Documents—whether they are contracts, agreements (NDAs, LLCs, employment agreements, etc.), invoices, claims, reports, or other forms—all come with important data.
And they come in various formats too. Manually extracting and organizing the information they contain would require a superhuman effort.
Therefore, to streamline your business processes—and to meet the expectations of your customers who are less and less willing to wait—you need to go for a state-of-the-art solution such as Intelligent Document Processing.
What is Intelligent Document Processing?
Intelligent Document Processing (IDP) is a machine learning process that extracts information from any document, no matter its format.
It means that the processing of unstructured or semi-structured data becomes automated, saving organizations time, manpower, and money.
“AI-based extraction saves businesses 30%-40% time.”
Why you need IDP on top of RPA/OCR:
Robotic Process Automation (RPA) and Optical Character Recognition (OCR) are suitable for highly structured documents—usually the template-based ones where the expected data have a fixed position.
The problem occurs when the documents you need to read and extract data from come in Word, image files, PDF, or other forms with a random structure (it is believed this goes for around 80% of documents in organizations).
RPA and OCR cannot deal with these types of documents effectively. Still, using OCR or RPA does not mean you are doing anything wrong. But adding Intelligent Document Processing to these two takes automation to a whole new level.
“The technology behind it—Machine Learning (ML) and Artificial Intelligence (AI)”
Intelligent Document Processing uses AI-based technologies that understand the document’s content. This enables it to detect variations in the document and capture them correctly.
It also uses Machine Learning. Specialized algorithms analyze documents with various data components (not only words but also graphs and charts, for example), then extract and display data from the document.
A good example would be addresses, phone numbers, invoice amounts, or a customer profile.
Once the data have been collected and extracted, they get the human touch: final verification of the data performed by a real person.
The feedback that the person enters serves as further knowledge for the IDP, so that it becomes better and more accurate with every subsequent processing.
The five-step process of IDP, or how it works
1. Document collection
Collecting and arranging all the documents you wish to process (both paper-based and digital) may sound obvious.
But since it can take a significant amount of time, we always take this into account in the whole IDP timeline.
In this pre-process phase, intelligent software cleans the documents by reducing noise such as marks or stains, rotating them to their proper orientation, cropping, changing their brightness, etc.
This step also uses the above-mentioned OCR techniques. The point is to put things in order first to make the IDP as accurate as possible.
Documents are broken down into categories, which helps in selecting and further processing only those data which are relevant.
At this point, it is useful to check the automated classification manually.
Specific visual and textual data are extracted from the pre-processed documentation.
The technology is trained not only to read but also to understand the information, and therefore the accuracy of the data extracted is close to 99%.
The data can be reviewed by AI and humans. Machine Learning can correct commonly occurring mistakes such as misspellings and standardize information to the selected formatting.
The final human touch in the process is called Human in the Loop (HITL) and ultimately it leads to improved Machine Learning.
Would IDP make sense for your business workflows?
Here are some IDP use cases that may inspire you:
E-mail overload is something many businesses struggle with. But handling e-mails manually is not a solution—it costs you time and there is always the risk of information not being processed properly.
Another challenge comes from e-mail complexity. They often come with attachments and lack information about the subject, sender, etc.—so a perfect case for using Intelligent Document Processing.
Your clients probably have to do annoying and repetitive tasks, such as returning a form, sending a copy of the desired document, and other routine tasks.
You can relieve them of this burden by letting them upload a document from wherever they are, and then extracting the data it contains and connecting it to the specific customer case.
ML and AI deliver process automatization results of over 95%.
Classification and extraction of incoming mail:
If you get rid of manual document classification and data extraction, you can save up to 60% of your time.
If you then also use our Digital Mailroom solution, the information will go directly to the right person or workflow.
Plus, you can make it easier for your customers to send you documents—we will create a designated, customizable upload portal that they can use directly from their mobile phones.
Sensitive data storage and usage (GDPR compliance):
Automated anonymization solves your worries about whether your systems contain data that should not be there according to data protection laws.
Our solution uses AI and ML to recognize and anonymize all your incoming information—reducing texts, blurring or blacking out images (such as ID headshots). All on the spot and in real time.
How to start with Intelligent Document Processing?
Even though you are already working with intelligent technologies such as OCR or RPA, you have most probably come to a point where you need to move on—both to streamline your processes and meet the expectations of your clients.
Implementing the AI/ML-based technologies is the step to take. Please consider which of the four use cases we’ve outlined (or a combination of them) would be suitable for your business case.
If you have an idea in mind or need assistance, we are here for you to give advice and implement IDP.