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Things Customers Say: RPA & OCR’s Weaknesses

By: Melissa Annecchini
Published: January 15, 2021

We take pride in helping Global 2000 companies and government agencies leverage automation to transform their outdated, manual operations. In our latest #ThingsCustomersSay installment, we’re sharing first-hand customer soundbites on why they continue to choose our Intelligent Document Processing [IDP] offering for data extraction over alternative approaches like legacy OCR tech and RPA. 

“OCR is going the way of the dinosaur” – U.S. Workers’ Compensation Provider

A new year is upon us, bringing new opportunities for ever-evolving AI and Machine Learning techniques to further uncover Optical Character Recognition [OCR] and Robotic Process Automation [RPA] as inferior, outdated solutions for document processing and data extraction.

OCR’S WEAKNESSES

“We’ve tried to implement OCR for 10 years in starts and stops…it just hasn’t worked” – Global Financial Solution Provider to Asset Managers

Let’s face it: it’s rare that documents will enter a business in perfect – let alone good – order. This makes finding a solution that can handle scan or fax distortions, messy handwriting, low DPI, and white text on black backgrounds critical for true process improvements and efficiency gains.

When facing high volumes of real-world, diverse documents, Optical Character Recognition fails to extract the complete, accurate data needed for fast and reliable processing. Even if you’re dealing with standard, quality input documents (with consistent handwriting in boxes, for example, or fields for extraction located in the same place across pages), OCR products will still create friction when you try to plug data outputs into your downstream systems or databases. 

Character recognition is just one piece of the document processing puzzle, and OCR often leaves you with a text representation of the image but not the structured information required for downstream processes. 

Additionally, OCR has low accuracy rates when performing data extraction, which often results in errors that are costly and time consuming to correct. For example, imagine a bank is using OCR to more crudely extract personal data from new account application forms. An error in the individuals’ Social Security Number – one digit incorrectly transcribed at the start of the journey – makes its way through to the final verification step. When the employee isn’t able to verify the information, they then have to chase down the original applicant (whether by phone or email), correct the mistake, and ensure it’s updated and reflected elsewhere. 

These shortcomings amount to a partial solution that requires significant set-up, ongoing template creation, and regular review and validation – not exactly the better, faster, cheaper promise of automation technology.

Fortunately, there’s a better way: Learn how one Global 2000 finance & insurance firm decreased manual work by 70% by trading OCR for IDP.

RPA’S LIMITATIONS

“Hyperscience’s high quality data extraction is exposing all kinds of shortcomings with RPA and downstream solutions that they were previously blaming on OCR.” – Federal Cabinet-level agency serving more than 5M veterans each year 

Robotic Process Automation faces its own barriers when attempting to provide a robust, long-term solution to streamline business processes and unlock operational transformation. 

RPA only works with structured data and simple, well-defined, repeatable steps needed to execute a particular business application, such as dragging and dropping files into a folder. 

Most RPA tools lack the intelligence and flexibility to handle the dynamic inputs & data types that make up an organization’s workflow (e.g. claims processing, loan origination, Know Your Customer). When confronted with unstructured document data like the kind contained in PDFs or scanned images, RPA needs complementary technologies that leverage Artificial Intelligence [AI] and Machine Learning [ML] to extract the data required for greater downstream automation. (Hint: This is where IDP comes in.) This is particularly limiting for RPA as it’s estimated that only 20% of all enterprise data is structured.

Most importantly, RPA automates processes as they exist today without accounting for whether the underlying process is flawed. Furthermore, making any future changes (like adding a new third party tool) becomes considerably more difficult with RPA, requiring people to attend the bots and track systems to ensure the more brittle processes don’t break. 

This forces enterprises to decide whether to invest significant manpower, cost and time to make changes, or remain tied to their existing approaches.

AUTOMATION WITHOUT SACRIFICING ACCURACY

“Intelligent Automation starts with good data extraction.” – Global services & technology consultant

Most business processes today begin with some kind of data input and end with a decision outcome. 

Think of a mortgage application package, the information required to open a new brokerage account, or the standard forms and supplemental materials required to take out a life insurance policy. Organizations comb through this information, pull out the relevant data, and use it to render a decision; how they do that (e.g. teams of data keyers, legacy tech, outsourcing) varies by organization and business need.

At Hyperscience, we believe that high quality data is the critical step zero of automating or streamlining any business process. Without accurate data extraction at the source, “automation” becomes meaningless, and an organization’s ability to hinder an informed, quality, timely decision is hindered. 

Automation is the answer to legacy document processing woes with the ability to reduce manual work, save time, contain costs and mitigate errors. But, OCR and RPA aren’t the input-to-outcome solution enterprises need.

So what should your automation toolkit look like?

It starts with a robust, intelligent automation solution [like Hyperscience] that leverages AI and ML to categorize and extract relevant data for further processing – despite complex document types and imperfections

“We’re turning things off that we’ve had in place for 20 years now that Hyperscience is up and running. People are really pleased with what they see.” – VP of Automation, Global BPO Provider

What makes the Hyperscience IDP offering so unique is its combination of state-of-the-art AI and proprietary human-in-the-loop technology. Our built-in Quality Assurance mechanism is constantly running in the background to ensure the machine continues to learn on the new data it’s exposed to, driving lower error rates and greater automation over time. 

“I’m getting near 100% extraction at really high accuracy. In the 5 years I’ve been here, this is the first time I haven’t heard complaints about extraction!” – Global 2000 Finance & Insurance Firm

Achieving Input-to-Outcome Automation

“Hyperscience is life changing.” – VP of Billing, Precision Medical Products

Hyperscience’s proven ability to extract data accurately and efficiently is the key to achieving the next level of business automation, data input to decision outcome. 

The next generation of the Hyperscience Platform will allow businesses to snap together horizontal, stackable blocks and workflows to build vertical solutions that automate key business processes such as claims processing or loans origination, seamlessly involving humans in the process to render a faster and more informed decision, and ultimately resulting in better outcomes for our customers and their end customers. 

Interested in learning more? Connect with a member of our team today.