How does AI validate transformed bordereaux before use?

Transforming a bordereau into a consistent target structure solves one problem, but it creates another question immediately behind it: how does anyone know the transformation is correct before the figures are used to update bound premium, settle claims or feed regulatory returns?

A bordereau can be perfectly formatted and still contain errors. Those errors may have originated with the coverholder or MGA, or they may have been introduced during the transformation itself.

Validation is the step that stands between a transformed bordereau and its use in core systems. It is a distinct activity from transformation, with its own checks, its own logic and its own point at which human judgement is required.

Why transformation alone is not enough

A transformed bordereau looks correct. Fields are aligned, headers are standardised and the file matches the target structure the insurer expects.

None of that guarantees the underlying values are right.

A policy reference might be misspelled at source. A premium figure might be entered in the wrong currency. A sum insured might be ten times larger than it should be because of a decimal point error at the coverholder. Transformation will faithfully carry these errors into the target structure, because transformation is concerned with structure and mapping, not with whether the values themselves make sense.

Validation is the step that asks a different question: not "is this in the right place?" but "is this plausible, consistent and correct?"

How validation has traditionally been performed

Before automated checks were widely used, validation depended heavily on manual review.

Operations teams would spot-check a sample of lines against the original bordereau, reconcile totals in a separate spreadsheet, and apply a fixed set of rules, such as checking that dates fell within the policy period or that currency codes were on an approved list.

This approach works, but it has practical limits. Spot-checking a sample cannot catch every error in a bordereau with thousands of lines. Fixed rules only catch what someone thought to write a rule for in advance. And reconciliation spreadsheets require constant maintenance as binders, coverholders and products change.

At volume, manual validation becomes a matter of managing risk rather than eliminating it. Teams accept that some errors will pass through, and focus their limited review time on the areas judged most likely to matter.

Where AI changes what validation can achieve

AI validation applies the same underlying logic as traditional checks, structural, referential, business-rule and reasonableness checks, but does so consistently across every line of every bordereau, rather than a sample.

Structural checks confirm that required fields are present and correctly typed, for example that a date field contains a valid date. Referential checks confirm that the data is consistent with related records, such as matching a policy reference against the binder it should belong to. Business-rule checks apply the specific terms of the agreement, such as confirming that a premium falls within the rating basis agreed for that class. Reasonableness checks compare a value against historical patterns for that account or class, flagging figures that are statistical outliers even if no explicit rule was broken.

Because AI can hold and apply this many checks simultaneously, it can also detect patterns that a fixed rule set would miss, such as a coverholder whose reporting has gradually drifted from its historical pattern over several months.

What AI validation does not do is quietly correct these issues itself. Its role is to surface exceptions clearly, with enough context for a reviewer to understand why a line was flagged, and then leave the decision about what to do next to a person.

What oversight still needs to happen

Validation is only as good as the reference data it checks against. If binder terms or policy records are out of date, referential and business-rule checks will flag genuine data as an exception, or worse, wave through data that should have been queried.

Reasonableness checks also need to be calibrated to the specific class of business and account. A sum insured that is unremarkable for one coverholder may be a clear outlier for another, so thresholds cannot simply be applied uniformly across all business.

Most importantly, a flagged exception is not a resolved exception. Every flag needs a named reviewer who investigates the cause, decides whether the issue lies with the source data, the transformation, or the reference data used in the check, and takes the appropriate action. Validation reduces the volume of routine checking, but it does not remove accountability for the decisions that follow.

Example

A managing agent receives a monthly bordereau from an overseas MGA writing agricultural risk under a binder agreement. The bordereau is transformed into the agent's target structure.

Before the figures are used to update bound premium records, the validation layer checks the transformed data against the binder's coverage terms, the currency and date ranges expected for the account, and the historical premium pattern for that coverholder.

One line shows a sum insured far outside the expected range for the class of business. The outlying line is flagged as an exception rather than being accepted or silently corrected.

The DA oversight analyst reviews the flagged line against the original submission, confirms it is a genuine data entry error at source, and requests a corrected bordereau from the MGA before the record is updated.

FAQs

• Does AI validation replace manual review of bordereaux?

  No. AI validation reduces the volume of routine checking required but still relies on a human to review and resolve flagged exceptions. Sign-off remains with DA oversight professionals.

• What happens when a transformed bordereau fails validation?

  The failing line or field is flagged as an exception rather than being auto-corrected. It is routed to a reviewer who investigates the cause, which may originate at the coverholder, in the transformation step, or in the reference data used for the check.

• How does AI know what a "normal" or "reasonable" value looks like?

  Reasonableness checks are calibrated against historical patterns for the specific account, class of business and binder terms, so what counts as an outlier is defined relative to that context rather than as a single fixed threshold across all business.