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 […]
Within practically any industry, key processes hinge upon access to information and data stored within documents and it is no less in healthcare. Even with significant advancements with EHR/EMR and interoperability, use of documents continues to flourish due to a number of factors including that documents are easy to use, and changing from documents to […]
Most organizations presume that the foundation of document automation relies upon optical character recognition (OCR). This is largely due to the fact that most documents are text-based and therefore, the primary methods available for automation tasks such as document classification and data entry require OCR (among other techniques). But the perspective that document automation is […]
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, […]
Catch Me If You Can – Today, AI-powered software can mimic human investigators, detect check fraud in milliseconds and stop the scam.
Find out how cognitive capture can help eliminate the heavy toll of fraud and human error that impacts health insurers and patients.
The need for a data science approach where machine learning is applied to cognitive capture starts with high quality input data. Find out why.
Here are some guideposts that are useful in evaluating the authenticity of an AI capture product – whether it’s really cognitive capture.
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.
Check fraud is now rampant among millennials when checks remain a huge part of the payments landscape – the case for investing in check fraud detection software.
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.
- AP/AR Automation
- Best Practices
- Claims and Insurance
- Digital Transformation
- Document Classification
- Fraud Prevention
- Handwriting Recognition
- Information Governance
- Intelligent Automation
- Intelligent Capture Stack
- Machine Learning
- Mortgage Document Automation
- Payment Automation
- Post and Mail
- Service Providers
- Signature Verification
- State/Local Government
- straight through processing