How can AI help specialty underwriters prepare better questions for brokers?

Specialty underwriters often need more information than a submission provides. The quality of the next question can determine whether the broker supplies useful evidence or simply adds another layer of narrative.

AI can generate a long questionnaire in seconds. Length is not the capability. Useful questions focus limited broker and underwriting attention on uncertainties that could change appetite, terms, price, line size or referral.

An underwriter can use AI to widen and structure candidate questions, then apply class knowledge, proportionality and relationship judgement before using them.

Better questions focus scarce broker and underwriting attention

A complex submission may contain a contradiction, an unexplained loss, a control described without evidence or a dependency that appears only in a supporting document. These gaps are not equal.

One may affect whether the risk fits appetite. Another may change a condition or exclusion. A third may be useful background but have little effect on the current decision. Sending every possible question can obscure what is genuinely material and slow the conversation.

Question wording also shapes the answer. “Confirm that your backup controls are adequate” encourages agreement. “Describe how recovery was tested, when the latest test occurred and what failed” asks for evidence. A leading question can make an assumption look confirmed without improving the risk view.

AI is useful when it helps the underwriter see different lines of inquiry. It is weak when it turns a sparse prompt into a generic insurance checklist.

Checklists support consistency but cannot anticipate every specialty risk

Class-specific proposal forms, minimum-information requirements and underwriting guidelines help teams ask consistently about known exposures. Experienced underwriters then use broker dialogue to understand what is unusual, test controls and explore how the risk behaves.

That combination remains important. A checklist can stop a common fact from being missed, while professional conversation can follow an unexpected answer. Specialty risks are often non-standard enough that the second or third question matters more than the first.

AI should sit inside this process. It can compare the reviewed submission with appetite and known information requirements, group gaps and suggest follow-ups. It does not know which question is commercially proportionate or how a broker will interpret its tone unless the underwriter supplies and reviews that context.

The source matters too. A question based on an AI inference should not be presented as though the submission established the underlying concern.

Turn evidence gaps into decision-changing questions

Create a question map with four columns: the gap, its source, the decision it could affect and the evidence that would help. This forces each candidate question to have an underwriting purpose.

Ask AI for several types of question:

• Clarification questions resolve inconsistent figures, dates, terms or scope.
• Control questions seek evidence about how a mitigation operates and is tested.
• Scenario questions examine what happens when a dependency or control fails.
• Comparison questions explain why the risk differs from a prior year, location or peer exposure.

Require neutral wording and ask the model to label any assumption behind the question. Then review the candidates. Remove duplicates, unsupported allegations and items already answered in the file.

Rank the remaining questions by potential decision impact and uncertainty. Lead with the issues the broker needs time to investigate. Group related points so the conversation has a clear structure rather than feeling like an automatically generated interrogation.

Before the call, decide what different answers may mean. A satisfactory control record might resolve the concern. An unknown dependency may require a referral. An inconsistent loss account may require further documentation. This preparation helps the underwriter listen for evidence rather than for confirmation.

Protect proportionality, neutrality and the relationship

Read every question before use. Check accuracy, relevance, confidentiality and tone. AI-generated text should not be sent directly to a broker merely because it is grammatically polished.

During the conversation, follow the answer. A broker may provide context that changes the original hypothesis or reveals a more important issue. The underwriter should be willing to leave the AI-prepared sequence and ask the next relevant question.

Record the response in the approved underwriting file and update the risk view. If the answer remains incomplete, keep that uncertainty visible. The next step may be clarification, a term, referral, decline or a decision to proceed within authority.

Practical learning can give underwriters the same fictional gap map and compare the questions they choose. Facilitation can examine whether each question is evidence-led, neutral and decision-relevant. The strongest set is rarely the longest.

Example

A hypothetical contingency underwriter reviews an international event submission. AI helps group gaps around venue dependency, weather planning, supplier concentration and cancellation rights.

The underwriter removes speculative questions and links each remaining point to appetite, a proposed condition or a referral threshold. A question about supplier resilience is rewritten to ask for the named alternatives, contractual access and latest test rather than a general assurance.

The broker call concentrates on four material uncertainties. The answers change the team's view of one dependency and identify a wording question for specialist review.

FAQs

• Can AI decide which broker question is material?

  AI can rank questions against criteria supplied by the underwriter, but materiality depends on the risk, class, wording, appetite, authority and proposed terms. The underwriter remains responsible for deciding what deserves attention.

• Should AI-generated questions be sent directly to a broker?

  No. Review them for factual basis, proportionality, duplication, confidentiality and tone. The underwriter should also decide the sequence and be ready to adapt the conversation when an answer changes the line of inquiry.

• How should an underwriter use an incomplete broker answer?

  Keep the uncertainty explicit and consider what it means within appetite and authority. The appropriate response may be another clarification, a term, referral, decline or proceeding with a documented rationale. AI should not fill the gap.