How does AI support human oversight in DA decisions?

Delegated authority oversight teams are responsible for monitoring coverholder and MGA performance across large volumes of bordereaux, correspondence and audit findings, often with limited resource.

As AI tools are introduced to help process this volume, oversight teams and their governance committees need clarity on what AI can safely be trusted to do, and where human judgement must remain firmly in control.

This article explains where AI genuinely helps with DA oversight, and why the decisions themselves must stay with experienced professionals.

The oversight burden facing DA teams today

Managing agents and insurers are required to demonstrate active oversight of every coverholder and MGA operating under a binder agreement.

In practice, that means reviewing monthly bordereaux, tracking performance against agreed terms, preparing for periodic audits and following up on queries or discrepancies.

As the number of coverholders grows, so does the volume of data that needs reviewing. Oversight teams are frequently expected to cover more ground with the same, or fewer, resources.

The result is that full, line-by-line review of every bordereau is rarely realistic. Teams instead rely on sampling and experience to focus their attention where it matters most.

How oversight has traditionally been carried out

Traditional DA oversight relies heavily on structured, periodic activity: sample-based bordereaux checks, scheduled coverholder audits and manual reconciliation against binder terms.

These approaches are well established and defensible. Sampling allows a team to form a reasonable view of a coverholder's performance without reviewing every single transaction, and periodic audits provide a formal checkpoint for deeper investigation.

However, sampling has a structural limitation. Issues that occur outside the sampled records can go unnoticed until the next audit cycle, or until they surface as a larger problem. Manual review is also time-consuming, which limits how frequently it can realistically be repeated.

These approaches remain sound in principle. The constraint is one of scale and frequency, not of method.

Where AI genuinely helps

AI changes what is operationally possible when reviewing bordereaux and related oversight data.

Rather than reviewing a sample, AI can scan complete data sets and flag anomalies such as:

• Premium volumes that deviate sharply from historical patterns.
• Risks written outside agreed binder terms, such as unauthorised classes or limits.
• Inconsistent or missing data that may indicate reporting quality issues.
• Patterns across submissions that suggest a coverholder's practices have changed.

AI can also help prepare material ahead of a coverholder audit, drawing together relevant bordereaux history, prior findings and outstanding queries into a single view for the reviewing team.

In each case, the AI output is an input to human review. It tells the oversight analyst where to look and why, rather than concluding whether an issue is significant or what should happen next.

Keeping human judgement and accountability central

Introducing AI into oversight workflows raises a governance question that must be answered clearly: who is accountable for the decision that follows an AI-generated flag?

The answer should always be the same. The oversight analyst, and the organisation's oversight function more broadly, remains accountable for investigating flagged issues, deciding whether escalation is warranted and documenting the reasoning behind that decision.

To keep this boundary clear in practice, organisations should:

• Treat AI outputs as prompts for investigation, not as findings in their own right.
• Maintain an audit trail showing who reviewed each flagged item, what they concluded and what action was taken.
• Define, in governance documentation, which oversight tasks may be AI-assisted and which must remain fully manual.

AI changes the volume of data that can be reviewed. It does not change who is responsible for the review outcome.

Example

A Lloyd's managing agent's oversight team uses an AI tool to review monthly bordereaux from a portfolio of coverholders. The tool flags a coverholder whose declared premium volumes have deviated sharply from historical patterns and highlights several risks written outside the agreed binder terms.

The oversight analyst investigates the flagged items, requests clarification from the coverholder, and ultimately escalates one issue to the audit committee for further review.

The AI tool reduces the time needed to identify potential issues across the full bordereaux data set, rather than a sample. The analyst's investigation, judgement and escalation decision, not the AI output itself, form the basis of the recorded oversight action.

FAQs

• Can AI make oversight decisions on behalf of a managing agent?

  No. AI can surface information, such as anomalies or inconsistencies, that supports an oversight decision, but it cannot make or be accountable for that decision. Interpreting flagged issues and deciding what action to take remains a human and organisational responsibility.

• What DA oversight tasks are most suitable for AI support today?

  AI is best suited to tasks involving large-volume pattern detection, such as reviewing full bordereaux data sets rather than a sample, flagging figures that deviate from historical patterns, and preparing material ahead of coverholder audits.

• Does using AI in oversight change regulatory or Lloyd's expectations?

  No. Using AI does not remove existing oversight obligations. Organisations must still be able to demonstrate who made each oversight decision and on what basis, with AI treated as a tool that supports that process rather than replacing it.