We have a multi-layered framework to harness the combined power of machine learning algorithms,
OCR, and a series of validation processes to make invoice and expense handling more efficient.
Our In-House Multi Layered Framework
Digitise Invoices Through OCR
While using OCR technology has its challenges,
OCR is still a critical first step in extracting data from images.
We use a combination of best-in-class and internally developed
OCR solutions to convert images of documents into text.
Our in-built algorithms adjust the submitted images to ensure
that we get the best possible results from the OCR conversions
Natural Language Processing (NLP)
The text obtained from the OCR conversion is fed into the NLP models.
These statistical models drive the relevant data classification and extraction.
The statistical models also help to overcome the issue of the various formats
that invoices and receipts have. Instead of trying to pinpoint where data is on
a particular document, like in traditional OCR solutions, with NLP, an understanding
of this document is built in order to extract the relevant data.
Leverage a Layered Business Validation Process
After NLP has driven the relevant data classification and extraction,
logical/business validation adds an extra layer of checks to ensure data is
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 business validations apply deterministic rules on the outcomes of stochastic models,
the accuracy of the output is greatly increased.
Handle Exceptions In-House
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 NLP models are lower than required
or logical/business validations fail, the invoices or receipt will be sent
to an operator for closer attention.
These processes ensure clients get the most accurate data possible
which can be used with no extra verification.
Also, this process ensures that issues detected are used to correct
and configure the NLP models and logical/business validation rules.
This process continuously improves accuracy of the solution via iterative learning.