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Line Items Digitisation

One of the primary challenges in invoice digitisation is line item processing. This can result in errors and delays. Inaccurate or incomplete line item data can result in mistakes, which cost companies an average of 61 £ per invoice to correct.


To overcome this challenge, Paperless Hub uses AI to extract and process line item details, match these items to the client-specific configurations, clean and enhance the data and highlight any discrepancies for review.


Line Items Extraction

For each line item, key values are automatically identified and extracted. The key values list: line number, description, unit net, vat, vat%, vat code, gross, quantity, discount, discount %, credit/debit, and date.

Paperless Hub Merge & Split functionality aims to optimise bookkeeping, while the AI engine ensures active learning through validation.


Matching Line Items Details

Machine Learning algorithms establish correct Ledgers, Departments, Projects and Cost Centres allocated to the line item based on validated records.

Line Items values are reconciled, and exceptions are highlighted for review.


Line Items Validation

Paperless Hub is equipped with colour-coded accuracy for ease of use.

Users can enable auto-approve for set accuracy levels, specific suppliers, certain invoice thresholds, certain currencies, or other user-defined custom rules. Bespoke rules can be set for the receiver and/or supplier to streamline operations and reduce low-value activities.


Line Items Export & Integrations

Prepare line item templates for excel export by customising the selection, order, name or format of outputted date, in line with your accountancy software provider.

Integrate with standard accountancy software providers through our built-in connectivity.




Machine Learning

The use of machine learning algorithms significantly enhances the accuracy of invoice digitisation. Paperless Hub includes machine learning models trained on validated data to predict which details are likely to be applicable when dealing with line items. This feature improves the matching accuracy and reduces the need for manual intervention. The machine learning algorithms can also identify patterns in the data that may not be apparent to the human eye. For example, it can locate vendors who consistently make invoice errors.


Deep Learning

Another crucial aspect of line item processing is active learning. Paperless Hub active learning enables users to provide enhancements through validation. This feedback continually improves the algorithms, ensuring the solution becomes more accurate over time and throughout changes.





Paperless Hub with line items can help businesses streamline invoice processing, reduce errors, and improve efficiency.

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