What governance structures should oversee AI use in delegated authority?

Delegated authority oversight teams have spent years building governance around bordereaux quality, coverholder audits and binder compliance.

When AI is introduced to help process, validate or transform that same data, it does not arrive in a governance vacuum. It arrives inside a framework that was built before AI was part of the process.

The question is not whether AI needs governance. It is which existing structures should be extended to cover it, and what new controls need to be added.

Get this wrong and gaps appear quickly: no one owns the accuracy of AI-assisted outputs, no escalation path exists when AI flags something unusual, and audit trails fail to show how a decision was actually reached.

Why AI use creates a governance gap if left unaddressed

Most DA organisations already have oversight mechanisms for bordereaux data: audit committees, coverholder review cycles, binder compliance monitoring and sign-off processes for reporting.

These structures were designed around manual or semi-automated review, where a person applies judgement to a spreadsheet and a colleague checks their work.

When AI takes on part of that interpretation, for example flagging inconsistent premium values or reconciling policy references across formats, the existing structures often do not explicitly say who is responsible for reviewing what the AI has done.

This matters for two reasons. First, regulators and delegated authority oversight frameworks expect clear accountability for delegated functions, regardless of what tools are used to support them. Second, operational risk increases if AI-flagged issues sit unresolved because no one has been assigned to act on them.

The gap is rarely caused by the AI performing poorly. It is caused by governance structures not yet accounting for a new step in the process.

How DA organisations traditionally govern data quality and oversight

Traditional DA governance typically includes several layers.

Binder audits assess whether a coverholder is operating within the terms of their delegated authority, including the quality and completeness of bordereaux submissions.

Coverholder and TPA oversight committees review performance across multiple coverholders or third-party administrators, often on a quarterly basis, looking at trends in data quality, complaints or claims handling.

Bordereaux sign-off processes require a named individual, often within operations or underwriting support, to confirm that a bordereau has been validated before it feeds into bordereaux management systems, reporting or reinsurance recoveries.

These mechanisms share a common feature: they assign responsibility to specific people or committees, and they generate a record that review took place.

That combination, defined accountability and an auditable record, is exactly what needs to be preserved and extended when AI is introduced.

Where AI requires extended governance

Introducing AI into bordereaux processing or validation typically requires four additions to existing governance.

Ownership of AI-assisted outputs. Someone needs to be named as responsible for the accuracy and performance of the AI's output, in the same way a data owner is responsible for a manually validated bordereau.

Model monitoring. Oversight committees should periodically review how well the AI is performing, for example the rate of false positives in exception flagging or drift in data patterns from a coverholder, rather than assuming performance remains constant.

Exception escalation. When AI flags a bordereaux issue, there should be a defined path for who reviews it, what timeframe applies and what happens if the exception is not resolved.

Audit trail requirements. Records should capture what the AI identified, what action was taken, and who signed off, so that the reasoning behind a decision can be reconstructed later, whether for an internal audit, a regulator or a coverholder dispute.

None of this requires replacing existing governance. It requires extending the terms of reference of existing committees and roles to explicitly include AI-assisted activity.

Practical steps for extending governance ahead of go-live

Organisations introducing AI into DA data processing should treat governance extension as part of implementation, not an afterthought.

Before go-live, this typically means:

• Updating the terms of reference for the existing DA oversight committee or audit function to include AI-assisted processing.
• Naming an individual or role responsible for signing off on AI-flagged exceptions.
• Defining escalation timeframes and routes before the AI tool goes live, rather than creating them reactively once issues arise.
• Agreeing what the audit trail must capture, and confirming the AI tool can actually produce that record.

Accountability for underwriting and oversight decisions remains with named individuals throughout. AI can reduce the manual effort involved in spotting inconsistencies or reconciling formats, but it does not change who is responsible for the decisions that follow.

Example

A managing agent introduces an AI tool to help validate monthly bordereaux submitted by several coverholders.

The existing DA oversight committee, which already reviews binder compliance and audit findings, extends its terms of reference to include review of AI-flagged exceptions each quarter. A named data owner is appointed to sign off on AI-assisted validation results before they feed into reporting.

As a result, the managing agent maintains a clear audit trail showing which exceptions the AI flagged, how they were reviewed, and who approved the final validated bordereaux. Governance of AI use sits within the same structure that already oversees coverholder compliance, rather than existing as a separate, unowned process.

FAQs

• Do we need a separate committee to govern AI use in DA?

  Usually not. Extending the remit of an existing DA oversight committee or audit function is generally more effective than creating a new structure, since it keeps AI oversight connected to established accountability. Very large organisations with extensive AI use across multiple functions may choose to set up a dedicated AI governance function, but this is not a requirement for most DA operations.

• Who should be accountable when AI flags an error in bordereaux data?

  A named individual should own the review and sign-off of flagged exceptions, consistent with how manual bordereaux review already works. The AI system identifies and flags issues, but accountability for deciding what happens next sits with a person, not the tool.

• What should an audit trail for AI-assisted DA processing include?

  At minimum, it should record what the AI identified, what action was taken as a result, who reviewed the flagged item, and when sign-off occurred. This preserves the same standard of accountability expected of manual review processes.