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.
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.
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.
- 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