How do we handle partial or missing bordereaux data?

Anyone who has reviewed a coverholder or MGA bordereaux knows that a perfectly complete submission is the exception rather than the rule.

Sums insured go unrecorded. Inception dates are formatted inconsistently. Claims fields are left blank pending further information.

The operational question is rarely whether a bordereaux is completely clean. It is what to do when it isn't: which gaps matter, which can wait, and which need an immediate query back to the source.

Getting this right, consistently and at volume, is one of the more persistent challenges in delegated authority oversight.

Why Bordereaux Data Is Often Partial or Missing

Partial or missing data is rarely the result of carelessness. It is usually a byproduct of how bordereaux are produced.

Coverholders and MGAs operate their own policy administration and claims systems, often built around local market practice rather than the reporting requirements of the insurer or managing agent receiving the data.

Common causes include:

• Fields that are genuinely unknown at the point of reporting, such as a claims reserve before assessment is complete.
• System exports that do not capture every field the receiving organisation expects.
• Manual entry errors or omissions during preparation of the spreadsheet.
• Inconsistent definitions of what a field should contain, leading to blank or malformed entries.
• Deadline pressure, where a submission is sent on time but incomplete rather than late and complete.

None of these causes are unusual. They are a routine feature of receiving data from many independent sources, each operating slightly differently.

Traditional Approaches to Handling Incomplete Data

Historically, DA operations teams have relied on manual review to catch gaps.

This typically involves:

• Checklists of required fields, reviewed row by row or spreadsheet by spreadsheet.
• Spreadsheet-based tracking logs recording which submissions have outstanding queries.
• Direct email queries to coverholders or MGAs, often on an ad hoc basis.
• Periodic escalation to underwriters or oversight committees when issues persist.

These methods work reasonably well at low volume, where a small number of experienced staff can apply consistent judgement to each submission.

The difficulty is scale. As the number of coverholders, binders and monthly submissions grows, manual review becomes slower, more inconsistent between reviewers, and harder to audit. Two analysts may treat the same missing field differently, and there is often no systematic record of why a particular gap was accepted or escalated.

Where AI Helps Identify and Classify Gaps

AI-supported validation changes what is operationally possible when reviewing large volumes of bordereaux for completeness.

Rather than relying on a person to check each row against a checklist, AI can automatically scan submissions to identify which fields are missing, which are present but inconsistent, and which fall outside expected ranges or formats.

It can also classify gaps by likely materiality, distinguishing, for example, between a missing sum insured on a live policy and a minor date formatting inconsistency that carries little practical risk.

Across multiple submissions, AI can surface patterns, such as a specific coverholder consistently omitting the same field, that might otherwise take months of manual observation to notice.

This does not remove the need for human judgement. It removes the repetitive burden of manually checking every field in every row, so that DA professionals can focus their attention on the gaps that genuinely warrant a decision.

Operational Considerations When Handling Partial Data

Regardless of the tooling used, certain safeguards remain necessary.

Materiality thresholds should be agreed in advance and documented, rather than decided inconsistently case by case. This ensures that similar gaps are treated similarly, whoever is reviewing them.

Escalation paths need to be clear: who is queried, how urgently, and what happens if a query goes unanswered.

Persistent gaps from the same coverholder or MGA are worth monitoring over time. A recurring issue often points to a training gap, a system limitation, or a misunderstanding of reporting requirements, rather than a one-off error.

Finally, any automated detection or classification must route to a human for a decision on acceptability and next steps. AI can identify and prioritise gaps efficiently, but the judgement about whether a submission is fit for purpose remains with experienced DA professionals.

Example

A managing agent receives a monthly bordereaux from an overseas MGA covering an agricultural risk binder. Several rows are missing sums insured, and a handful of policy inception dates are inconsistently formatted.

Rather than rejecting the entire submission, the DA operations team uses an AI-supported check to flag exactly which rows and fields are incomplete, classify the missing sums insured as material and the date formatting as low-risk, and prepare a targeted query for the MGA covering only the material gaps.

The managing agent processes the bulk of the bordereaux without delay, sends a focused query on the material gaps only, and maintains a documented record of the completeness issue for future oversight conversations with the MGA.

FAQs

• What counts as 'partial' versus 'missing' bordereaux data?

  Partial data means a field is present but incomplete, inconsistent or questionable, such as a date in the wrong format. Missing data means the field is entirely absent from the submission. Both require review, but the appropriate response can differ.

• Should we always reject a bordereaux with missing data?

  Not necessarily. Rejecting an entire submission over a small number of immaterial gaps can create unnecessary delay. A materiality-based approach, where gaps are classified by their practical impact and only significant issues trigger a query or rejection, is usually more operationally sound than blanket rejection.

• Can AI decide whether missing data is acceptable?

  AI can detect gaps, classify them by likely materiality and highlight patterns across submissions, but it does not decide whether a gap is acceptable. That judgement, along with any decision to escalate or query the source, remains with experienced delegated authority professionals.