RPA Document Processing: Everything You Need to Know

What is RPA document processing, how does it work, and how can you leverage it in your business? Check out this guide to learn all of that and more.

  • Brianna Valleskey
    Head of Marketing
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Pick any industry, and you’ll likely find large volumes of documents locked in text, PDFs, emails, and scanned documents. 

Bank statements, invoices, mortgage applications, contracts, and insurance claims all require manual intervention to read, interpret, and process. 

Armed with vast amounts of information, innovative organizations turn to robotic process automation (RPA) for faster, more accurate document processing.

So what is RPA document processing, how does it work, and how can you leverage it in your business? Let's take a look. 

What is RPA document processing? 

Pick any industry, and you’ll likely find large volumes of documents locked in text, PDFs, emails, and scanned documents.

Enterprises deal with broad swathes of data generated from several business units including sales and marketing, finance and accounting, and human resources.

Plus, they manually process documents, which:

  • Wastes time
  • Increases costs
  • Leads to unwanted errors
  • Lacks scalability

“Companies pay $6 to $8 on average to manually process a basic document. Documents that are more complicated can cost upwards of $50.” —Travis Lindemoen, Managing Director of Nexus IT Group.

Automating document processing—a slow, error-prone, and resource-intensive process—lies at the core of RPA. 

RPA software robots mimic human actions in business processes to automate simple, rule-based, high-volume, and repetitive tasks. These bots constantly run through the task until they process all the data the rule applies to.

For example, an accounts payable team receiving large volumes of digital invoices from different vendors each month will benefit from automated document processing. 

RPA bots can:

  • Open and flag emails with attached invoices
  • Key in invoices into the corporate systems
  • Find supplier IDs
  • Extract data from the invoices
  • Enter data into the accounting system 
  • Reconcile financial data
  • Flag any discrepancies for investigation
  • Generate reports
  • Process payments
  • Update customer accounts
  • Offer real-time updates

By handling all these and other tasks, RPA bots replace the cumbersome, time-consuming, manual data entry and extraction task, enabling the accounts payable team to focus on more strategic, value-added work.

How does RPA document processing work?

RPA automates document processing using a set of instructions based on “If this, then that” rules. Some suitable candidates for RPA include:

  • Opening emails and attachments
  • Logging into enterprise apps
  • Connecting to system APIs
  • Moving folders and files
  • Copying and pasting information
  • Filling in forms 
  • Reading or writing to databases
  • Extracting structured data from documents

For example, a bank can use RPA document processing for customer due diligence, such as client onboarding and identity verification. RPA bots extract relevant information from various client documents with higher accuracy, resulting in fewer costs and better customer satisfaction.

However, a critical part of document processing is reading the data manually and keying it into systems of records. 

RPA automates document processing workflows piecemeal across processes, so it’s not sufficient in and of itself. To mitigate its shortcomings, automation vendors are combining RPA with Optical Character Recognition (OCR) to: 

  • Empower bots to automate document processing workflows 
  • Extract the required insights from the documents

Let’s look at how RPA and OCR work together to power document processing. 

RPA and OCR: The perfect document processing duo

A critical part of document processing is reading the data manually and keying it into systems of records.

A report by Seagate Technology found that 68% of data available to enterprises go unleveraged. Only 32% is put to work, meaning companies aren’t maximizing the data they collect to draw better insights for decision-making. 

One of the major barriers to using the information is collecting the right data.

RPA only works with structured data and well-defined, standardized, and repeatable steps to execute business applications like copying and pasting data or dragging and dropping files into a folder.

Most RPA tools aren’t flexible or intelligent enough to handle the dynamic inputs and data types in business workflows like Know Your Customer (KYC) or claims processing. 

When confronted with unstructured data—which comprises 80% to 90% of enterprise data ‌in images, PDFs, surveys, or scanned documents—RPA needs complementary technologies (OCR, artificial intelligence, and machine learning) for greater downstream automation.

For example, in trade finance processing, OCR extracts data from trade documents (party name, bank name, account number, and swift code). RPA populates the extracted data into a core banking system or trade workflow.

