Don’t choose between automation and accuracy
By now, every organization is deeply familiar with the value of data. However, you may be surprised to learn that a significant percentage of data remains trapped in unstructured document formats (e.g. PDFs, images) and disparate systems.
One of the biggest challenges facing businesses today is finding the most reliable and efficient way to classify and extract this data without choosing between automation and accuracy.
So how did we end up here and what’s the best way forward?
Billions of pages move between businesses, customers and their partners every year. Traditionally, organizations have relied on some combination of manual operations (teams of data keyers) and legacy capture software to index and extract data from documents so that it can be used by downstream systems.
This is slow, expensive, and error-prone, and it’s costing businesses more than $60 billion each year.
While humans may be slow and error-prone (particularly if they are entering data for hours on end), they bring context to the data and have vastly superior flexibility in understanding data as it appears in different document formats.
In contrast, while software may be faster, legacy systems are limited in their processing capabilities and produce unreliable results. This requires employees to manually check transcription outputs or identify incorrect fields before the data can be used downstream.
Optical Character Recognition, or OCR, as one example, requires pristine conditions to achieve accurate results. It fails when faced with document imperfections like messy handwriting, low-quality scans or patterned backgrounds.
In addition, OCR fails to produce a reliable measure of accuracy. OCR measures accuracy at the character level, despite the fact that a Social Security Number or bank account number with one digit off might be “90% accurate” but 0% useful.
In your world, accuracy makes all the difference.
Organizations can’t afford to be wrong when it comes to high-value transactional processing. So, even with data capture or “automation” software in place, they have to throw humans at the problem to correct what the technology couldn’t do in the first place.
Achieve Accuracy & Automation with Intelligent Automation
The good news is that ever-evolving advances in Artificial Intelligence and Machine Learning are bridging the gap between human understanding and machine processing.
Intelligent automation is transforming dated approaches to document processing. With this technology, companies are enabled to unlock and parse through huge volumes of data with greater accuracy, gain new efficiencies, and drive better outcomes for their business and customers.
While legacy approaches still force organizations to choose between accuracy and automation, Hyperscience takes a fundamentally different approach, treating accuracy as paramount.
Hyperscience clients select their desired target accuracy rate (based on internal SLAs or other compliance requirements) and the machine automates against that. We’ve developed and trained our own proprietary Machine Learning models to process documents as they exist in the real world – imperfections and all. Generally, we achieve around 80% automation out-of-the-box.
What’s more, we measure accuracy at the field level. A first name or policy amount with one incorrect digit off is meaningless, so we measure it as 0% accurate. This results in an overall more robust measure of accuracy.
Not only is the data extraction engine at the core of our system stronger than alternative solutions, but our system is exceptionally good at knowing when it’s likely to be right or wrong, sending a small subset of edge cases to an organization’s data entry teams to review. We’re smart about when to involve humans. This translates to minimal manual involvement or oversight for your teams, and we continue to learn on your documents to drive lower error rates and greater automation over time.
By combining human expertise with the latest in intelligent automation, Hyperscience helps organizations unlock more accurate data with less manual work – the critical first step towards better decision-making and business outcomes.