What role does exception reporting play in validation?

Delegated authority data arrives from many coverholders and MGAs, each with different formats, terminology and levels of data quality.

Running validation checks against this data produces large volumes of flagged issues, but not all flagged issues carry the same operational risk.

Oversight teams need a reliable way to separate genuine, actionable exceptions from routine noise so they can direct their limited time and judgement to what matters.

That is the role exception reporting plays within data validation.

Why validation alone isn't enough

Validation checks data against rules, formats and expected values. When a bordereau is run through validation, it is common for hundreds of rows to be flagged for one reason or another.

Some of these flags represent minor formatting inconsistencies. A date entered in the wrong format, a missing but non-critical field, or a currency code written slightly differently to expected.

Others represent something far more significant. A premium calculated outside the agreed rating basis, a policy reference that does not exist, or a claims reserve that has moved well beyond expected tolerances.

If every validation failure is presented to an oversight team with equal weight, two things tend to happen. Reviewers either spend their time working through low-risk noise, or genuine risk gets buried in the volume and is missed entirely.

Exception reporting exists to solve this specific problem. It takes the raw output of validation and organises it into something a human can act on efficiently: a prioritised view of what actually needs attention.

How exception reporting has traditionally worked

Most delegated authority operations build exception reporting around manually defined rules and thresholds.

A typical approach might flag any premium variance above a set percentage, any missing mandatory field, or any date that falls outside an expected policy period. These thresholds are usually set by experienced oversight staff, based on what has historically mattered.

Once flagged, exceptions are often categorised manually, or through fixed rule sets, into groups such as "critical", "review required" or "informational". The results are typically compiled into a spreadsheet-based exception log, which is then worked through by the oversight team during the reporting cycle.

This approach works, but it has practical limits. Thresholds and categorisation rules need continual maintenance as products, coverholders and risk appetites change. Because coverholder formats vary so widely, the same underlying issue can appear differently across submissions, meaning rules tuned for one MGA's bordereau may not catch an equivalent issue in another's. At scale, across dozens of coverholders and monthly cycles, maintaining and re-tuning these rules becomes a significant ongoing task in its own right.

Where AI helps with exception reporting

AI can support exception reporting by reading bordereaux in varied formats and identifying the likely cause and severity of a flagged issue, rather than relying solely on a fixed rule matching a specific column name or threshold.

This means exceptions arising from genuinely different underlying causes, such as a data entry error versus a calculation that falls outside the rating basis, can be distinguished and grouped more consistently, even when the source spreadsheets look nothing alike.

AI can also help surface patterns across submissions. For example, recognising that a particular coverholder is producing a recurring type of exception month after month, which may point to a process issue worth addressing at source rather than repeatedly flagging the same symptom.

This reduces the repetitive interpretation work involved in triaging large volumes of flagged rows. It does not remove the need for oversight judgement. Confirming whether a flagged exception represents genuine risk, deciding how to resolve it with the coverholder, and signing off the bordereau remain the responsibility of experienced oversight professionals.

What to consider when relying on exception reporting

Exception reporting is only as useful as the categorisation and prioritisation logic behind it, whether that logic is manually built or AI-assisted.

Poorly tuned exception reporting creates one of two problems. Thresholds set too tightly flood reviewers with low-risk noise, while thresholds set too loosely can hide genuine risk within the routine.

Organisations relying on AI-assisted triage should treat its output as a prioritised starting point, not a final decision. Exceptions still need to be confirmed and actioned by oversight staff, and there should be a clear audit trail showing how each exception was resolved and by whom.

Regularly reviewing how exceptions are categorised, and adjusting thresholds or logic as coverholder behaviour and risk appetite change, keeps exception reporting genuinely useful rather than becoming another source of noise.

Example

A Lloyd's managing agent receives a monthly bordereau from an overseas MGA covering a marine cargo binder. Automated validation flags several hundred rows for issues ranging from minor formatting inconsistencies to a handful of premium calculations that fall outside the agreed rating basis.

An exception report groups these flagged rows by severity and likely cause, surfacing the premium calculation issues at the top for the oversight team to review first.

The oversight analyst resolves the premium calculation exceptions with the MGA within the reporting cycle, while the minor formatting issues are logged for a routine follow-up rather than delaying sign-off of the bordereau.

FAQs

• Is exception reporting the same as data validation?

  No. Validation identifies issues by checking data against rules, formats and expected values. Exception reporting takes those validation results and organises them into a prioritised list for human review. Validation produces the raw findings; exception reporting turns them into something an oversight team can act on efficiently.

• What makes an issue an "exception" rather than a routine error?

  Exceptions are typically issues that fall outside expected tolerances or carry material risk, such as a premium calculated outside the agreed rating basis or a missing policy reference. Minor, low-risk formatting discrepancies are often handled automatically or logged separately, without requiring the same level of review.

• Can exception reporting be fully automated?

  Categorisation and prioritisation of exceptions can be automated or AI-assisted, which reduces repetitive triage work. However, confirming genuine risk, deciding how to resolve an issue, and signing off the bordereau should remain with experienced oversight professionals.