What data quality risks exist in delegated authority reporting?

Every delegated authority operation depends on data it does not directly control.

Coverholders and MGAs compile and submit bordereaux using their own systems, spreadsheets and internal processes, often across multiple territories and product lines. By the time that data reaches an insurer or managing agent, it may already contain gaps, inconsistencies or errors that are difficult to spot at a glance.

These issues are not occasional accidents. They are a structural feature of how delegated authority reporting works, and they affect everything downstream: reserving, regulatory reporting, claims handling and oversight.

Understanding the specific ways bordereaux data quality breaks down is the first step towards controlling it.

Why data quality risk is structural in DA reporting

A managing agent or insurer typically receives bordereaux from many coverholders at once, sometimes dozens, sometimes hundreds, each writing different classes of business in different territories.

Each coverholder usually operates its own policy administration system, its own spreadsheet conventions and its own internal review process before a bordereau is submitted. Some have dedicated operations teams checking data before submission. Others rely on a single administrator compiling figures manually each month.

This variation means that data quality risk is not the result of one organisation doing something wrong. It is the natural consequence of many organisations, with different systems and different levels of data discipline, all feeding into the same reporting chain.

As the number of coverholders grows, so does the range of formats, terminology and quality standards the receiving organisation must reconcile.

The main categories of data quality risk

Although every coverholder's bordereau looks different, the underlying risks tend to fall into a small number of recurring categories.

Missing or incomplete data. Fields such as currency codes, risk peril codes, policy references or claims status are left blank, either because the coverholder's system does not capture them consistently or because they were simply omitted during compilation.

Inconsistent formats and terminology. The same business concept is described differently across coverholders. One bordereau may use "Gross Premium", another "Written Premium", while dates, currencies and codes may follow different regional conventions.

Miscoding. Risk classifications, peril codes or claims categories are recorded incorrectly, often because a coverholder has applied an internal code that does not map cleanly to the receiving organisation's structure.

Duplication or overlapping records. Corrections, resubmissions or system errors result in the same policy or claim appearing more than once, sometimes with conflicting figures.

Late or partial submissions. A coverholder submits only part of the expected data for a reporting period, or submits after the reporting deadline, leaving gaps that must be chased and reconciled later.

Each of these categories can appear in isolation, but in practice they usually compound. A bordereau with missing currency codes may also contain duplicate records from an earlier correction, all within a submission that arrived later than expected.

How organisations traditionally manage these risks

Most delegated authority operations rely on a combination of manual review, static validation rules and periodic coverholder audits to manage these risks.

Manual review involves an operations analyst checking each bordereau against expected fields and known issues. This works well for smaller volumes but becomes difficult to sustain as the number of coverholders grows.

Static validation rules check submissions against predefined criteria, such as confirming that a currency code exists or that a date falls within an expected range. These rules are effective at catching known, predictable errors but require ongoing maintenance whenever a coverholder changes its format or a new product is introduced.

Spot-checks and sampling allow oversight teams to review a subset of records in detail rather than every line, trading some coverage for practicality.

Coverholder audits address the root cause by reviewing a coverholder's own data practices periodically, but these audits are infrequent and cannot catch issues arising between visits.

Together, these approaches catch a significant proportion of data quality issues. Their main limitation is scale and adaptability. As the volume and variety of bordereaux increases, purely manual or rule-based approaches struggle to keep pace with the range of formats and errors that can appear.

Where AI changes what is operationally possible

AI-based approaches extend what is operationally possible by processing large volumes of bordereaux consistently, regardless of format or coverholder.

Rather than relying solely on predefined rules, AI can recognise when a value looks inconsistent with the rest of a submission, when terminology likely refers to a known field despite unfamiliar labelling, or when a record resembles a duplicate even if it is not an exact match.

This allows anomalies and inconsistencies to be flagged across every submission, not just a sampled subset, and without requiring a new rule to be written every time a coverholder's format changes.

AI does not remove the need for human judgement. Exceptions still need to be reviewed, coverholders still need to be queried, and final sign-off on reserving or regulatory figures still rests with experienced DA professionals. What changes is the amount of repetitive checking required before that judgement can be applied, freeing oversight teams to focus on genuine exceptions rather than routine reconciliation.

Example

A Lloyd's managing agent receives monthly bordereaux from twelve coverholders writing agricultural risk across different territories. Each coverholder uses a slightly different spreadsheet template.

One coverholder's bordereau is missing currency codes for several claims entries. Another uses inconsistent risk peril codes. A third submits a bordereau with duplicate policy records from a prior correction that was never removed.

The oversight analyst uses a combination of validation rules and an AI-supported review to flag the missing currency codes, inconsistent peril coding and duplicate records automatically, rather than discovering them manually during reconciliation. The analyst reviews each flagged issue, confirms the correct treatment with the relevant coverholder, and the managing agent's reporting timeline is protected without compromising oversight sign-off.

FAQs

• What is the most common data quality risk in bordereaux?

  Missing or incomplete fields and inconsistent formatting or terminology across coverholders are among the most frequently encountered issues. They arise because each coverholder compiles data using its own systems and conventions, making gaps and inconsistencies difficult to avoid entirely.

• Why does data quality risk vary so much between coverholders?

  Coverholders differ in the systems they use, the templates they follow and the level of internal review applied before submission. These differences in process and data discipline create variation in the quality and consistency of the bordereaux they submit.

• Can automated validation rules catch all data quality issues?

  No. Static validation rules are effective at catching known, predictable issues, but they struggle with novel formats or subtle inconsistencies that fall outside their predefined criteria. This is where broader review or AI-supported anomaly detection can help extend coverage.