What controls ensure AI does not bypass human judgement?

As AI tools are adopted to process bordereaux and support oversight activity, DA professionals face a legitimate governance question: how do we ensure AI does not quietly make decisions that should remain with underwriters, compliance teams or oversight committees?

Regulators and internal audit functions expect clear evidence that human judgement remains the final authority, particularly for decisions affecting coverage, claims handling and regulatory reporting.

This article sets out the practical controls that keep humans in the decision loop, and what auditors and regulators actually expect to see as evidence.

Why this question matters now

AI adoption in bordereaux processing is accelerating, and with it comes closer scrutiny from boards, regulators and internal audit functions.

The concern is not that AI performs tasks previously done manually. It is that decisions affecting coverage, claims or compliance could be made without a named individual having genuinely applied judgement.

Oversight committees increasingly ask a specific question when reviewing AI-assisted processes: where exactly does a human make the final call, and can we prove it?

This is not a hypothetical concern. Auditors reviewing delegated authority arrangements routinely ask for evidence of control points, not just a description of the technology in use.

How organisations traditionally maintain human control

Long before AI entered bordereaux processing, DA organisations built control disciplines to prevent unsupervised decision-making.

Common mechanisms include:

• Maker-checker processes, where one person prepares a transaction and another approves it.
• Delegated sign-off limits, where individuals can only approve decisions up to a defined value or risk threshold.
• Manual exception handling, where anomalies are pulled aside for senior review rather than processed automatically.
• Periodic sampling and audit, where a proportion of processed bordereaux are reviewed retrospectively.

These mechanisms exist because delegated authority itself is a governance arrangement: an insurer grants underwriting authority to a coverholder or MGA, and must be able to demonstrate that authority is being exercised within agreed limits.

The introduction of AI does not remove this requirement. It changes how the control points are implemented.

Where AI changes what is operationally possible

AI allows these same control principles to operate more consistently and at greater scale, provided they are deliberately designed into the process.

Key mechanisms include:

• Confidence thresholds. AI assigns a confidence score to its own output. High-confidence matches with low financial or regulatory impact can proceed with light-touch review, while low-confidence or high-value cases are automatically routed to a human.
• Exception queues. Rather than silently correcting anomalies, AI surfaces them in a structured queue for a named reviewer, together with the reasoning behind the flag.
• Escalation paths. Where an exception exceeds a reviewer's authority, it is escalated to an underwriter or oversight committee with defined sign-off responsibility.
• Audit trails. Every AI recommendation, the identity of the reviewer, the decision made and the rationale are logged in a form that can be retrieved during an audit.

The effect is that oversight becomes more consistent. AI does not get tired, skip steps under time pressure, or apply exception criteria inconsistently between reviewers. Human judgement is still exercised at the same points it always was, but with better visibility into where it needs to be applied.

AI's role here is to remove repetitive interpretation work, not to make the underlying decision. A confidence score is a prompt for human attention, not a substitute for it.

Practical considerations for implementing these controls

Designing these controls well requires a few deliberate steps.

• Define thresholds explicitly. Agree, in writing, what confidence level or risk category triggers automatic escalation, and review these periodically as coverholder relationships and data patterns evolve.
• Assign named reviewers. Every exception queue and sign-off gate needs an accountable individual, not a generic team inbox.
• Document escalation criteria. Reviewers should know exactly when a decision needs to go further up the chain, rather than relying on informal judgement.
• Test the controls, not just the AI. Periodically check whether exceptions are actually being reviewed, and whether reviewers are engaging critically with AI output rather than approving it by default.

This last point matters more than it might appear. Over-reliance on AI output without genuine review is itself a governance failure, even if a human technically clicked "approve". Evidencing genuine review, not just the existence of a review step, is what auditors and regulators are ultimately looking for.

Example

A Lloyd's managing agent uses an AI tool to process monthly bordereaux from an overseas MGA writing agricultural risk. The tool flags a cluster of premium entries where the calculated exposure falls outside the agreed binder terms.

Rather than auto-correcting the figures, the system routes the entries to an exception queue for the DA oversight analyst, who reviews the underlying policy documents and confirms a genuine discrepancy. The analyst escalates it to the underwriter with binder authority, who decides whether to accept, query or reject the bordereau.

The AI tool surfaces the anomaly quickly and consistently, but the decision on how to handle the discrepancy remains with the underwriter. The exception, review and decision are all logged, giving the managing agent a clear audit trail showing human judgement was applied before any action was taken.

FAQs

• Can AI ever approve a bordereau without human review?

  Best practice keeps final approval with a named human, even where AI handles routine validation. Allowing AI to fully automate approval for anything with regulatory or financial consequence removes the accountability that delegated authority arrangements depend on, and most organisations reserve unsupervised automation for genuinely low-risk, low-value matches only.

• What evidence do regulators or auditors expect to see?

  Expect requests for logs of AI recommendations, the identity of the reviewer, timestamps, and the rationale for any decision, particularly where a human departed from or confirmed the AI's suggestion. A policy statement describing controls is not sufficient on its own; auditors want to see the controls operating in practice.

• How do we decide what should trigger human review versus what AI can handle automatically?

  Most organisations use risk-based thresholds. Low-risk, high-confidence matches can flow through with light-touch review, while anomalies, low-confidence matches or high-value transactions are routed to full human review. These thresholds should be documented and revisited periodically as data patterns and coverholder relationships change.