission to quote-ready file
Underwriter
Owns every bind. AI never auto-binds.

Where we bet with you

The four moves that shift your metrics fastest — and where in our stack they live.

Submission Ingestion & Extraction

SOVs, loss runs, broker email threads → structured risk record with source citations on every field. Underwriter confirms before the file goes to quote.

See underwriting solutions

Appetite & Referral Copilot

Real-time appetite check with the reasons attached. Referral flags before the underwriter opens the file, not after they've spent 40 minutes on it.

See AI Factory

Portfolio & Reserving Analytics

Continuous drift detection, exposure summary, reserving triangulation. Actuarial and UW read the same numbers, daily.

See solutions catalog

Broker & Distribution Copilot

Producer-facing assistant grounded in your appetite guide and rate book. Every answer cites the source doc; no hallucinated coverage.

See solutions

Proof to send your team

Three links that get the rest of your organisation onside.

Verticals

P&C, L&H, Reinsurance, Specialty — see how the stack applies to your book.

Open →
Case studies

Named-carrier outcomes with underwriting metrics.

Open →
Responsible AI

Extraction model cards, fairness testing, no auto-binding.

Open →

What you'll be asked

The questions your peers, procurement, and board will fire back at you. Our answers.

I don't want AI writing my policy.+

It doesn't. The model produces a structured file with citations. Your underwriter reads, edits, decides. No auto-bind, ever.

My brokers' submissions are chaos. Will this actually work on messy data?+

Yes — that's the point. Extraction models are trained on real broker messiness (multiple templates, inline emails, scanned addenda). Every field cites its source page so your UWs can verify fast.

How do I keep the underwriting judgement in-house?+

The AI is a copilot, not a rater. Rating factor changes still flow through your actuarial pricing pipeline. We just give your UWs more time to exercise judgement on the risks that need it.

What about specialty and reinsurance?+

Specialty is where we started. Submission depth and non-standard templates are the norm, not the exception. See the Reinsurance vertical.

Two ways to take the next step.

Pick the one that fits where you are.

Submission-flow diagnostic

Share a redacted week of submissions — we'll benchmark extraction quality and time-to-quote.

Request diagnostic
Book a working session

60 minutes with an ex-underwriter and an AI lead.

Book a meeting