How does AI map bordereaux to a target schema?
AI maps bordereaux to a target schema by identifying the business meaning of source fields and matching them to the organisation's required output structure. The process works best when the target schema, validation rules and review thresholds are clearly defined before automation begins.
Key takeaways
- A target schema defines the structure the bordereau data must become.
- AI can map different source fields to common target fields.
- Validation rules are essential to confirm that mapped data is usable.
- Human review is needed where mappings are uncertain or business impact is high.
Most delegated authority teams are not trying to extract data for its own sake.
They need bordereaux transformed into a format that downstream systems, reporting processes and business teams can use.
That required format is the target schema.
Mapping to a target schema is where AI can create significant operational value, because it helps bridge the gap between inconsistent incoming bordereaux and consistent internal data structures.
What is a target schema?
A target schema defines the fields, formats and rules that bordereaux data should follow after processing.
It may include:
- Required field names.
- Data types.
- Date formats.
- Currency standards.
- Mandatory fields.
- Accepted values.
- Validation rules.
- Downstream system requirements.
Without a target schema, processing can become inconsistent because there is no agreed definition of what the output should look like.
How AI performs mapping
AI first interprets the source bordereau.
It identifies fields such as policy reference, premium, inception date, claim paid or reserve value.
It then matches those source fields to the corresponding fields in the target schema.
For example:
- Policy Ref may map to Policy Number.
- GWP may map to Gross Written Premium.
- Loss Date may map to Date of Loss.
- Paid to Date may map to Claims Paid.
The mapping should be recorded so that reviewers can see how the data has been transformed.
Why mapping is more than renaming columns
Mapping is not simply a column-renaming exercise.
Values often need to be standardised as well.
Dates may need converting. Currencies may need normalising. Names may need matching to approved reference data. Some fields may need splitting or combining before they fit the target structure.
AI can support this process, but business rules must define what is acceptable.
Validation after mapping
Once data has been mapped, it should be validated.
Typical checks include:
- Are mandatory fields populated?
- Are dates valid?
- Do premium totals reconcile?
- Are currencies recognised?
- Are duplicate records present?
- Are values outside expected ranges?
Validation confirms whether the mapped output is suitable for further processing.
Operational considerations
A poor target schema will lead to poor outcomes.
Before introducing AI, organisations should confirm what data they actually need, who uses it and what decisions it supports.
The best target schemas are practical, stable and aligned to business use cases.
They should improve delegated authority data flow rather than simply reproduce every field received from every coverholder.
Example
A managing agent wants all premium bordereaux transformed into a standard structure containing policy number, insured name, inception date, expiry date, gross written premium, commission and currency.
One coverholder sends Policy Ref, another sends Certificate ID, and a third sends Contract Number.
AI maps each of these to the target Policy Number field, applies validation rules and flags uncertain mappings for review before the data is loaded.
FAQs
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What is the difference between extraction and mapping?
Extraction identifies data in the source bordereau. Mapping places that data into the organisation's required target structure.
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Does AI create the target schema?
AI can help suggest a schema, but the organisation should own and approve the target structure because it reflects business, reporting and governance requirements.
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Can one target schema work for every bordereau?
Not always. Some common fields may apply across many bordereaux, but class-specific or process-specific data may require additional structures.
See it on your own bordereaux template
Send us your target BDX format and we'll show how AI can transform typical market bordereaux into your required structure.
