EmergingInsurance, actuarial and claims AI

AI Liability Underwriter

Also called AI Liability & Insurance Underwriter · AI Risk Underwriter

An AI Liability Underwriter prices and insures the errors and harms that AI systems and agents can cause. They help a company turn vague AI risk into terms it can quote, accept, limit or decline.

What they do

They read how an AI system is built and used, then judge what could go wrong and what it would cost. They review model documentation, test results, human oversight and the contracts around the product. They build or adapt pricing models for loss scenarios such as wrong advice, biased decisions or an agent acting outside its remit. They write policy wording, set exclusions and limits, and decide when to decline. They talk to engineers, brokers and claims teams, and feed claims learning back into pricing.

What a strong hire delivers

In the first months, a strong hire gives the company a clear view of which AI risks it will cover and which it will not. They produce a working underwriting approach with defined information requirements, a first pricing method, and wording that claims and legal teams can live with. They also set up a way to review quotes and learn from early losses.

How you can tell they are good

Ask them to take a real or imagined AI product and walk through how they would assess it. Strong people name the failure modes, say what evidence they would request, and explain how they would price with thin loss data. They are honest about uncertainty. Be wary of anyone who treats AI risk as a copy of cyber or professional indemnity without explaining the differences.

Why it is hard to fill

This is an emerging role, and it often sits inside other titles such as specialty underwriter, actuary or AI risk lead. Few people have written AI liability cover, and fewer understand how models fail. Most candidates are strong on one side only. Titles rarely match, so searching by job name misses most of the real talent pool.

Where to find them

Look at specialty and professional liability underwriters, cyber underwriters, actuaries and insurance analytics leads who have moved towards technology risk. Others come from claims teams that handled technology errors, or from model risk and validation work in financial services. Insurers, insurtechs, and brokers with technology practices are the main places to start. Some come from AI governance teams.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Liability Underwriter

  1. Tell us about a risk with little loss history that you had to price. How did you decide?

    Strong answers show structured reasoning: scenarios, expert judgement, proxies from related lines, and wide ranges stated openly. They explain how they would update with new claims. Worry about false precision or reliance on a single borrowed benchmark.

  2. What would you ask a company to show you before quoting cover for its customer-facing AI agent?

    Good answers cover intended use, permissions, testing, monitoring, human escalation, incident history and contract terms. They tie each request to a loss driver. Worry about generic checklists with no link to how the system actually fails.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Liability Underwriter to help us decide what AI risks we will insure and on what terms. You will work across underwriting, actuarial, claims and engineering to build a sound approach to a new kind of cover, and to price it with care.

What you will do

  • Assess AI systems and agents for the errors and harms they could cause
  • Build and maintain pricing methods for AI liability exposures
  • Draft policy wording, limits, exclusions and underwriting conditions
  • Decide which risks to quote, restrict or decline
  • Review model documentation, testing evidence and oversight controls
  • Work with claims to learn from losses and update pricing
  • Advise brokers and internal teams on appetite and terms

What you will bring

  • Demonstrated underwriting or pricing of specialty, liability or technology risk
  • Strong grounding in probability and statistics, shown in real work
  • Experience judging risk with limited or noisy data
  • Working understanding of how machine learning systems fail
  • Experience with model validation or risk governance
  • Clear written communication with technical and commercial people

Nice to have

  • Experience with fairness or impact assessments
  • Background in claims handling for technology errors
  • Familiarity with AI regulation and governance practice

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Financial model validationWorkingChallenge financial-model assumptions and limitations
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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