How Do Coverholders Challenge or Appeal an AI-Influenced Decision?

Coverholders are increasingly encountering decisions that have been shaped, at least in part, by AI.

A bordereaux submission might be flagged for anomalies, a claim might be queried automatically, or a risk score might trigger a referral before a human ever looks at the file.

When that happens, coverholders need to know one thing above all else: how do they challenge it?

The answer does not depend on the technology involved. It depends on whether the managing agent or insurer has built a clear, accountable escalation route, and on the coverholder knowing how to use it.

Why AI-influenced decisions still need a human-accountable appeal route

An AI tool may generate a flag, a score or a recommendation, but it does not make the final decision in any meaningful governance sense.

The managing agent or insurer remains fully accountable under the binder agreement for every outcome that affects a coverholder, whether that outcome was reached manually or with AI support.

This accountability cannot be transferred to the AI system itself. Lloyd's and insurers expect a named individual or function to stand behind every oversight decision, and that expectation does not change because a tool contributed to the process.

For coverholders, this means an AI-influenced decision should never feel like a dead end. There is always a human function responsible for the outcome, and therefore always a route to challenge it.

How coverholders have traditionally challenged oversight decisions

Long before AI entered delegated authority workflows, coverholders had established ways to question decisions they disagreed with.

A declined bordereaux line might be queried directly with the underwriter. A disputed claims decision might be escalated through a relationship manager. A pattern of repeated queries might prompt a wider conversation about the binder agreement itself.

These mechanisms share common features:

• A named point of contact for raising the query.
• An expectation that the insurer explains its reasoning.
• An opportunity for the coverholder to provide further information.
• A final decision made by someone with the authority to change the original outcome.

These principles remain the foundation for challenging AI-influenced decisions. Nothing about AI changes the basic shape of a fair escalation process.

Where AI changes the practicalities of an appeal

What AI does change is the practical experience of raising and resolving a challenge.

A well-implemented AI tool can record precisely which factors contributed to a flag or referral, whether that was an unusual premium value, a missing field or a pattern inconsistent with prior submissions. This gives the oversight team a documented rationale to share with the coverholder far more quickly than reconstructing the reasoning manually.

AI can also improve consistency. Two similar cases are more likely to be flagged, or not flagged, in the same way, which makes disputes easier to reason about and reduces the sense that outcomes are arbitrary.

This is a genuine operational improvement, but it is a practical enhancement rather than a shift in accountability. The AI tool speeds up the explanation. It does not decide the appeal, and it does not replace the named human reviewer who must ultimately reconsider the case.

What to expect from a well-governed appeal process

Coverholders should be able to expect certain minimum standards from any appeal process involving an AI-influenced decision:

• A clear escalation contact, named in the binder agreement or oversight documentation.
• A response that sets out the factors contributing to the decision, even without technical detail about the underlying model.
• A reasonable timeframe for the initial response and for a final outcome.
• A named human reviewer with real authority to overturn or amend the decision, not simply to relay the AI output.

Managing agents should treat the absence of any of these elements as a governance gap. If a coverholder cannot easily find out how to challenge a decision, or if no one appears able to overturn it, the oversight framework needs attention regardless of how well the underlying AI tool performs.

Example

A coverholder handling a marine cargo binder submits its monthly bordereaux. Several claims lines are automatically flagged as anomalous by the managing agent's validation tool, triggering a delay in payment authorisation.

The coverholder believes the flagged claims are legitimate and wants to understand why they were queried and how to have them reconsidered.

The coverholder raises a query through the agreed escalation contact named in the binder agreement. The oversight team provides the factors that triggered the flags, drawn from the validation tool's output, and a named underwriter reviews the underlying claims documentation directly.

Two of the three flagged claims are cleared for payment once additional context is confirmed, and the process is documented for future audit.

FAQs

• Does a coverholder have the right to know why an AI tool flagged their submission?

  Yes, in principle. While full technical detail about the model may not be shared, the coverholder should be able to request the key factors or rules that contributed to the flag, and this expectation should be set out in the oversight framework or binder agreement.

• Who is responsible if an AI-influenced decision turns out to be wrong?

  The managing agent or insurer remains responsible, in the same way they would be for a manual decision. AI tools support the process but do not carry accountability, so the appeal is directed at the human oversight function, not the technology.

• What should a coverholder do if there is no clear appeal route defined?

  Raise the issue through the standard relationship contact, usually the underwriter or delegated authority oversight contact, and ask for the escalation process to be clarified. This is also a signal that the binder agreement or oversight documentation may need updating to explicitly cover AI-influenced decisions.