How can AI tools validate delegated authority data automatically?

Delegated authority data arrives in large volumes and varying quality, and before it can support underwriting decisions, accounting or regulatory reporting, it has to be checked.

That checking process is validation: confirming that the right fields are present, that values make sense, and that nothing looks obviously wrong compared with what's expected.

For years, this has meant either manual review against a checklist or automated checks against a fixed set of rules.

AI tools now offer a third option, one that can learn from patterns in the data rather than relying solely on predefined conditions.

This article explains what that actually means in practice, and where the boundaries of AI-based validation lie.

The scale of the validation challenge in DA data

Bordereaux and other delegated authority submissions arrive from many coverholders and MGAs, each with their own systems, conventions and levels of data discipline.

One submission might be missing policy references. Another might use currency codes inconsistent with the territory of risk. A third might report premium figures that don't reconcile with the number of policies listed.

Oversight teams are expected to catch these issues before the data is used, but the volume of submissions, combined with limited review resource, makes consistent manual checking difficult to sustain month after month.

The problem isn't simply the number of errors. It's the range of different ways errors can appear, which makes any single fixed checklist incomplete almost as soon as it's written.

Traditional approaches to data validation

Most organisations validate DA data through some mix of manual review and rule-based automation.

Manual review relies on experienced staff checking submissions against a checklist or their own knowledge of what a scheme normally looks like. This is transparent and can catch contextual issues that a machine might miss, but it doesn't scale well and is vulnerable to fatigue and inconsistency between reviewers.

Rule-based validation, by contrast, encodes specific conditions into scripts or software: a policy number field must not be blank, a premium value must fall within a defined range, a date must be in a valid format.

Rules are predictable and easy to explain to auditors, which matters in a regulated market. However, they are brittle. A rule written for one coverholder's format often doesn't apply cleanly to another's, and rules need to be maintained every time a submission's structure or terminology changes.

Neither approach on its own scales comfortably with growing submission volumes and format variety.

Where AI helps

AI-based validation extends the checking process in ways that go beyond fixed rules, generally falling into four categories.

Structural validation confirms that expected fields are present, correctly typed and appropriately formatted, similar to rule-based checks but with more tolerance for variation in layout and naming.

Semantic validation checks whether the data makes sense given its context. For example, does the class of business match the type of risk described in the policy terms, or does a claims reserve figure look plausible for the type of loss recorded.

Cross-referential validation checks consistency between related fields or across related records, such as premium figures reconciling against policy counts, or claims data aligning with the underlying policy schedule.

Anomaly detection compares a submission against historical patterns for that coverholder or scheme, flagging entries that deviate unusually, even where no specific rule has been broken.

What distinguishes AI from a rules engine is its ability to learn these patterns from data rather than requiring every condition to be manually specified in advance. This allows it to adapt more readily to variation in format and terminology across coverholders.

That said, AI validation still depends on having a reasonably clear definition of what "correct" looks like for a given binder or scheme. Without that grounding, even a well-trained model has nothing meaningful to compare against.

Operational considerations when adopting AI validation

Introducing AI-based validation into an existing bordereaux process raises several practical questions.

First, someone still needs to define what valid data looks like for each scheme, including acceptable ranges, expected relationships between fields, and known exceptions. AI reduces manual checking, but it doesn't remove the need for this upfront definition work.

Second, false positives and false negatives need to be actively managed. A model that flags too much creates as much review burden as manual checking; one that flags too little erodes confidence and risks letting genuine errors through. Feedback loops, where reviewers confirm or correct flagged items, help refine performance over time.

Third, the benefit realised depends heavily on how well the validation step integrates with the rest of the bordereaux workflow. Validation that happens in isolation, disconnected from mapping, transformation and sign-off processes, delivers less value than validation built into the wider pipeline.

Finally, human oversight remains necessary for exceptions, ambiguous cases and any decision that carries underwriting or regulatory consequence. AI can clear the routine cases and narrow down what needs attention, but final judgement stays with experienced professionals.

Example

A Lloyd's managing agent receives monthly bordereaux from a coverholder writing agricultural risk in South America. Historically, a member of the DA oversight team manually checks each submission against a checklist, a process that takes several days per binder and often misses subtle inconsistencies, such as premium figures that don't reconcile with policy counts.

The agency introduces an AI-based validation tool that checks the submission's structure, flags fields where currency codes look inconsistent with the territory, and highlights a cluster of claims with unusually high reserve values compared to historical patterns for that coverholder.

The AI tool clears the majority of the bordereaux automatically as consistent with expected patterns, while flagging a small number of entries for the analyst's attention. The analyst reviews only the flagged items, reducing review time significantly while retaining final sign-off on any queries raised with the coverholder.

FAQs

• Can AI validation fully replace manual review of bordereaux?

  No. AI reduces the volume of routine checking but human review remains essential for exceptions, ambiguous cases and final sign-off, particularly where judgement about underwriting intent is required.

• How does AI validation differ from a rules engine?

  Rules engines check against fixed, predefined conditions and struggle with variation. AI-based validation can learn patterns from historical data, adapt to format variation, and catch semantic or contextual issues that a fixed rule would not be written to detect.

• What data does an organisation need before it can use AI validation effectively?

  A reasonably consistent history of past submissions and a clear definition of expected values or ranges for a given binder or scheme help AI validation tools establish an accurate baseline for comparison and anomaly detection.