What rules govern transforming raw bordereaux into usable data?

Every delegated authority operation eventually faces the same question: once a bordereau lands in your inbox, what actually needs to happen to it before it can be trusted?

The raw file, however messy, is not the problem. The problem is that turning it into something usable, data that can be booked, reconciled and reported, is not a single mechanical step. It is a series of checks and judgements governed by rules.

Those rules fall into four categories: structural, semantic, referential and business/regulatory. Understanding them matters because they define what "correct" transformation actually looks like, whether the work is done by an experienced analyst, a scripted process, or an AI system.

Why bordereaux transformation needs rules

A bordereau arrives as a spreadsheet. A usable dataset is something quite different: a set of records that conform to a target schema, agree internally, and satisfy the reporting obligations attached to the binder.

Getting from one to the other requires interpretation, not just reformatting. A column header has to be understood, not just matched. A date has to be recognised as a date in a particular format, then converted correctly. A currency figure has to be tied to the right exchange rate and reporting basis. A policy reference has to correspond to something real.

If this interpretation happens without clear rules, the result can look perfectly structured while still being wrong. Rows import cleanly, columns line up, and yet premiums are booked in the wrong currency, or claims are linked to policies that do not exist in the binder register. The risk is not obvious data corruption. It is data that passes a visual check but fails when it reaches accounting, exposure management or regulatory reporting.

The four categories of transformation rules

Structural rules govern whether the incoming data can be reshaped to fit the target format: which columns map to which fields, how multiple worksheets or merged cells are handled, and how the data is reorganised into rows and fields the target system expects.

Semantic rules govern whether each value means what it is supposed to mean once transformed. A date must be interpreted in the correct format before conversion. A peril description must be mapped to a standard classification. A premium figure must be understood as gross or net, not just copied across.

Referential rules govern whether related values agree with each other and with existing records. Does the policy reference on a bordereau row exist in the binder register? Does the coverholder name match an approved list? Do claim references tie back to a known policy?

Business and regulatory rules govern whether the transformed data satisfies the reporting and compliance obligations attached to the class of business and the regulatory regime, such as Lloyd's reporting requirements, minimum data standards, or contractual reporting terms in the binder agreement.

All four categories have to be satisfied. Structural rules alone produce data that fits the shape of the target system without necessarily being true. Semantic and referential rules catch errors that structural mapping cannot see. Business and regulatory rules confirm the result is actually fit for its intended purpose.

How organisations have traditionally enforced these rules

Historically, this work has depended heavily on experienced analysts. Someone familiar with a particular coverholder's reporting habits reviews each bordereau, recognises known quirks, and manually maps fields into the target structure. Spreadsheet-based templates and macros formalise some of this knowledge, and bespoke scripts or outsourced bureau processing add further consistency for higher volumes.

These approaches work, and the judgement involved is genuinely valuable. But they have real limitations. Knowledge of a coverholder's format idiosyncrasies often sits with one or two individuals rather than being documented anywhere systematic. Templates and scripts need updating every time a coverholder changes its layout. And consistency across a large panel of coverholders, each handled slightly differently by different analysts, is difficult to guarantee.

Where AI helps apply transformation rules

AI is well suited to applying structural, semantic, referential and business rules consistently across large volumes of varied bordereaux. Rather than relying on a fixed template per coverholder, AI can interpret the meaning of fields and values directly, recognising that "GWP", "Gross Premium" and "Premium Amount" likely represent the same concept, or that a date string in an unfamiliar format still represents a date.

This reduces the repetitive interpretation burden that would otherwise fall on analysts for every new or changed bordereaux format. It does not remove the need for the rules themselves, and it does not remove the need for human judgement on genuine exceptions.

Where a peril description cannot be confidently mapped, or a policy reference cannot be matched against the binder register, that row should be flagged for review rather than resolved silently. AI supports consistent rule enforcement at scale; it does not replace the sign-off and exception handling that experienced DA professionals are responsible for.

Operational considerations for rule-governed transformation

Whichever method is used, certain things need to be in place.

Transformation rules should be documented and version-controlled, so that when a target schema or regulatory requirement changes, the update is deliberate and traceable rather than an undocumented tweak to a script or template.

Every transformed value should be traceable back to the original submitted value. If a premium figure or date has been converted or reinterpreted, it should be possible to show exactly what the source value was and how it was transformed.

Exceptions and ambiguous data should be escalated to a person, not quietly resolved by best guess. A missing or unrecognised value is a signal that something needs review, not a gap to be filled automatically.

Finally, rules should be reviewed periodically. Target schemas evolve, regulatory reporting requirements change, and coverholders adjust their own systems. Rules that were correct last year may need revisiting.

Example

A Lloyd's managing agent receives monthly bordereaux from an overseas MGA writing agricultural risk. The MGA's spreadsheet uses local date formats, a different currency to the binder's base currency, and free-text peril descriptions.

Before the data can be booked into the agent's systems, it must be transformed against the agent's target schema: dates standardised, currency converted at the correct rate, perils mapped to standard classification codes, and policy references cross-checked against the binder register.

The transformation process applies structural rules to reshape the spreadsheet into the target format, semantic rules to standardise dates, currencies and peril codes, referential rules to confirm policy references exist in the binder register, and business rules to confirm the data meets Lloyd's reporting requirements.

A handful of rows with unmapped peril descriptions are flagged for the analyst's review rather than guessed, preserving data integrity while still processing the bulk of the bordereaux automatically.

FAQs

• Is transforming bordereaux data just a matter of renaming columns?

  No. Renaming or reshaping columns is only the structural part of the process. The data also has to satisfy semantic rules (does each value mean what it should), referential rules (do related values agree with each other) and business or regulatory rules (does the data meet reporting requirements) before it is genuinely usable.

• What happens if transformation rules are not clearly defined?

  Data can appear structured and clean while still containing hidden errors, such as mismatched currencies, incorrect date conversions or invalid policy references. These issues tend to surface later during reconciliation or regulatory reporting, often at greater cost to fix than if they had been caught during transformation.

• Can AI apply these rules without human oversight?

  AI can apply structural, semantic, referential and business rules consistently across large volumes of bordereaux and flag exceptions for review. Judgement on ambiguous or exceptional cases should still rest with experienced delegated authority professionals. AI supports consistent rule enforcement; it does not replace oversight.