Acronyms Explained: Integrating with RPA

ML, AI, RPA…the world of enterprise technology is changing quickly and it can be hard to keep up. Whether you’re a Director of Operations upgrading document processing workflows or part of your organization’s digital transformation team, we’re here to be a resource as you make sense of the evolving automation landscape and identify a solution that will create value at your organization.

In this blog post, our Head of Partnerships, Max Lien, talks about the integration power of Machine Learning and RPA and our recently-announced partnerships with UiPath and Blue Prism.

The rise of robotic process automation (RPA) and Machine Learning into the mainstream has significantly impacted the way organizations operate and set their digital transformation priorities. While RPA and Machine Learning are each valuable on their own, together they’re synergistic, enabling enterprises to reap the full benefits of business process automation and the associated time and cost savings. To understand why, it’s important to explain briefly what these technologies bring to the table.

RPA works well for processes that are rules-based and involve structured data outputs (e.g., copying data from a spreadsheet and pasting it into the correct database or dragging and dropping files into a folder). RPA solutions automate the highly repetitive tasks that hamper individual and organizational productivity, enforcing process and reducing the risks associated with manual entry.

Structured data, however, is often a miniscule portion of an enterprise’s data stack. (Research firm IDC estimates that 80% of data will be unstructured by 2025.) Today, the vast majority of information that enterprises need to conduct their business lives in documents that are inaccessible to RPA solutions, such as forms, images, and PDFs.

This is where Machine Learning comes in. As a complement to rules-based approaches, Machine Learning can perform specific tasks without using explicit instructions, relying on patterns and inference instead. These models then learn and re-train in response to the data they’re exposed to.

At HyperScience, we regularly hear from customers who have adopted RPA only to discover that a large portion of their workflows start with/involve document processing and data extraction. Our solution leverages the latest in Machine Learning to automatically classify documents and extract handwritten, cursive, and printed-typed text with greater automation and accuracy than any other solution available today (up to 95% automation and >99.5% accuracy). It also transforms that information into structured data outputs that can be used by any system for downstream processing.

By combining HyperScience with leading RPA platforms, including UiPath and Blue Prism, organizations can automatically submit documents and images living in email inboxes, desktop folders, and more into HyperScience for classification and data extraction. Once processing is complete, structured data output files can be picked up, parsed through, and routed to appropriate downstream applications.

Let’s consider one real world example involving a financial services company with over $8 billion in revenue.

Before HyperScience, the firm’s financial advisory business was manually extracting data from over one million new client onboarding documents. These documents include standard applications as well as pages of supplemental materials, many of which are handwritten. Manually indexing and transcribing these documents was slow, expensive, and error-prone, resulting in slow customer response times and suboptimal customer experience. Using HyperScience, the company can process new client forms with ease – and at scale. What’s more, by leveraging the UiPath integration, the organization automatically routes the extracted data sets into the client’s ERP system for further processing.

Interested in learning more? Register for our October 1st webinar with Doculabs on Maximizing Automation Initiatives at Your Organization.

Max is the Head of Partnerships at HyperScience. He can be reached by email or on LinkedIn.

Related: Check out this behind the scenes interview with Max and UiPath during IA Week Chicago!

Credits

Copy: Max Lien, Annie Christian

Design: James Rivas

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