What is the difference between OCR and AI bordereaux processing?
OCR converts scanned or image-based bordereaux into machine-readable text. AI interprets that information, understands the business meaning of the data and maps it into a usable structure. OCR extracts content; AI understands it. Used together, they enable a more effective delegated authority data flow.
Key takeaways
- OCR and AI perform different roles in bordereaux processing.
- OCR extracts text from documents, while AI interprets the business meaning of the data.
- AI is particularly valuable where bordereaux formats vary between coverholders.
- The most effective delegated authority processes often combine OCR, AI and human oversight.
Many delegated authority teams receive bordereaux as PDF documents rather than Excel spreadsheets.
Some are generated directly from underwriting systems. Others are scanned copies of printed reports received from coverholders around the world.
For many years, the first challenge has been converting those documents into machine-readable data.
That is the role of Optical Character Recognition (OCR).
However, extracting the text is only one part of the problem. The greater challenge is understanding what that information represents and transforming it into a structure that supports underwriting, oversight and reporting.
OCR extracts information
OCR has been used successfully for many years to convert scanned documents into searchable, machine-readable text.
Without OCR, a scanned bordereau is simply an image.
OCR identifies the characters on the page and converts them into digital text that can be processed by other systems.
This is an important capability, particularly for organisations receiving scanned bordereaux from overseas coverholders or legacy systems.
However, OCR does not understand the meaning of the information it extracts.
It simply recognises characters.
The traditional approach
Historically, OCR has been followed by significant manual effort.
Operations teams review the extracted information, identify the purpose of each column, correct any recognition errors and map the data into the organisation's target structure.
This process has worked well for many years.
It is reliable, well understood and provides important opportunities for human validation.
The drawback is that it can become increasingly time-consuming as bordereaux formats vary between coverholders.
AI interprets information
Recent advances in AI introduce a capability that did not previously exist.
Rather than relying solely on predefined templates or manual mapping, AI can interpret the business meaning of the extracted data.
For example, it can recognise that Policy Ref, Certificate Number and Policy ID may all represent the same business concept.
It can identify table structures, distinguish between premium and claims information, recognise dates, currencies and financial values, and map those fields into a target schema.
This is fundamentally different from OCR.
OCR identifies characters.
AI interprets meaning.
Why this changes delegated authority processing
For many years, organisations largely accepted that interpreting each new bordereau format was simply part of delegated authority operations.
Modern AI changes that assumption.
For the first time, technology can help interpret unfamiliar bordereaux by understanding the information they contain rather than relying entirely on predefined mappings.
That does not remove the need for governance or human expertise.
Instead, it allows experienced delegated authority professionals to spend less time interpreting routine layouts and more time reviewing exceptions, improving data quality and supporting better business decisions.
Operational considerations
OCR and AI should not be viewed as competing technologies.
They solve different problems.
OCR is often the first stage of document processing.
AI builds on that foundation by interpreting, validating and transforming the extracted information.
Organisations considering AI should first understand where OCR ends and where business interpretation begins.
That distinction helps define realistic expectations and identify the greatest opportunities for improving delegated authority data flow.
Example
A Lloyd's managing agent receives monthly scanned claims bordereaux from several overseas coverholders.
OCR converts each scanned document into machine-readable text.
AI then identifies the claims table, interprets the business meaning of each field, maps the information into the managing agent's target schema and highlights any uncertain records for review.
Operations teams focus their attention on the exceptions rather than manually interpreting every bordereau.
FAQs
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Is OCR a type of AI?
Modern OCR products may include AI capabilities, but OCR and AI perform different functions. OCR focuses on converting images into text, while AI interprets the meaning of that information.
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Can AI replace OCR?
Not usually. If a bordereau is a scanned image, OCR is generally required to convert it into machine-readable text before AI can analyse the content.
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Why do organisations use OCR and AI together?
OCR extracts the information from the document. AI interprets that information, maps it into the required structure and supports validation. Together they provide a more complete delegated authority processing workflow.
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