Intelligent document processing (IDP) is all about making use of data science tools such as AI, machine learning algorithms and natural processing language (sentiment analysis, feature-based tagging) in each step of the data processing. Combining those tools on a single platform is digitalizing the way organizations process data. By integrating the right digital tools in their organization, they can reduce costs and save countless hours of manual work.
As per a report from ThinkAutomation, the digital automation market is currently worth $6.76 billion and will increase up to $12.61 billion by 2023.
We have put together a full guide to explain what intelligent document processing is about and how companies can leverage it to scale up their business.
What is intelligent document processing?
In simple terms, the main purpose of IDP (also known as intelligent document automation) is to minimize human intervention with technology. Also known as document assembly, it helps to extract data from different sources and layouts.
Wikipedia defines document processing automation as the design of systems and workflows that assist in the creation of electronic documents.
"Automatically collecting required data from different types of documents, approving its validity, and make use of the extracted data adding relevant features and increasing its value"- definition by AI Multiple, May 2020
As per the latest study by Fact.MR, the global market for business workflow automation is likely to experience a strong growth. Technology-based solution is estimated to exceed US$ 2,100 million revenue by the end of 2026.
By 2026 end, the global market for business workflow automation is projected to bring in US$ 5,247.2 million revenue.
North America is expected to dominate the global business workflow automation market throughout the forecast period. Document processing automation is gaining attention globally as it provides disruptive solutions for data extraction.
How does intelligent document processing work?
Data extraction is the process of converting unstructured data into structured data and plays a big role in document processing automation. A report published on Forbes in 2019 stated that 95% of businesses have to manage unstructured data on a regular basis.
The extraction of data depends 3 types of documents:
- Unstructured data is data which does not have a pre-defined structure and cannot be read by computers. Examples of unstructured data include books, journals, medical records, or text files.
- Semi-structured data is a type of unstructured data that cannot be organized. Digital photographs, date stamps, images and invoices are considered semi-structured.
- Structured data is information that has been transformed into a well-defined data model.
The 7 steps of document processing automation
Handling document processing manually is prone to errors and time-consuming. By switching to automated tools, companies can generate more documents automatically within seconds. We have highlighted the steps of document processing automation below which requires zero human intervention:
Step 1: Data ingestion
Data ingestion is the first step to kickstart the process which is moving or importing data from different sources (emails, PDFs, MS Excel) to a single destination. The data is moved so that it can be stored and analyzed.
Step 2: Data capture
The process of extracting information from a document and converting it into data readable by a computer is known as data capture. Data capture is usually done by optical character recognition (OCR) with machine learning algorithms and deep learning AI technology for receipts, images or books.
Step 3: Data classification
It is the process of organizing data into different categories that make it easy to find and retrieve based on the sensitivity of those documents. For example, in an email parsing tool like Parseur, there are specific templates for different use cases such as real estate, food ordering or Google alerts.
Step 4: Data extraction
Through this process, specific information is retrieved from the documents. For instance, on a purchase order, data such as the customer details, order number, total price and quantity are extracted.
Step 5: Data validation
This step ensures the accuracy and quality of the extracted data. The end results won’t be correct if the parsed data aren’t accurate. This is done through several logical checks with zero human intervention.
Step 6: Data transformation
Once the data accuracy has been checked, it is time to transform the raw data into a different format that is usable, that is into structured data (the end-results).
Step 7: Data export
The last step is downloading, sending or exporting the extracted data to any other application of your choice with automated workflows.
Benefits of using intelligent document processing in your business
IDP can be applied to any industry and different use cases such as finance, real estate and the food industry among others. The benefits in implementing this type of workflow automation in your company are as follows:
Cost savings in time and resources
As per Mckinsey’s report, when it comes to business processes, 60% of occupations could save 30% of their time with automation. There is a huge amount of time and resources that could be saved by automating repetitive tasks. 90% of employees are being burdened with boring and repetitive tasks which could be easily automated - Key demand statistics, ThinkAutomation.
A benchmark made at Parseur in September 2022, concluded that on average a customer of Parseur document processing tool saves about 142 hours of manual data entry and about $5417 every month.
—Parseur statistics, September 2022
Zero human errors
Manually going through hundreds of documents on a daily basis is prone to human errors. Through document processing automation, you can reduce those errors to almost zero. The robots and machine algorithms are trained each and every time to perfect the results.
Backup of data
It goes without saying that using automated tools will backup your data automatically. Document processing tools are usually cloud-based applications where your data is stored safely and you can access them anytime and anywhere.
Pre-trained models and ready-made templates
There are automated solutions that provide ready-made templates for different industries. For example, Parseur supports a lot of real estate platforms where your documents are extracted and processed automatically (contact details, property information, lead source).
Businesses can focus on other core aspects such as customer experience and thus increasing productivity & sales. Implementing automation in the office generates 30-200% ROI in the first year, mainly in labour savings Since staff no longer have to work on time-consuming tasks, they can better utilize their creative time for the company.
Features of an automated document processing tool
When choosing an automated document processing tool for your business, it is important to understand what type of features your company will require. We have highlighted the main common features below:
User-friendly and seamless workflows
You might want to take up an automated tool which is easy to use, especially if you’re not tech savvy. A no-code tool with zero coding knowledge where you can have seamless workflows between different processes would do great!
Integration with other applications
Integrating the document processing tool with any application of your choice will contribute to time-saving as well. For instance, if you are using the tool for lead generation, it is essential that you can connect to other applications such as Mailchimp or Intercom so that data is sent automatically across.
Types of intelligent document processing solutions
With the advent of big data and technology, the extraction of data can be done in many different ways, depending on how many documents need to be processed or in which structure.
AI builder by Power Automate
The AI builder is the new Microsoft automated tool that allows you to add artificial intelligence in your applications and integrations. With a simple point and click experience, you can build different AI models which can be tailor-made for your organization.
Optical character recognition (OCR) is important for machines to read text from different images. Some companies have to process bulk receipts and this is where having a tool that focuses on OCR helps.
Parseur as an intelligent document processing software
Parseur is a document processing tool that extracts data from emails and and PDFs. With an document parsing tool like Parseur, you can save countless hours of manual work and have an automated workflow process in place.
Parseur's OCR software uses machine learning algorithms to identify the text as well as Zonal OCR and Dynamic OCR to process documents into structured data. Each time you create a template, the machine gets better at extracting accurate data. The next time you send a similar document to your Parseur mailbox, it will be processed automatically.
Parseur can also be integrated with thousands of applications such as Zapier, Power Automate and Integromat.
By embedding document processing automation within your organization, you will enable end-to-end automatic business processes. The introduction of document processing automation has numerous advantages that help to streamline business operations and achieve quicker results.