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.
Key factors driving IDP adoption involve data science and specifically the confidence score that is arguably the most important factor involved with any decision to adopt IDP.
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.
Modern IDP software enables the automation of processes due to key factors including the ability to work in suboptimal conditions discussed here.
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.
How to configure your Intelligent Document Processing (IDP) software so it does what you want it to by leveraging truly intelligent capture.
Is there such a thing as machine learning that does NOT require training on sample data? The answer is “sort of” – find out why here.
Every organization with complex processes can benefit from automation by automating manual processes to reduce effort, but how do you do that for core processes?