Everyone talks about AI (and more appropriately machine learning) as being the silver bullet to the challenge of achieving highly efficient business processes. And on the surface, the excitement and perspectives appear appropriate. After all, machine learning has the ability to see things in data that humans might never notice and automatically perform tasks with […]
How can IDP software simultaneously push through both the complexity barrier and expand to more complex documents? Machine Learning, that’s how. Now, I won’t ever make the claim that machine learning is a magical silver bullet, but when it comes to the chore of crunching large amounts of data to identify patterns and optimal solutions, […]
Don’t call it “OCR” anymore. Intelligent Document Processing (IDP) or what analyst firm Deep Analysis calls “cognitive capture” is something well beyond the traditional approach of applying brute force OCR on documents in order to create searchable content. In fact, increasing OCR is not needed at all with more and more documents born digital. All […]
Quick, which animal is smarter? If you selected the newborn baby. you’re right! And if you selected the newborn Gazelle, you’re right! Ok, both assertions cannot possibly be correct…can they? After all. the human will ultimately be able to communicate and do things that the Gazelle could never do. But the Gazelle, as a newborn, […]
The need for a data science approach where machine learning is applied to cognitive capture starts with high quality input data. Find out why.
The automation industry is on year-3 of its infatuation with everything machine learning and ‘unparalleled accuracy’ claims, what’s changed?
No machine learning to-date works with zero training, but advances to reduce the training required for reliable results are underway. Details here.
This new IDP article covers a major automation trend necessary for the claims submission-to-payment reconciliation process.
This week is a deep-dive into the second factor, which addresses how modern IDP software can learn and improve—different solutions tackle the problem in different ways—some better than others.
Bridging the gap between healthcare today and healthcare tomorrow requires machine learning. Find out how ML solves healthcare claims processing problems.
In automation, using that most derided approach—the template—can be useful, but it has its limitations with the best and worst discussed here.
Explore if and when templates are useful in Intelligent Capture and how machine learning is used for highly variable documents or unstructured documents.
