Why is bordereaux processing difficult?
Bordereaux processing is difficult because every coverholder, broker and delegated authority arrangement can produce data in different formats, using different structures and terminology. Before the information can support underwriting, oversight or reporting, it must be understood, validated and transformed into a consistent format.
Key takeaways
- No two bordereaux are exactly the same.
- Differences in layouts, field names and data quality create significant manual effort.
- Traditional import tools rely on predefined templates that require continual maintenance.
- AI reduces effort by understanding the meaning of data rather than simply matching column names.
Every delegated authority operation faces the same challenge.
Bordereaux arrive every month from multiple coverholders, managing general agents and third parties, each with their own preferred spreadsheet layouts, terminology and reporting standards.
Although the information may represent exactly the same business concepts, the way it is presented can vary enormously.
Before that data can be trusted for underwriting, claims analysis, regulatory reporting or delegated authority oversight, it first has to be understood and standardised.
That is why bordereaux processing remains one of the most labour-intensive activities within delegated authority.
Every bordereau tells the same story differently
At first glance, two premium bordereaux may appear almost identical.
However, one may refer to Policy Number, another Policy Ref, while a third simply uses Certificate ID.
Premiums may be reported as Gross Premium, Written Premium, Premium Amount or abbreviated entirely.
Dates may use different regional formats, currencies may be represented differently and mandatory fields may appear in different positions—or not at all.
For a human, recognising these differences is relatively straightforward.
For traditional software, they often require an entirely new import template.
Different structures create different problems
The challenge extends far beyond column names.
Bordereaux regularly contain:
- Multiple worksheets.
- Hidden columns.
- Merged cells.
- Summary rows mixed with transactional data.
- Blank rows and formatting.
- Notes inserted between records.
- Formula-driven values.
- Inconsistent date formats.
- Multiple currencies.
Even when two bordereaux contain identical information, the underlying spreadsheet structures may be completely different.
Data quality varies considerably
Another challenge is the quality of the underlying data.
Common issues include:
- Missing policy references.
- Invalid dates.
- Duplicate records.
- Incorrect currency codes.
- Unexpected premium values.
- Missing claims information.
- Inconsistent broker or coverholder names.
Operations teams therefore spend significant time validating data before it reaches downstream systems.
Traditional processing depends on templates
Most import solutions rely on predefined mappings.
Someone must decide that Policy Ref maps to Policy Number, that GWP means Gross Written Premium, and that the tenth column always contains the effective date.
Whenever a coverholder changes their spreadsheet—or introduces a new product—those mappings often require updating.
Over time, organisations accumulate hundreds of templates that become increasingly difficult to maintain.
Why AI changes the approach
Modern AI approaches the problem differently.
Rather than relying solely on column names, AI considers the content, context and relationships between fields.
It recognises that different terminology can describe the same business concept and can map information into a standard target structure without requiring a completely new template every time the layout changes.
Human oversight remains important, particularly where confidence is low or business rules identify exceptions, but much of the repetitive interpretation work can be significantly reduced.
Ultimately, the biggest challenge in bordereaux processing is not the data itself.
It is understanding the data quickly, consistently and at scale.
Example
An insurer receives monthly premium bordereaux from thirty coverholders.
Every bordereau contains policy references, premiums and inception dates, but no two spreadsheets use the same layout.
Instead of maintaining thirty individual import templates, AI analyses the structure of each submission, recognises equivalent business concepts and transforms the information into a common format for validation.
Operations teams spend their time reviewing exceptions rather than manually reformatting spreadsheets.
FAQs
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Why are bordereaux different between coverholders?
Each coverholder typically develops its own reporting formats based on local systems, products and operational processes. Unless a common reporting standard has been agreed, layouts and terminology often vary considerably.
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Why can't traditional software handle different bordereaux automatically?
Most traditional import tools rely on predefined mappings and templates. When layouts or field names change, those templates usually need updating before the data can be processed correctly.
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Is poor data quality the biggest challenge?
Data quality is one challenge, but understanding the structure and meaning of each bordereau is often the most time-consuming part of the process. AI can help address both challenges by combining intelligent interpretation with automated validation.
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