What insurers will ask for when underwriting AI risk

As artificial intelligence becomes embedded in core business processes, insurers are increasingly required to assess AI-related risk as part of underwriting decisions.

While terminology varies — model risk, algorithmic risk, operational AI risk — the underlying question is consistent: can this organisation demonstrate that its AI systems are governed in a controlled and defensible way?

Why AI governance now matters to insurers

AI systems introduce forms of risk that are difficult to evaluate through traditional controls alone. These include:

Unintended discriminatory outcomes
Opaque or non-explainable decision-making
Rapid system change without documented oversight
Regulatory and enforcement exposure

As regulatory regimes such as the EU AI Act mature, insurers are no longer able to treat AI risk as theoretical. Governance posture increasingly affects coverage terms, exclusions, and premiums.

The limits of questionnaires and self-attestation

Historically, insurers have relied on questionnaires and self-reported assessments to understand technology risk.

In the context of AI systems, this approach breaks down. Assertions such as “we have an AI policy” or “models are reviewed periodically” provide little insight into how governance operates in practice.

As a result, underwriters are increasingly seeking evidence rather than assurances.

What insurers increasingly expect to see

While requirements vary by insurer and risk profile, governance evidence typically includes:

A clear inventory of AI systems in use
Defined ownership and accountability per system
Documented governance and risk decisions
Versioned records showing how systems changed over time
Artefacts that can be independently reviewed

These expectations closely mirror those emerging in procurement and regulatory review — a convergence that reflects how AI risk is now assessed across the market.

Why evidence-based governance changes underwriting outcomes

Where organisations can produce structured, timestamped governance evidence, underwriting conversations shift.

Instead of debating intent or maturity, insurers can assess documented controls, decision histories, and oversight mechanisms.

This reduces uncertainty, accelerates review, and can materially improve underwriting confidence.

The direction of travel

As AI systems become more pervasive, governance evidence is likely to become a standard input into AI-related underwriting decisions.

Organisations that invest early in audit-ready governance are better positioned to respond — not only to regulators and procurement teams, but to insurers as well.

Veriscopic is designed to support this evidence-first approach to AI governance, including longitudinal records via governance drift detection.