AI Underwriting Quality Assurance

Catch every underwriting
gap before it
becomes a loss.

FigTrig reviews every underwriting decision against your own rules. Problems are flagged before they become losses.

See how it works

100%

of decisions reviewed,
not just a 5–10% sample

£4.2M+

in decisions flagged before
binding in our first pilot

Your rules

trained on your guidelines,
not a generic standard

How it works

Up and running in three steps.

Upload your guidelines

Your underwriting rules, authority levels, and risk appetite go in once. FigTrig learns your standards, not a generic set.

Notes are reviewed instantly

FigTrig reads each underwriting note as it comes in and checks every decision against the guidelines you loaded.

Flags are raised early

Gaps and risks are surfaced before the policy is bound. Your team can act while there is still time.

What we check

Six things that matter in every underwriting note.

Risk Identification

Did the underwriter address all the key risks for this type of account?

→ Catches omissions before they become coverage disputes

Pricing Rationale

Is the price justified by the risk? Is the reasoning written down clearly?

→ Ensures every rate can be defended in a claims or regulatory review

Authority Compliance

Did the underwriter stay within their delegated authority for this risk?

→ Prevents unauthorized bindings that expose your firm to uncovered losses

Loss History

Was past loss data reviewed, and did it shape the final decision?

→ Stops the same account being underpriced cycle after cycle

Documentation Quality

Is the note complete enough to defend the decision if it is ever questioned?

→ Creates an audit trail that protects your team when decisions are challenged

Policy Terms Fit

Are the limits, deductibles, and exclusions appropriate for the risks identified in this note?

→ Flags terms mismatches before they create unexpected coverage gaps

Real-world case study

A major European specialty insurer lost hundreds of millions in a single year. Nobody at the top knew it was happening.

When regulators investigated, they found the same root cause every time: underwriting decisions were not being checked against the rules. Notes were written. Policies were bound. But nobody was asking whether the decisions were right.

Management had no visibility. By the time the losses surfaced, it was too late to act.

This is not a one-off story. It is the default state of most underwriting operations today.

FigTrig caught a pricing rationale gap on a £1.8M commercial property risk that would have gone straight to binding. The system flagged it within seconds of the note being submitted; before anyone on the team had even read it.

Senior Underwriter

Commercial Lines, London Market

Built by a team from

University of Oxford Antler
Questions

Common questions.

Most teams are reviewing live underwriting notes within one week. You upload your guidelines once: your underwriting rules, authority levels, and risk appetite. FigTrig learns your standards. No long implementation project or integration required to start.
You upload your guidelines in whatever format they currently exist: PDFs, Word documents, internal manuals. FigTrig reads and indexes them, then checks every underwriting note against those exact standards. It does not use a generic model trained on other insurers' rules.
Yes. Your data is never used to train our models or shared with other customers. All data is handled in accordance with GDPR, and we can discuss data residency, retention periods, and processing agreements before you go live.
FigTrig explains exactly what it flagged and why, referencing the specific section of your guidelines that was not addressed. Your team reviews the flag and decides how to proceed. FigTrig surfaces the issue; the underwriter makes the call.
FigTrig can work alongside your existing underwriting management system via API or by reviewing submitted notes through a lightweight integration. We discuss your specific setup on the demo call and design the workflow around what you already have.
In our pilot, FigTrig reviewed £4.2M+ in decisions and flagged genuine issues that had been missed by manual review. Like any AI system, it is not perfect, which is why every flag goes to a human underwriter for review. The goal is to ensure nothing slips through unexamined, not to replace underwriter judgment.

See it work on your own guidelines.

Upload a few sample notes. We will show you exactly what FigTrig flags and why. No commitment required.

Your data is never used to train our models
Works alongside your existing underwriting systems
GDPR-compliant data handling