What data can AI extract from a bordereau?
AI can extract common delegated authority data such as policy references, premiums, claims, dates, currencies, insured details, risk attributes and exposure information. The exact fields depend on the type of bordereau and the target structure the organisation needs.
Key takeaways
- AI can extract policy, premium, claims, risk and exposure information.
- The value comes from mapping extracted data into a usable target structure.
- Free-text and class-specific fields often require additional review.
- Extraction should always be combined with validation and exception handling.
Bordereaux contain the information that allows delegated authority business to be understood, monitored and reported.
The difficulty is that this information is often spread across different formats, field names and structures.
Before it can support underwriting, claims, oversight or regulatory reporting, the relevant data needs to be identified and extracted.
AI can help by recognising the business meaning of fields and transforming them into a consistent structure.
Common policy and contract information
Most bordereaux include identifiers that connect the record to a policy, certificate, contract or binding authority arrangement.
AI can often identify fields such as:
- Policy number.
- Certificate reference.
- Binder reference.
- Coverholder name.
- Broker name.
- Insured name.
- Inception date.
- Expiry date.
These fields are important because they allow records to be reconciled across systems and reporting periods.
Premium information
Premium bordereaux typically include financial data that must be accurately extracted and validated.
Common fields include:
- Gross written premium.
- Net premium.
- Commission.
- Taxes and fees.
- Currency.
- Settlement amounts.
- Written or earned premium values.
AI can identify these fields even where labels differ, but reconciliation checks remain essential.
Claims information
Claims bordereaux may include both structured values and descriptive fields.
AI can help identify:
- Claim references.
- Date of loss.
- Notification date.
- Paid amounts.
- Outstanding reserves.
- Recoveries.
- Claim status.
- Cause of loss.
- Narrative descriptions.
Claims data often requires more careful review because free text may contain important context.
Risk and exposure information
Risk and exposure bordereaux vary significantly by class of business.
Depending on the portfolio, AI may extract:
- Location information.
- Sums insured.
- Occupancy.
- Construction type.
- Vehicle details.
- Vessel details.
- Limits.
- Deductibles.
- Territorial information.
These fields are often used for accumulation analysis, underwriting insight and portfolio management.
Why the target structure matters
Extraction on its own is not enough.
An organisation needs to define what the extracted data should become.
That means agreeing the target schema, mandatory fields, validation rules and downstream use cases.
Without that structure, AI may extract information successfully but still leave teams with data that is difficult to use.
Operational considerations
The best extraction workflows are designed around business outcomes.
A claims team may need different fields from an underwriting team. An oversight team may focus on timeliness, completeness and exception reporting.
Before implementing AI extraction, teams should decide what decisions the data needs to support.
Example
A delegated authority team receives a risk bordereau for a property portfolio.
The spreadsheet includes insured details, location information, sums insured, occupancy and premium values, but the field names do not match the insurer's internal data model.
AI identifies the relevant business fields, extracts the values and maps them to the insurer's target structure.
Records with missing postcodes or unusual sums insured are flagged for review.
FAQs
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Can AI extract both structured and free-text data?
Yes. AI can extract structured fields such as policy numbers and dates, and can also interpret free-text descriptions. Free-text outputs should usually receive additional review.
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Does AI know which fields are mandatory?
Only if the process has been designed that way. Mandatory fields should be defined in the target schema and checked through validation rules.
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Can AI extract class-specific risk information?
Yes, but class-specific fields usually require a clearer target structure and more review, especially where terminology varies between coverholders.
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