5 Use Cases where Intelligent Process Automation Complements RPA

November 14, 2019 / Business, Intelligent Process Automation, Robotic Process Automation, Text Data Use Case

 

In a recent post we explained how intelligent process automation can complement robotic process automation and help companies maximize the return on investment from automation projects. In this post, we’ll clue you in on some of the top use cases for combining RPA and IPA solutions. 

As discussed in the previous post, robotic process automation is great at automating deterministic, repetitive tasks that involve structured data, such as data in a spreadsheet or database. However, it needs help dealing with unstructured data, including videos, images, audio, PDFs, Word documents, emails and more. 

Intelligent process automation takes advantage of technologies such as optical character recognition (OCR), natural language processing (NLP), machine learning and deep learning techniques to enable it to understand and process unstructured data, which typically accounts for about 80% of all data in an enterprise. When used together, IPA can complement RPA by dealing with the unstructured data while RPA handles that structured data, enabling the organization to apply automation technology to a far greater number of tasks. Here are five common use cases that illustrate the point. 

1. Corporate email inbox 

Most companies have a central inbox that receives lots of emails from customers, contractors, suppliers and the like, often with attachments. You can use RPA to detect when a new email arrives with an attachment, then automatically route the email to an intelligent automation tool. The IPA tool can then extract the attachment and “read” it, using OCR and NLP. It can also extract relevant unstructured content such as payment terms, invoice numbers, contractual language and so on. The tool can then normalize the data in an appropriate format and send it to a downstream platform, such as a customer relationship management (CRM) or enterprise resource planning (ERP) tool. 

2. Contract renewals 

Poor contract management can cost companies a shocking 9% of their annual revenue, according to the IACCM. Businesses might not realize they are owed credits, overlook renewal dates on contracts that automatically renew, or even fail to send invoices. RPA platforms can automate processes to help deal with these issues, but may be limited due to the variability of language. For instance, provisions and clauses across contracts may be worded differently but mean similar things. This is where IPA plays a powerful role: its ability to understand context through NLP techniques can help normalize this information so that your RPA system can then alert the right person and minimize financial waste. 

3. Invoice automation 

For invoice processing, RPA can automate data input, reconciliation error correction and some decision-making. But the challenge is dealing with the many formats different vendors use for their invoices. That’s where IPA can contribute—by using NLP and other machine learning techniques to understand and pull out necessary data from the invoices, normalize it to a structured format, then send it back to the RPA platform for automated data input, error handling and so on.  

4. Financial document analysis 

Financial firms need to compile lots of data for monthly and quarterly reports. RPA can aid in the process by automating data collection from various structured sources. But introduce unstructured PDF documents into the fold and RPA hits its limit; now you need OCR and NLP capabilities of an IPA solution to pull out relevant information and convert it into a structured format that the RPA tool can deal with. (Take a deeper dive into this financial services IPA use case here.) 

5. Insurance claims

Insurance companies can automate some aspects of the claims process with RPA platforms, such as inputting data from structured sources and ensuring all required fields are filled out. But insurance claims often include unstructured data, including photos showing auto damage, PDFs of scanned drivers’ licenses, or perhaps images such as CT scans for a healthcare insurance claim. An IPA platform can be used to extract relevant information from these sources, once again adding value to the RPA tool. 

To learn more about how IPA helps automate processes that include unstructured content, download this free white paper from the Everest Group, “Unstructured Data Process Automation.” 

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