What Is Intelligent Document Processing and How Is It Different From RPA?

Difference between IDP and RPA

Intelligent document processing (at the most basic level) is the analysis of digital documents. Intelligent document automation can even categorize and classify documents based on their content in order to make the information easier to access. Robotic process automation (RPA), on the other hand, is the operation of a digital workforce that completes a series of rules-based tasks. 

IDP and RPA can be combined to create a powerful digital workforce capable of processing documents made up of unstructured data, based on pre-preprogrammed rule sets. Then, an RPA tool can use that information to update records and databases located in other systems, such as accounting applications, or even a simple spreadsheet.

How does it work?

By using data extraction programming like natural language processing (NLP), optical character recognition (OCR), and machine learning (ML), IDP scans and collects information from a feed of digital documents. The data can then be run through a workflow by what's known as a bot to parse out the pertinent information, without tying a team of employees to their desk reading documents all day.

Intelligent document processing use cases

Organizations and teams that have to read a ton of digital documents have an excellent use case for automated intelligent document processing on their hands. Though this type of work deals in paragraphs instead of spreadsheet cells, if the work requires repeatable data extraction (as it so often does), then the entire workflow can be programmed into an automation program that runs the process from start to finish, until it's time to get a human involved for some critical thinking.

Below are a few examples of how intelligent document processing can capture that kind of data and turn it into insights for your organization.

Healthcare

Digitized medical records and billing statements can be “read” by IDP programs, and automation technology can then take insights pulled from those documents, and import them into billing dashboards and patient records. IDP is also used to scan explanations of benefits (EOBs) to determine coverage without human involvement.

Insurance

Tracking and analyzing documents is key to making accurate claims and returns. Automation can complete insurance claims workflows, using IDP to scan PDFs for information, extract critical pieces of information, and export those into emails, programs, and even other documents for human review.

Legal

Paralegal work that involves reviewing long case documents and parsing out the pertinent information is an excellent candidate for automation and IDP. Digitized court documents and other unstructured data, including emails, can be scanned automatically by these programs, and then relevant information can be sent in emails and case management programs.

Finance

Use cases for IDP in finance include the ability to process invoices automatically, review and process financial statements and purchase orders, and process human resources related financial documents, such as reimbursement request forms.