Innovo Invoice is a invoice digitization application that takes in invoices of multiple formats (PDF, PNG, JPEG), extracts the relevant data and returns it in a structured format that can be used for booking into accounting or ERP systems with little or no manual effort.
Invoice Digitization vs E-Invoicing vs Invoice Issuance
Invoice digitization should not be confused with e-invoicing or invoice issuance.
E-invoicing solutions require that both supplier and customer are on a platform that allows the exchanges of invoice in a structured electronic format. This means that the supplier and customer need to agree on the format of the invoice message before processing.
Invoice issuance is done when a company renders a service and then invoices for it. Today, this can be automatically carried out via accounting and ERP systems.
The source of a digitized invoice could be a scanned copy or image of a paper invoice or a PDF copy of an invoice. The digitization process extracts the relevant data from these invoices and produces a digitized invoice.
This process helps reduce the manual efforts required in keying in incoming supplier invoices into an accounting or ERP system. Unlike e-invoicing, with invoice digitization there is no need for suppliers and customers to agree on a format, which can prove to be challenging.
How does Innovo Invoice work?
Invoices received from a supplier can be dragged and dropped, emailed or sent via an API call to Innovo Invoice. The application will extract the relevant information like vendor details, invoice and due dates, tax, total amounts and line items. The extracted data can then be sent to downstream systems for booking. Custom field extractions can be handled upon request.
How does Innovo Invoice extract data and ensure a high level of accuracy?
While many would consider an application like Innovo Invoice similar to an OCR (Optical Character Recognition) tool, there is much more to extracting data and ensuring accuracy than OCR.
Innovo Invoice uses a multi-stage process of the extraction and validation of data.
Scanned documents and images of invoices received in multiple formats are first converted to text via OCR.
2. Natural Language Processing
The text obtained from the OCR is then fed into the machine learning models. These statistical models drive the relevant data classification and extraction.
3. Logical/Business Validation
Logical/business validation adds an extra layer of checks to ensure data is captured accurately. Examples of business validation include checking if a transaction date is not in the future or if the tax amount is the correct percentage of the total amount.
As these validations apply deterministic rules on the outcomes of stochastic models, the accuracy of the output is greatly increased.
4. Exception Handling
A robust exception handling process with algorithmic intervention based on insights from the collected data acts as a last line of defence.
If the confidence levels provided by the machine learning models are lower than required or logical/business validations fail, the invoices will be sent to an operator for closer attention.
This processes ensure that, as a client, you get the most accurate data possible which can be used with no further verification.
Also, this process ensures that issues detected are fed back into the machine learning models and logical/business validation rules so that they continually improve via iterative learning.
If you'd like to know more, or have any further queries, please feel free to chat with us in the Messenger or contact us at [email protected]. 👋🏻