What is Intelligent Document Processing? The Ultimate Guide
Data is the cornerstone of most businesses. This has led to more data being generated over the last two years than in the whole of human history before that.
Data is important for so many reasons, for progress, gaining useful insights, day-to-day workflow, employee happiness, generating sales leads – the list goes on.
While it’s all well and good having an abundance of data, it’s what you do with it that counts. This is why one of the biggest challenges many businesses are facing is utilizing this data in a smart way.
Extracting, handling, organizing and analyzing this vast amount of data can be extremely labour-intensive for the human workforce.
However, thanks to advances in technology, it’s now possible for computer algorithms to scan and understand documents as humans do.
This technology is called Intelligent Document Processing or IDP for short and has been gaining in popularity over recent years.
But what exactly is Intelligent Document Processing? Well, in this guide, we’re going to take a look at:
- What Intelligent Document Processing (IDP)
- How Intelligent Document Processing works
- Examples and use cases in Intelligent Document Processing
- The benefits of Intelligent Document Processing
- Why Intelligent Document Processing solutions are gaining in popularity
So let’s start by getting a better understanding of what Intelligent Document Processing actually is.
Defining Intelligent Document Processing
As we’ve mentioned above, data is often central to a company’s workflow and ability to function effectively. Therefore, this data needs to be easy to capture, organize and use. This is where Intelligent Document Processing comes in.
As the name suggests, IDP is about processing documents in an intelligent way that helps businesses to extract and store the data as simply and efficiently as possible.
However, the problem that many face is that data doesn’t always come in an easy-to-read or structured format. This is because today’s digital world has made it possible to share data in so many different ways.
This means as much as 80% of a business’ data is embedded in unstructured formats. This includes documents such as emails, PFDs, scanned images, online forms, receipts, and more.
This is where Intelligent Document Processing really shines.
IDP makes it possible to capture, extract and process data from these unstructured and semi-structured documents and turn this into useable data.
For this reason, it has become a game-changer for a lot of businesses. It is the next generation of automation and the ideal way for companies to transform documents into relevant, usable information on their systems.
But with so many documents to contend with, lots of them unstructured, these tools and technologies are complex. In the next section, we’re going to continue building on our understanding of IDP by taking a look at how these solutions actually work.
How does Intelligent Document Processing Work?
Intelligent Document Processing must follow a certain set of steps in order to transform unstructured and semi-structured data into structured information. Each of these steps will require different technologies to be involved. For example, machine learning, Natural Language Processing (NLP) and Robotic Process Automation (RPA).
This means that Intelligent Document Processing is not one singular type of technology rather an intricate solution made up of different tools and technologies.
While these steps can sometimes vary depending on the type of solution you’re using in your business, for the most part, IDP follows a similar structure.
As a general rule, Intelligent Document Processing works by following these five steps:
Step one: Capture
The first part of Intelligent Document Processing is setting up the different capture points.
As we said, data comes in so many different forms and so the data might be presented as a scanned image or digitally via PDF, email, or other attachments.
Either way, the IDP solution needs to be integrated and able to capture data from these documents no matter what they are. And it does this in two key ways:
Firstly, the Intelligent Document Processing solution must integrate with hardware like scanners and then use technology like Optical Character Recognition (OCR) to scan and extract data from these paper documents.
Secondly, the IDP solution must be able to ingest online documents, for example, PFDs or excel files, and then extract data from these.
Step two: Pre-processing
In order to improve the quality of the document and to ensure that the data that is being extracted through OCR is as accurate as possible, the IDP solution pre-processes the documents at every stage.
Part of this pre-processing phase requires the IDP solution to:
- Transform some of the data features into vectors or binary numbers (also called binarization)
- Eliminate unnecessary marks and scribbles on the page (referred to as noise reduction)
- De-skew any scanned images that may be wonky or scanned at a strange angle
This makes extracting the relevant data simpler and more efficient.
Step three: Classification
With so many different document formats to process, some structured and some not, even the smartest technologies need to get these organized.
As such, stage three is the classification stage in which the Intelligent Document Processing solution must group (or classify) these documents based on their format. This helps to improve the extraction and archiving of the data by ensuring the right extraction technology is being applied.
This also ensures the data is being routed through the right workflow depending on the format of the document.
At this stage, machine learning and Artificial Intelligence-based classification tools are used to analyze the content and structure of a document before separating these into the right categories.
Step four: Data extraction
This is one of the most important steps in Intelligent Document Processing and uses AI algorithms to extract data from the documents. AI technology such as Optical Character Recognition is capable of recognizing and extracting text from a scanned document or image.
Other tools such as Natural Language Processing (NLP) make it possible for computers to identify and translate accurately hand-written data.
At this stage, it is also important that IDP solutions are not only extracting data but also defining the different types of data that need to be translated. For example, numbers, dates, names, etc.
