Machine learning (a form of artificial intelligence) has been around for years as a tool to help researchers, scientists, and academics process vast amounts of information to gain better insights. More recently, machine learning has been applied to very practical use cases in business settings and is changing the way employees do their jobs.

Interestingly, only 29% of companies are regularly using any form of artificial intelligence today – which means current adopters who embrace machine learning will gain a significant advantage over their competitors.

What is machine learning (ML)?

Machine learning is a form of artificial intelligence where computer systems use algorithms and statistical models to perform tasks without receiving explicit instructions. In other words, an ML system can detect patterns in a set of data and use that “knowledge” to make a recommendation, prediction, or find an answer. The reason we use the term “learning” is that the system can become “smarter” (ie more efficient or more confident) in its tasks over time by learning from mistakes made along the way.

We humans have a popular game that mimics how ML works: “20 Questions.” In this game, a player thinks of an object and then allows other players to ask yes or no questions to guess what they’re thinking. The crowd of players can ask more targeted questions as the game goes on to help improve the quality of their guesses, with the ultimate goal of correctly identifying the object.

ML works much the same way: a system is trained to ask questions about the data it’s reviewing and then will “guess” the answer. Of course, the system isn’t limited to just 20 “yes or no” questions, but can ask thousands of questions and receive all sorts of answers back. The more answers it receives, the more likely it will make a successful guess. That’s how intelligent capture (IC), a form of ML, can impact tedious workplace operations: by taking over the document classification and extraction processes, which are primary elements of IC.

How machine learning impacts document processing

There are dozens of ways to apply ML in any company, but a really practical and valuable use of ML is document processing. Most companies have tons of documents flowing into their various functions (think invoices in accounting, new employee forms in HR, customer orders in sales, loan applications in a bank, etc.).

Today, these documents get handled by workers who review the content to answer a bunch of questions: Did I receive the correct documents? If not, which documents are missing and how can I track them down? Are the documents properly filled out and signed? Can I find the information I need to make a decision?

Of course, the practical result of these processes are to get to a very concrete answers: Should I pay this invoice, can I hire this employee, should I approve this loan, when do I fulfill this customer order, and so on.

Unfortunately, humans are not great at processing lots of unstructured data at high volumes — we make errors and miss important information. We’ve seen error rates for document identification in the 5-10% range and data entry error rates up to 25-30%, which really adds up if your company is making thousands of decisions every hour. These errors can lead to significant issues – delays in processing, poor decision-making, and general inefficiencies trying to track down the correct data. Thankfully, machine learning is a great solution for processing lots of unstructured content and resolving operational issues.

Here are two simple examples of how ML can help address workplace documentation processing:

 

Document classification

ML can review a document and figure out its type; for example, a driver’s license, an invoice, a loan application, or a customer order. This is really helpful when collecting multiple types of documents from a single source, like when approving a mortgage application at a bank or reviewing medical claims at an insurance company. Letting ML work off a “checklist” of documents to automatically determine what’s been received and what’s missing provides tremendous value to organizations.

Data extraction

ML can also read through documents and pull out important information. There are various extraction techniques that ML utilizes to find the correct information, ranging from structured forms to contextual entities to natural language processing. For example, an ML system can look at an invoice and find the payee’s name, mailing address, PO number, total invoice amount, and due date — and then check it against the accompanying purchase order to ensure the amounts match. Or, in a mortgage origination process, an ML system can review a paystub to ensure that the borrower’s name matches the application and income can be verified.

There are dozens of ways ML can assist companies when handling documents, and we won’t go through all of them here. However, let’s talk about the benefits of using ML in a document-intensive business process.

Better, Faster, Cheaper

Machine learning, when properly implemented, truly is a transformational solution and provides incredible benefits to a company. Here are a few:

Higher-quality data

When ML takes the first pass at pulling information out of documents, employees don’t have to focus on the 85-90% of data entry that’s straightforward and repetitive (after all, the ML system can figure that out) and instead can function as a “second set of eyes” on data that the system can’t decipher easily (for example, a scanned page that’s really blurry or a page with chicken-scratch handwriting). When employees transition away from data entry and into reviewing exception cases, their efficiency skyrockets and error rates drop substantially (not to mention improved morale!). With this approach, accuracy rates can approach near perfect (100%).

Quicker turnaround times

ML systems process data fast – they can read an entire page of text in a few seconds – and can locate pertinent information instantaneously. Coupled with cloud computing, an ML system can digest documents and data at massive speeds, which means time wasted by humans reviewing documents is mostly eliminated. Consequently, decisions can be made much faster, which results in a better experience for customers, employees, and vendors.

Lower costs

With ML, large teams aren’t needed to process documents and front-office employees can spend less time on tedious tasks and more time with customers. Because documents are digitized from the onset, expensive courier systems and physical storage solutions are eliminated. Further, your company can grow more efficiently as the incremental cost of ML technology is negligible, especially compared to hiring staff.

Embrace machine learning for future growth

Encapture, an intelligent capture platform, can help you easily deploy machine learning across your enterprise for a variety of use cases. Utilizing intelligent automation doesn’t have to be hard, and the benefits are transformational – better data, faster decisions, more productive employees, and happier customers.

Reach out so we can help you solve your biggest problems using machine learning.

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