What skills do teams need to interpret AI validation results?

AI-assisted validation tools have changed how quickly delegated authority teams can spot problems in bordereaux data. Anomalies that once took days of manual review can now be flagged within minutes, across every submission, every month.

But a flag is not a finding. It is a starting point.

Someone still has to decide whether a flagged premium figure reflects a genuine data error or a known seasonal pattern, whether a low confidence score means the tool is uncertain or the underlying data is simply unusual, and whether an issue needs escalating to a coverholder or can be resolved internally.

That interpretation work depends on skills that AI does not provide. As more DA teams adopt validation tools, the organisations that benefit most are those that deliberately build this capability, rather than assuming the tool does the thinking for them.

Why AI validation output still needs human interpretation

AI validation tools are good at detecting statistical anomalies, inconsistencies and patterns that deviate from expectation. They can compare a submission against historical data, flag unusual values and assign a confidence score to each finding.

What they cannot do is know, with certainty, why a figure looks unusual for this coverholder, this class of business or this reporting period.

A cluster of premium values that looks anomalous in isolation might be entirely normal for agricultural risk during a seasonal renewal period. A missing field might be a genuine data quality problem, or it might reflect a legitimate variation in how a particular coverholder structures its bordereaux.

The AI tool surfaces the pattern. It does not know the business context that determines whether the pattern matters.

That is where interpretation comes in. Someone with the right knowledge needs to look at each flag and decide what it actually means for this specific case, and what, if anything, should happen next.

How teams traditionally built validation judgement

Before AI validation tools existed, experienced DA professionals built this judgement through years of manual bordereaux review.

Reviewing hundreds of submissions over time teaches an analyst what normal variation looks like for a given class of business, which coverholders tend to submit clean data and which require closer attention, and which types of error are common versus rare.

This experience is not written down in a manual. It is built gradually, through repeated exposure to real submissions and the outcomes that follow from different decisions.

That accumulated judgement remains valuable. AI validation tools change how issues are detected, but they do not replace the underwriting and operational knowledge that experienced professionals bring to deciding what a flagged issue actually means.

The challenge for DA teams now is ensuring that this judgement is applied consistently to a much larger volume of flagged output than manual review ever produced.

The specific skills AI-assisted validation now demands

Interpreting AI validation output well requires three distinct skills.

Domain knowledge. The ability to assess whether a flagged issue is material, given knowledge of the class of business, the coverholder's typical patterns and the wider market context. This is the same underwriting and operational knowledge that supported manual review, applied to a new type of output.

Statistical literacy. The ability to read a confidence score and understand what it actually represents. A low confidence score does not automatically mean an error exists, and a high confidence score does not guarantee one does not. Teams need enough literacy to understand what the score is measuring, and where false positives are more likely to occur, without needing a technical or data science background to do so.

Escalation judgement. The ability to decide, once a flag has been reviewed, what should happen next: resolve internally, query the coverholder, escalate to oversight, or dismiss as expected variation. This judgement determines whether AI validation genuinely saves time or simply generates additional unnecessary work.

These skills build directly on traditional manual review experience. They do not replace it. What has changed is the volume and speed at which this judgement now needs to be applied.

Building this capability within a DA team

Organisations that build this capability deliberately tend to follow a similar pattern.

They pair experienced reviewers with less experienced team members so that judgement is transferred through shadowing, not just instruction. They establish a shared vocabulary for discussing confidence levels and exception types, so that different analysts interpret the same flag consistently. And they create a feedback loop between validation outcomes and the tool itself, so that patterns of false positives or missed issues inform how the tool is tuned over time, rather than simply being tolerated.

Clear escalation protocols also matter. Teams that agree in advance what counts as a genuine exception, and who reviews it, avoid the inconsistency that arises when individual analysts make ad hoc judgement calls under time pressure.

This is not a one-off training exercise. It is an ongoing capability that develops as the team gains more exposure to the tool's output and its own outcomes.

Example

A Lloyd's managing agent's DA oversight team introduces an AI validation tool to review monthly bordereaux from a coverholder writing agricultural risk. In the first month, the tool flags a cluster of premium figures as statistically unusual.

Rather than automatically rejecting the bordereaux or accepting the flag at face value, an experienced oversight analyst reviews the flagged entries against known seasonal patterns in agricultural risk pricing and recognises the pattern as expected rather than erroneous.

The analyst's domain knowledge prevents an unnecessary query back to the coverholder, saving time for both parties, while a genuine formatting inconsistency elsewhere in the same bordereaux is correctly escalated for correction. The tool accelerates detection. The analyst's interpretation skill determines the right response.

FAQs

• Do team members need a technical or data science background to interpret AI validation results?

  No. Domain knowledge of underwriting and bordereaux structure matters more than technical AI expertise. Teams need enough statistical literacy to understand what confidence scores represent, but not the ability to build or tune the underlying models.

• How long does it take to build this interpretation capability in a team?

  It develops gradually through structured exposure, shadowing and feedback loops alongside AI tool use, rather than through a single training session. Timelines vary depending on the team's existing manual review experience.

• What happens if a team interprets AI validation results incorrectly?

  Over-trusting flags leads to unnecessary escalations and wasted coverholder queries, while under-trusting them risks missing genuine data quality issues. Ongoing calibration and clear governance help mitigate both risks.