How accurate this stage is will depend on the level of training the AI algorithm has gone through.
Step five: Data Validation
Finally, after the data has been extracted, it needs to go through a number of post-processing steps. This will validate the quality and the accuracy of the data that has been pulled.
The data must be validated through pre-set logical algorithms and external databases. So at this final stage, Robotic Process Automation is used to check the data and pass it into the relevant workstreams.
For example, if data is being extracted from an insurance application, it will be automatically entered into a claim document and sent to the right department.
This is also the phase at which any inaccurate data will be flagged. It will then need to be reviewed by a human to work out where the discrepancies lie.
Examples and use cases in Intelligent Document Processing
There are numerous industries out there that use and benefit from Intelligent Document Processing and other technologies like this. Although most industries might rely on this in one way or another (even if this is through the use of a third-party service provider), there are some areas where IDP plays a much more significant role.
Below, we’ve pulled together some examples of use cases in Intelligent Document Processing and areas where IDP is most commonly used in the business world to boost efficiency. These industries include:
- Banking and mortgages
- Accounting and finance
- Human Resources (HR)
Of course, this list is by no means exhaustive. That said, it is easy to see how these industries could require support with data entry and Intelligent Document Processing.
For example, banking, accounting, and finance industries will be dealing with a lot of receipts, invoices, and application forms. They will also have to juggle forms of ID that may have been scanned in and a combination of online and paper documents to help them do their job.
As such, IDP is crucial for making data entry more efficient. It also frees up their time to focus on other important tasks such as advising or supporting clients.
Similarly, medical professionals are dealing with lots of important and sensitive data on a daily basis. From prescriptions to medical records, IDP frees up their time by dealing with these documents. It also reduces the risk of human error. Something which could have dire consequences in the medical industry.
The benefits of Intelligent Document Processing
If these haven’t already become clear throughout this guide, there are several benefits of Intelligent Document Processing. Here we have pulled together a list of seven of the reasons Intelligent Data Processing is so beneficial and why it is something many businesses are using or considering:
- Efficiency – IDP and other automated processes eliminate the need for manual work and intervention. This means with just a click of a button, data can be captured, sorted, and routed to the relevant location. This improves the overall efficiency of document and data processing.
- Speed – As well as boosting efficiency, IDP reduces the time it takes to get larger volumes of data processed. So a task that takes a human a few minutes to complete can be achieved by an IDP system in a matter of microseconds.
- Cost-effective – Companies who add IDP solutions to their workflow see quicker processing times and a decrease in labour costs. Getting more work done in a shorter space of time also saves on operational costs.
- Quality and accuracy – Every business wants to deliver the best results for themselves and for their clients and customers. IDP improves the quality of the data being extracted and reduces the risk of human error. It also automatically sorts the data into relevant, safe systems and makes this data quicker to access.
- Reliability – As well as improving accuracy, it’s also proven that machines are more reliable than humans when doing repetitive tasks.
- Compliance – Intelligent Data Processing is a secure technology that makes it better for maintaining data privacy and security. This helps businesses to stay compliant with data protection laws such as General Data Protection Regulations (GDPR).
- Scalability – IDP is not specific to any one process and is therefore scalable. This means it can be applied to multiple systems and applications to make document processing much easier and more efficient across the business.
Why Intelligent Document Processing solutions are gaining in popularity
Based on these advantages, it is easy to see why Intelligent Document Processing solutions are gaining in popularity. But not only this, as data continues to become one of the world’s most precious and valuable commodities, businesses are increasingly focusing on not only how to collect data but also how to leverage it.
With Intelligent Document Processing making data entry and analysis much quicker and easier, it stands to reason that more and more businesses are deploying these types of solutions.
IDP can help businesses cut down on labour-intensive tasks and give humans more time to work on important tasks.
With so many advantages, Intelligent Document Processing is becoming increasingly attractive to businesses across a huge range of industries.
A summary of Intelligent Document Processing
There has been a lot of information here about Intelligent Document Processing, how it works, and the impact it can have on businesses across a range of industries.
As we reach the end of this guide, we’ve pulled together some of the key points you should take away. These are:
- Intelligent Document Processing is a solution used to extract and store data as simply and efficiently as possible
- These technologies are able to capture, extract and process data from unstructured and semi-structured documents. They can then turn this into useable information and insights
- In order to work, IDP must follow a set of important steps. Though these occasionally differ, for the most part, these solutions must take five steps: capture, pre-processing, classification, data extraction, and data validation
- IDP can be used across a range of industries. Some of the most common include banking, accounting, insurance, HR, legal, medical, and humanities
- Intelligent Document Processing has grown in popularity in recent years and continues to do so
- This is because it has a range of benefits. For example, it is more efficient, cost-effective, accurate and reliable. Plus, it helps with compliance, and scalability