Who is accountable for AI-checked data governance?

As insurers and managing agents introduce AI tools to check bordereaux and other delegated data for errors, missing fields and inconsistencies, a governance question naturally follows.

If AI identifies, or misses, a data quality issue, who is accountable for the outcome?

This matters because delegated authority oversight frameworks depend on clear ownership of decisions. Introducing a new tool into the process can create uncertainty about where responsibility sits if that question is not addressed explicitly from the outset.

The answer is straightforward in principle, even if it requires deliberate documentation in practice.

Why accountability questions arise when AI checks DA data

Bordereaux oversight has always depended on someone being answerable for the quality of the data that flows into underwriting, claims and regulatory reporting.

When that checking work was performed entirely by people, accountability was rarely questioned. A named analyst reviewed the data, applied judgement and signed off the result.

Introducing AI into that process changes who, or what, performs the initial check. It does not change the underlying question that governance frameworks exist to answer: who is responsible if something goes wrong?

Oversight teams are right to ask this question early, before AI tools become embedded in day-to-day validation. Once a tool is relied upon routinely, it becomes harder to retrofit clear accountability if the framework was never defined.

How accountability works in traditional (manual) validation

In a manual validation process, accountability is usually structured through named roles.

A DA oversight analyst, operations manager or delegated authority specialist typically reviews bordereaux data against agreed rules, applies judgement to exceptions and signs off the result before it proceeds to underwriting systems, reserving or reporting.

This structure usually includes:

• A defined reviewer or approver for each stage of the check.
• Documented criteria for what constitutes an error, an exception or an acceptable variance.
• An escalation path for cases that fall outside standard rules.
• A record of who reviewed the data and what decision was made.

Critically, the person performing the check is accountable for the judgement applied, not simply for the mechanical act of reviewing rows in a spreadsheet. This distinction matters when AI is introduced, because it clarifies exactly what AI is, and is not, taking over.

Where AI changes the workload, not the accountability

AI-based checking tools can review far more data, far more consistently, than a manual process typically allows. They can flag missing fields, inconsistent currency codes, unexpected premium values and other anomalies at a scale that would take a human team considerably longer to work through manually.

What AI does not do is decide what should happen next.

When AI flags a row as a potential error, a human role within the oversight function still needs to determine whether that flag represents a genuine issue, a known exception or a misunderstanding by the tool. That decision, and the sign-off that follows it, remains with a named person, exactly as it did before AI was introduced.

This is the practical meaning of protecting the core while augmenting the expertise. AI absorbs the repetitive interpretation work of scanning large volumes of data for inconsistencies. The judgement about what those inconsistencies mean, and what to do about them, stays with experienced professionals who understand the business context.

Organisations that treat AI output as a recommendation requiring review, rather than a decision requiring no further action, preserve this distinction in practice as well as in principle.

Practical governance considerations when introducing AI validation

Introducing AI into bordereaux or data validation does not require a new governance philosophy. It does require the existing framework to be extended explicitly to cover the new tool.

Oversight teams should consider:

• Documenting precisely which checks are performed by AI and which decisions require human sign-off.
• Maintaining an audit trail that records what the AI flagged, what a human reviewer decided, and the reasoning behind that decision.
• Defining clear escalation paths for cases where AI output is uncertain, contested or overridden by a reviewer.
• Reviewing and periodically testing AI validation performance as an ongoing governance activity, not a one-off step completed during onboarding.

This documentation matters beyond internal comfort. If a regulator, auditor or senior manager asks how a data quality issue was handled, the organisation needs to show not just that AI flagged it, but who reviewed that flag and what they decided.

Accountability, in other words, is demonstrated through records as much as through role definitions. Both need to be in place before AI-assisted validation becomes routine.

Example

A Lloyd's managing agent introduces an AI tool to check monthly bordereaux submitted by an overseas MGA writing agricultural risk.

The AI flags several rows with missing premium fields and inconsistent currency codes.

The DA oversight analyst reviews the flagged rows, confirms two are genuine errors requiring correction by the MGA, and determines the third is a valid exception due to a known local reporting convention.

The analyst documents the decision and signs off the bordereau for processing.

The AI tool accelerates identification of data quality issues, but the decision on which flagged items represent genuine errors, and the sign-off to proceed, remains with the named oversight analyst. The governance record shows both what the AI flagged and what the human reviewer decided, preserving a clear accountability trail.

FAQs

• Does using AI to check bordereaux data reduce our regulatory responsibility?

  No. Regulatory and contractual responsibility for data governance remains with the insurer or managing agent regardless of the tools used to perform checks. AI supports the process but does not assume responsibility.

• Who should sign off on data that AI has validated?

  A named individual or role within the oversight function, as defined by the organisation's existing governance framework. AI output should inform that decision, not replace the sign-off step.

• What happens if AI misses a data quality issue?

  The oversight function remains accountable for the outcome. This is why ongoing performance review, spot-checking and clear escalation procedures are necessary parts of any AI-assisted validation process, not optional extras.