3 ways AI is revolutionizing data capture

From Siri and Alexa to customer-service chatbots and stock-trading Forex robots, artificial intelligence (AI) has fundamentally changed many aspects of the way we work.

Data capture is no exception to the rule.

Automated data capture technology already increases workplace efficiency and decreases business costs—but “intelligent” capture is even more powerful, leveraging the power of AI and robotic process automation (RPA) to bring additional benefits to enterprises. Imagine feeding a batch of different invoices to a scanner, stepping away, and letting a computer file and prepare them, so you only deal with exceptions before paying the bills.

This frees employees to focus on higher-value tasks, instead of processing documents and invoices manually.

Going further, truly intelligent capture software doesn’t require templates, keywords, exact definitions, taxonomies, or indexing to get the job done. It extracts the right information and makes sense of a wide variety of documents on its own, regardless of size, format, language, or symbols used.

Here’s a deeper look at how AI and RPA take automated data capture to the next level.

3 ways AI is changing data capture

With intelligent capture software, you teach the AI-driven “engine” by example, exactly like, say, an intern—how to perform a data entry task. And like a motivated intern, it will quickly pick up on contextual information and learn to interpret patterns and features in different document types (i.e., invoices versus transcripts).

Moreover, it validates data against existing systems, providing an additional layer of protection that employees can’t duplicate without tedious manual lookups.

Intelligent data capture has changed the game for three major tasks: classification, extraction, and validation. Let’s take a deeper dive into each one to see how you can optimize these tasks:

1. Classification

With classification, also known as document sorting, the software learns to recognize different types of documents after being given a few variations and examples. Just as a human can read through some sample documents and intuitively understand similarities and differences, it doesn’t need to see every single version of a contract or check request to recognize it.

The machine-learning engine cuts down on the rules that it needs to apply, resulting in a high level of confidence in document classification with minimal manual effort.

2. Extraction

AI has done wonders for data extraction in semi-structured and unstructured documents—including handwritten forms. Take, for instance, invoice number identification, which usually involves building out complex templates and providing keyword tags and pairings around particular fields and labels.

An intern can look at a document and immediately locate where invoice numbers are, no matter what the form looks like. Now, software can do that, too, without the need for programming. The machine-learning engine trains itself to understand context, such as what an invoice number is not and what should (or shouldn’t) be around the number, so there’s a high degree of accuracy in the extraction.

Additionally, extracting data from complex stacked tables with lines that don’t match up (i.e., transcripts), is now a breeze. Mature AI software learns how to understand patterns and formatting, looks for different types of information, and identifies key data elements without the need for someone to rope and band the information. Only the exceptions would require human intervention.

3.Validation

Artificial intelligence is a boon for tasks that go beyond the “scrape-the-page” approach, such as an advanced search capability that validates extracted data from a document with existing information in another system. It can even match a line item in an invoice with purchase information stored in another system.

AI-driven search also allows for multi-way search, which means it can use multiple pieces of information (i.e., quantity, price, description, and amount) to match an exact item in the back-end system. And even if things aren’t precise matches—say, an abbreviation is used in the description of the invoice, but not the back-end system—the software can deduce they are the same item.

Validating precise matches once required the efforts of a patient data-entry clerk. But by minimizing this type of work, employees can now focus on more strategic tasks, further driving efficiency and minimizing business costs.

Working in tandem: intelligent capture and robotic process automation

The enterprise RPA market is booming. So far, it’s delivering on its promise of automating complex, rule-based processes, with Forrester projecting an overall market—of which document capture is just a slice—worth $2.9 billion by 2021, up from a mere $250 million in 2016.

That’s 10x growth in five years.

Unfortunately, RPA’s processes fail when there’s too much variance—and with documents, sometimes you have nothing but variance. For instance, if two documents have the same type of information, but are not in the same exact template, optical character reader (OCR) errors occur.

Enter machine learning. Combined with standard RPA concepts, the AI engine creates a dynamic variance network, or a way for software to look at everything in relation to everything else on a document. By calculating vectors between all words on the document and in the target fields, it overcomes OCR errors during the extraction process by allowing the data to move around.

On top of that, the software monitors the activity of the user and will make corrections based on that behavior without human intervention.content services

In other words, the system gets smarter on its own.

The real value of intelligent data capture

In addition to the obvious benefits, using intelligent data capture software eliminates guesswork on the setup and programming side as well. But it’s important to note that the goal of AI-driven data capture isn’t to replace humans, but to drive as much automation as possible with machines that can intelligently carry out tasks. Ultimately, employees are freed from being bogged down by mundane tasks, and can take on high-value tasks that require a human mind to do well.

One thing is certain: successful companies aren’t built in a templated world. Information and documents continually change, and they must learn and adapt—ideally, with technology that does the same.

Jaclyn Inglis

Jaclyn Inglis

Over the last few years working at Hyland, creator of OnBase, Jaclyn has definitely started to drink the Kool Aid – day and night enthusiastically discussing the wonderful benefits of OnBase with fellow Hylanders, family, friends, and even complete strangers. Her graduation from the University of Rochester with a major in economics, minor in film studies and concentration in neurological science only goes to show how vast her interests are. With that in mind, it is no surprise she truly enjoys working to market OnBase across an equally vast number of industries – some even mirroring her academic interests (financial services, arts and entertainment and healthcare/sciences) – as a member of the Product Marketing team at Hyland.

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