What should insurers ask an AI vendor before signing?

What insurers need to know before they commit to an AI supplier

Insurers should not treat AI procurement like a normal software buy. The real risk is not just whether the tool works in a demo, it is whether the vendor can prove where data goes, how outputs are produced, and how the service behaves when something breaks.

Before signing, insurers should pressure-test the vendor across data storage, explainability, failure handling, accountability, and contractual control. If those answers are weak, the commercial upside is not worth the governance exposure.

The questions that matter most before signing

At minimum, insurers should ask these questions:

• Where is customer, claims, and broker data stored and processed?
• What data is retained, for how long, and who can access it?
• Can the vendor explain how the model produces outputs in a way your risk, compliance, and operations teams can challenge?
• What happens when the model is wrong, unavailable, or produces inconsistent results?
• What controls exist for human review, escalation, and override?
• Has the vendor documented testing, monitoring, and model change management?
• What contractual protections cover incidents, data misuse, and service failure?

Those questions give you a practical read on whether the vendor is ready for regulated insurance workflows or is still selling a promising prototype.

Book an AI Risk & Efficiency Audit

Why data storage should be examined first

Data storage is usually the first hard filter. Insurers handle sensitive personal data, claims histories, and commercially sensitive underwriting information. A vendor must be able to state clearly:

• the hosting region and any cross-border transfers
• whether data is used to train shared models
• how encryption works in transit and at rest
• whether sub-processors are involved
• how deletion requests and retention rules are enforced

If the vendor cannot answer those points in plain language, that is a governance warning. In practice, insurers should expect written architecture, data flow maps, and contract wording that matches the technical reality.

What good model explainability looks like in practice

Explainability does not mean the vendor recites machine learning terms. It means your team can understand why a recommendation appeared, what inputs influenced it, what confidence or uncertainty exists, and where the limits are.

For insurers, that matters most when AI is shaping decisions around claims triage, fraud detection, customer communications, or underwriting support. Your team should ask:

• Can outputs be traced to source inputs or evidence?
• Can users challenge and override outputs?
• What testing has been done for bias, drift, and edge cases?
• What documentation exists for model updates and version changes?

If the answer is effectively, trust the black box, that is not mature enough for a high-impact insurance workflow.

How failure handling separates serious vendors from risky ones

Every AI system fails at some point. The real question is whether failure is contained. Insurers should ask how the vendor handles outages, hallucinated content, low-confidence outputs, broken integrations, and monitoring alerts.

A credible answer includes fallback paths, manual review queues, incident response ownership, alerting thresholds, and service-level commitments. It should also explain when the system refuses to answer rather than inventing a response.

If the product touches customer outcomes, there should be a documented human-in-the-loop control for exceptions and a clear route for operational rollback.

See Governance Retainers

The commercial and governance checks insurers should not skip

Alongside technical questions, insurers should push on governance and contracting:

• Who is accountable inside the vendor for risk, security, and model oversight?
• What audit evidence can they provide before go-live?
• How are material model changes communicated?
• What rights do you have to pause, exit, or retrieve data?
• What indemnities, liability positions, and incident notification terms are offered?

These are not legal clean-up questions for the end of procurement. They are core suitability questions. A vendor that is evasive early usually becomes more difficult after signature.

The safest approach in practice

The safest approach is to treat AI vendor diligence as a joint exercise between operations, risk, compliance, security, and procurement. Ask for evidence, not reassurance. Run a bounded pilot, define where human review is mandatory, and document what must happen if the tool degrades or gives unsafe output.

That gives insurers a realistic basis for deciding whether the vendor is fit for production, fit only for a low-risk pilot, or not ready at all.

Plan an AI Implementation Project

FAQ

Should insurers allow vendor data to train shared AI models?

Usually not without explicit review and contract control. Sensitive insurance data should have clear restrictions on reuse, retention, and downstream model training.

How much explainability is enough for an insurance AI tool?

Enough for your teams to understand the basis of outputs, challenge them, and document why a recommendation was accepted or rejected.

What is the biggest red flag during AI vendor diligence?

Vague answers on data handling, no clear failure process, and no evidence of monitoring or governance ownership.

Can a strong pilot replace formal governance checks?

No. A useful pilot helps, but it does not replace contract controls, risk review, and operational safeguards.

Who should own AI vendor diligence inside an insurer?

It should be shared across business owners, risk, compliance, security, procurement, and the team that will run the process day to day.