Can AI validate bordereaux automatically?

Many checks on an incoming bordereau can run without a person examining every cell. That is automatic validation. It does not follow that AI performs every check, or that a bordereau becomes automatically acceptable after receiving a high score.

The safest approach uses exact rules for requirements that can be stated precisely, and AI-supported signals where meaning or expected patterns vary. The workflow then applies approved consequences to the results.

The important question is not only whether a check can run automatically, but what evidence it produces and what the organisation permits to happen next.

Many validations do not need AI

Deterministic rules are well suited to explicit requirements. They can check whether mandatory fields are populated, dates can be parsed, currencies use an approved list and identifiers follow an expected pattern. They can test whether inception precedes expiry, whether a transaction reference is unique and whether a value falls within an agreed range.

Arithmetic checks can recalculate totals, commissions or movements. Relational rules can compare risk, premium and claims records, while control totals can test whether the transformed population agrees with the source.

These checks are automatic, but they are not necessarily AI. Their advantage is clarity: the condition, tolerance and outcome can be stated, tested and versioned. Where Lloyd’s core reporting requirements or a contract defines the expected information, the local implementation should translate that requirement into an approved rule and disposition.

Not every blank or unusual value is invalid. Conditional requirements need the relevant context, and thresholds should reflect the intended use rather than convenience.

AI can add contextual validation signals

Some questions are difficult to reduce to one exact rule. A field may contain a plausible value that conflicts with neighbouring fields. A label may have been mapped to the wrong target concept. A record or monthly movement may be unusual compared with a relevant peer or its own history.

AI or statistical methods can generate semantic consistency and anomaly signals for these situations. They can rank cases for review and present the features or source evidence that made a case unusual.

Such a signal is not proof that the data is wrong. Legitimate large losses, portfolio changes and unusual risks occur. Conversely, a value can look typical and still be wrong. AI-supported validation therefore complements exact rules rather than replacing them.

The comparison context matters. A claims movement should not be judged against an unrelated line of business, and a coverholder’s seasonal portfolio should not be compared with a flat annual expectation without adjustment.

Automatic checking is not automatic acceptance

Every validation result needs a defined operational consequence. A hard failure may stop the affected records. A warning may allow processing to continue but create evidence for later review. An uncertain semantic mapping may require a specialist decision. A minor issue may be corrected only through an approved, traceable process.

A confidence score should not become an undocumented acceptance threshold. If high-confidence cases are eligible for straight-through processing, the conditions should be approved and supported by representative evidence. Required deterministic checks and reconciliations still apply, and a safe fallback is needed when an input falls outside the tested conditions.

The materiality and downstream use affect the control. A formatting normalisation may be low impact. A value used to assess authority, calculate money or meet regulatory reporting can require stronger evidence and review.

Automatic validation describes how checks run. Acceptance describes an accountable decision under the organisation’s control framework.

Validation quality must be measured

Checks should be tested on representative valid, invalid, borderline and changed cases. Testing only known clean files can show that the workflow passes easy cases without showing whether it detects important problems.

Teams should measure false positives—valid data unnecessarily referred—and false negatives—problems allowed through. The second can be harder to observe, so sampling, reconciliation, downstream feedback and independent review matter.

Production measures can include failure rates, exception age, overrides, recurrent causes and outcomes by source or business segment. Rule, tolerance, reference-data and model changes need version control and approval.

If a coverholder changes a format, a portfolio shifts or a reporting requirement changes, earlier performance may no longer apply. Monitoring should detect those changes and route affected cases safely while the workflow is reassessed.

Automatic validation can remove repetitive checking, but its success is measured by accepted data and controlled exceptions—not by the number of green ticks generated.

Example

A claims bordereau passes required-field, date, currency and arithmetic checks. Its control total also reconciles to the submitted summary. One paid movement is nevertheless much larger than comparable claims in the relevant segment.

An anomaly signal creates a review item with the source record and comparison context. A claims analyst checks the movement against the underlying claim information and confirms that it reflects a legitimate settlement.

The bordereau was structurally valid, and the unusual value was not an error. The recorded disposition prevents the signal from being confused with a failed rule and provides evidence for monitoring the usefulness of the check.

FAQs

• Which validation checks are easiest to automate?

  Explicit completeness, data-type, code-list, date, arithmetic, uniqueness and control-total rules are usually the clearest candidates because their expected result can be stated and tested.

• Can AI decide that a bordereau is correct?

  No single model score proves that a bordereau is correct or fit for every purpose. Acceptance depends on approved checks, reconciliations, materiality and any required human decision.

• What should happen to a failed check?

  The approved disposition should apply: stop or quarantine the data, issue a warning, make a traceable correction, request resubmission or refer the case to a named owner.