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.
2021 trends for healthcare insurance explored here including how to mine the wealth of medical data and interoperability using Intelligent Document Processing.
Loan file review is critical in the loan process with the automation of file sorting, extracting and verifying of data taking on new urgency. Discover why here.
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.
There’s no easy button for intelligent document processing, but we are getting closer with the use of deep learning. Find out how.
The age of software-defined Intelligent Document Processing is here. Find out what this means for your organization.
Rethinking operations during this black swan event means the adoption of new technologies and services to keep businesses running. Find out how.
Since the document is serving as a Digital Transformation (DX) data container, the document might not die with DX; this may be its resurgence. Find out why.