How Do We Audit AI Data Quality Assessments?

Delegated authority oversight teams are increasingly relying on AI tools to assess the data quality of incoming bordereaux before that data reaches underwriting, claims or regulatory reporting.

That raises a question oversight teams have not previously had to answer: how do you audit an assessment that a machine produced, rather than a colleague?

When a human analyst reviews a bordereau, their reasoning can be questioned directly. When an AI tool flags, scores or passes thousands of records automatically, oversight teams need a different way to satisfy themselves that the assessment is sound.

This article explains how to build that audit approach: what evidence to expect, how to sample effectively and how to reconcile AI output against manual review without recreating the very manual effort the AI was meant to reduce.

Why AI data quality assessments need their own audit approach

When an AI tool assesses bordereaux data quality, it is not simply running a fixed set of validation rules. It is interpreting content, applying judgement about what counts as an anomaly, and producing a verdict, pass, flag or score, for each record.

That verdict is only useful to an oversight team if it can be trusted. But trust cannot rest on the fact that the AI produced an answer quickly. It has to rest on evidence that the answer was reasonable.

This creates a distinct audit question. Auditing raw bordereaux data asks: is this record correct? Auditing an AI data quality assessment asks a different question: was the assessment of this record reasonable, consistent and traceable? The object of the audit has shifted from the data itself to the process that judged the data.

Oversight teams that skip this step risk a specific failure mode: relying on AI output as though it were as scrutinised as manual review, when in fact nobody has checked whether the AI's judgement is dependable for their particular bordereaux, coverholders or lines of business.

Traditional approaches to auditing data quality checks

Before AI tools were involved, data quality audits were built around direct human review. Analysts checked bordereaux line by line against source documents, cross-referenced fields against policy or claims systems, and applied spot checks to a sample of records each reporting period.

This approach has clear strengths. Every check performed is fully explainable, because a person made the decision and can describe their reasoning. Audit trails are, in effect, the analyst's own working notes.

However, the same approach becomes impractical as volume grows. Reviewing every field across hundreds of bordereaux from dozens of coverholders each month is not something most operations teams can sustain. In practice, manual audits have long relied on sampling, focusing scrutiny on a subset of records and inferring overall data quality from that subset.

The limitation of the traditional approach is not thoroughness in principle. It is that thoroughness at scale requires resourcing that most operations teams do not have, which is precisely the gap AI is being used to close.

Where AI changes what's possible for audit

A properly implemented AI data quality tool does not just produce a pass or fail. It can generate structured evidence for each decision: which field or record was assessed, what issue was identified or ruled out, a confidence level attached to that judgement, and the rationale behind it.

This is a meaningful change in what audit can achieve. Instead of an oversight team having to reconstruct why a record was accepted or rejected, that reasoning already exists as an output of the assessment process itself. Reviewing a sample of AI decisions becomes a matter of checking the stated rationale against the source bordereau, rather than performing the original analysis again from scratch.

This evidence also makes patterns visible that manual sampling would struggle to detect. If an AI tool is systematically passing records from a particular coverholder that later prove problematic, a review of confidence scores and rationale across that coverholder's submissions can surface the issue faster than isolated spot checks would.

None of this removes the need for review. The evidence produced by AI still has to be examined by someone with the expertise to judge whether the stated rationale actually holds up. What AI changes is the volume of assessment that can be meaningfully reviewed, not the requirement for that review to happen.

Building a practical audit process

A workable audit process for AI-assisted data quality assessment rests on three components.

First, the audit trail itself needs to be detailed enough to support review. At minimum, it should record what was flagged or confirmed clear, the confidence level attached to that judgement, the rationale or supporting evidence behind it, and what action followed, whether that was an automatic pass, an escalation, or a hold for manual review.

Second, sampling should be risk-based rather than random or exhaustive. Attention is best directed at high-value records, complex or unusual bordereaux structures, new or previously problematic coverholders, and any records where the AI's confidence score was low. This concentrates scrutiny where it is most likely to matter, rather than spreading it evenly across records that carry little risk.

Third, periodic reconciliation against independent manual review provides a check on the AI's overall reliability, not just individual decisions. Selecting a modest percentage of records each quarter for full manual assessment, and comparing the outcome against what the AI concluded, helps oversight teams detect drift: cases where the AI's judgement has started to diverge from what a human reviewer would decide, perhaps because a coverholder's reporting format has changed in ways the AI has not adapted to.

Together, these three components let oversight teams maintain confidence in AI-assessed data quality without reverting to full manual re-checking. The workload changes considerably. The accountability for the result does not.

Example

A managing agent uses an AI tool to assess the data quality of monthly bordereaux submitted by several coverholders writing marine cargo business. The oversight team wants to be confident the AI's assessments are reliable before relying on them to reduce manual review effort.

Each month, a delegated authority oversight manager and a data quality analyst review the AI's audit trail for a sample of flagged and passed records, checking the stated rationale against the source bordereaux. Attention is weighted towards new coverholders and records the AI flagged with lower confidence.

Each quarter, they also reconcile a small percentage of records through independent manual review, comparing the outcome against the AI's original assessment.

This combination gives the team confidence to rely on the AI assessment for the bulk of submissions, while directing their attention to the specific coverholders and record types where discrepancies are found.

FAQs

• What should an AI data quality audit trail actually contain?

  It should record the specific issue flagged, or confirmed absent, for each record, the confidence level attached to that judgement, the rationale or supporting evidence behind it, and the action that followed. This lets a human reviewer assess the decision without having to repeat the underlying analysis themselves.

• How much of the AI's work should we manually re-check?

  Re-checking everything defeats the purpose of using AI in the first place. A risk-based sampling approach, focused on high-value or high-complexity records and low-confidence flags, combined with periodic broader reconciliation against manual review, is a more practical way to maintain confidence while detecting drift over time.

• Who is accountable if an AI data quality assessment misses an issue?

  Accountability remains with the organisation and its oversight processes, not with the AI tool itself. The audit process described here exists precisely to catch and correct such gaps. AI changes how the assessment work is performed, but it does not remove the need for human sign-off and governance.