What is Intelligent Document Processing?

This article will give an in-depth explanation of Intelligent Document processing.

Data has become the cornerstone for most businesses. It can give meaningful insights, generate leads/sales, help a company grow… But this abundance of data must be captured and interpreted. The first problem is the capturing of data from documents. Most companies are not able to effectively extract data from unstructured documents and therefore they do not use data to improve their business. The next problem is when companies do collect data, but they do not know how to extract and interpret this data. These companies waste too much time on manually extracting unstructured data, without getting the insights they desire.

Around 80% of total enterprise data is unstructured and can't be analyzed as is.

Types of data a company encounters

Structured data

Structured data is organized in a predictable, orderly pattern. Spreadsheets, sales data, website analytics, ERP’s… are all examples of structured data. If column A represents currencies, A1 might say ‘euros’, A2 might say ‘dollar’, but A3 wouldn’t say ‘water’. Structured data is usually composed of numbers or values that make it easy for Optical Character Recognition to extract, interpret and classify information.

Semi-structured data

Semi-structured data comes from documents that are generally the same but sometimes have variations. Say a bank uses a corporate KYC form. It’s a template that is structured and systematized, but it might be necessary to have different KYC templates for each industry. Sometimes templates have to be updated for new regulations, or the signature location moves from the right to the left. The data is structured, but less predictable. OCR cannot handle the variation, so humans must step in.

Unstructured data

Unstructured data can come in highly unsystematized, more unpredictable forms due to the variety of formats: email, chat, social media posts, sensor data, IoT, audio and video files. It is qualitative and not quantitative in nature. A document will not have a designated location to extract information from. Think of looking for vehicle information on a loan application form (structured) versus having to figure out vehicle information from a social media profile (unstructured). More effort goes into extracting “Mercedes SL AMG” from Bob’s Facebook post than from the loan application. By necessity, extraction, interpretation, and classification has largely been a human job, albeit a tedious one.

The problem with all this data is that around 80% of total enterprise data is unstructured and can’t be analysed as is. Processing this unstructured data is time-consuming and demotivating for employees. How can organisations transform this data to valuable, structured data without overburdening their employees?

Intelligent Document Processing (or IDP) might be the answer.

What exactly is Intelligent Document Processing?

Intelligent Document Processing or IDP, is a technology based on AI and machine learning that allows organisations to automate the data extraction from complex, unstructured documents and convert it into usable data. IDP utilises different technologies to extract, interpret, categorize, relevant data. Before it can be implemented, an IDP system must be trained on a number of different example documents. Afterwards, the system automatically extracts the relevant data. After training, when the system is not sure if the data is correct, it will demand human validation to continually improve the algorithm. The different sub-technologies used within IPD are Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), Computer Vision, Robotic Process Automation (RPA), and Intelligent Character Recognition (ICR ). IDP can be a huge time saver for businesses that still manually extract data from documents.

Because all these technologies work seamlessly together, an IDP system can learn from itself. This means that organisations can automate data extraction from complex and varying document types and emails.

What is the difference between Intelligent Document Processing and Optimal Character Recognition?

IDP is different from OCR (Optimal Character Recognition). IDP does use OCR technology, but it is larger than just that. It also incorporates so much more technologies that help the IDP system to make well-thought-out decisions. Curious to know more about OCR vs IDP? Read our blog article

What is the difference between Intelligent Document Processing and Robotic Process Automation?

IDP is also different from robotic process automation. RPA is a separate, single task that runs on a data-driven and trained model. But this model can only do this one repetitive task that it is trained on. RPA does not have the ability to understand and interpret the data like an IDP system. Want to know more details about how these technologies differ and how they can work together? Read our blog article

Benefits of Intelligent Document Processing

Save time and process documents faster

Save up to 90% time with respect to data extraction compared to manual data extraction. An employee needs about one minute for short documents and up to hours for documents with 100+ pages, while an IDP system can do both in about 30 seconds.

Improved accuracy

Up to 99.99% accurate according to document complexity and length.

Improved productivity

Because IDP-systems work (almost) entirely automatically, your employees get a lot more free time to work on other things.

Process any document

IDP transforms all physical, paper documents into digital versions that are sharable with peers. This helps the digital transformation in the organisation.

Cost efficient

Because the employees do not have to spend hours manually extracting data from documents and emails, there is no possibility for human errors. This combination can lead to a cost reduction of up to 70%.

Success stories in Intelligent Document Processing

Get inspired by these use cases within different industries. 

Automating loan applications in banking

Discover how Axa bank manages to process the same amount of loan application documents with less than 50% of the time and effort. Read case here

Insurer automates incoming communications

The automation of incoming communications at Europ Assistance is being automated using Metamaze. Lean more about it in the case study. Read case here.

Automation of incoming orders

Discover how Group Nivelles saves 70 hours per month and automates 90% of incoming orders. Read case here.

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