Typically, RPA document processing with OCR follows these steps:

  1. Scan documents or image files into the OCR software
  2. Align letters and characters to smooth edges, remove imperfections, and extract plain text
  3. Turn remaining text to black and white only, and replace all gray shades (for better accuracy and easier text recognition)
  4. Use text and pattern recognition, feature detection and extraction to figure out what the page says
  5. Compare the text to internal dictionaries to cross-reference for context and higher accuracy
  6. Deliver a fully readable, searchable, and editable digital document
  7. Send the document to RPA systems for straight-through document processing

Integrating OCR with RPA for better workflows

OCR can’t process and analyze data on its own, nor can it adapt to different scenarios like RPA bots do. 

RPA captures and analyzes data accurately, adding immense value to OCR, which converts unstructured formats into machine-readable, searchable text. 

For example, residential and commercial real estate companies create settlements, expenses, bills of sale, and maintenance records—all of which need to be signed and made accessible once filed. 

An RPA and OCR solution can categorize, collate, and organize large volumes of information, making data entry quicker for property managers and agents. RPA takes the OCR-processed data and properly distributes it to the relevant corporate systems and applications—ERPs, CRMs, and more. 

Together, RPA and OCR create efficiency and give businesses a competitive edge.

Examples of RPA + OCR industry use cases 

An RPA and OCR solution can categorize, collate, and organize large volumes of information, making data entry quicker for property managers and agents.

Used alone, RPA and OCR each complete a piece of the automation puzzle. But they work better together, producing more impressive results. Here are some illustrative examples of their practical application in different industries.

Banking and finance

The banking industry is one of the largest users of RPA and OCR. 

Data capture simplifies banking processes faster and more efficiently. For instance, ATMs and mobile check deposits apply RPA and OCR technology to read and accurately recognize account names, numbers, signatures, and currency amounts.

OCR allows banks and financial institutions to extract data from loan and mortgage applications, pay slips, and other documents with unstructured data. 

RPA takes the extracted data for processing in the relevant loan or mortgage systems. Software bots also track transactions and raise flags if they detect fraud transaction patterns. 

Then banks use RPA + OCR technology for KYC checks to verify a customer’s identity from their national ID card or driver’s license. 

If there’s a discrepancy in the client’s information, the bots will notify a human employee for further investigation.

Real estate

Federal and state guidelines require property managers to maintain property documents pertaining to the properties, their owners and tenants, and finances. 

This leaves property managers with volumes of paperwork: property deeds, lease agreements, brokerage contracts, tax assessments, and so on. And they must file and maintain all these documents based on guidelines provided by respective authorities.

Real estate companies usually keep these documents in hard copy or scanned formats, leaving employees with manual, tedious, and repetitive data entry tasks. 

For example, a property deed document contains a date, serial number, address, and handwritten information related to the ownership or lease, among other details. Using RPA and OCR, the document processing would look like this:

  • RPA bots scan, map, and place documents into structured, semi-structured, or unstructured categories based on the file format and document quality. 
  • OCR extracts data from the classified documents, converts it into structured data, and sends them to the RPA system.
  • Bots further analyze the extracted data and notify a human worker for quick resolution in case there are discrepancies, inaccuracies, or exceptions.


Insurance companies handle tons of documents daily (proposals, new accounts, claims processing, policy renewals, etc.) which require paperwork. Manual reviews cost insurers more in terms of labor and payroll than automated reviews. 

RPA and OCR quickly automate the data extraction and analysis processes, ensuring customer data is entered into the relevant corporate systems accurately. 

Insurance workers need only scan and file the documents into the system and let RPA and OCR work their magic. In case of any discrepancies or fraudulent patterns, employees are notified to resolve the issue. 

RPA and OCR make it easier for insurance agents to pull up customer information at any time, whether a client wants to ask questions, change or renew their policies, process claims, or close their account altogether.

Leverage RPA and OCR for intelligent document processing 

When implemented strategically, RPA document processing offers improved efficiency, reduced costs, better employee and customer experiences, and a host of other bottom-line benefits.

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