Automation with ML: Are we there yet?

Magic of Machine Learning Applied to Automation: Are We There Yet?

The automation industry is on year-3 of its infatuation with everything machine learning and ‘unparalleled accuracy’ claims, what’s changed?

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Is there Machine Learning that does NOT require training?

IDP: Machine Learning and the Training Required for Reliable Results

No machine learning to-date works with zero training, but advances to reduce the training required for reliable results are underway. Details here.

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IDP Key Factors - minimizing human intervention

Dramatically Minimize Human Intervention – Key Factors Driving IDP

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.

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context in IDP with Parascript

Understanding Complex Data via Context in Key Factors Driving New IDP

This new IDP article covers a major automation trend necessary for the claims submission-to-payment reconciliation process.

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Check fraud is now rampant among Millennials

“Check fraud is now rampant among Millennials!” Who would have thought?

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.

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Machine Learning: Ability to learn and improve with Parascript

Key Factors Significantly Impacting IDP Adoption – Factor 2: Ability to Learn and Improve

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.

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Key Factors Driving Intelligent Document Processing

Key Factors Driving New “Intelligent Document Processing”

Modern IDP software enables the automation of processes due to key factors including the ability to work in suboptimal conditions discussed here.

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Insurtech - 2021 Trends for Healthcare Insurance: Interoperability and Data Mining

2021 Trends for Healthcare Insurance: Interoperability and Data Mining

2021 trends for healthcare insurance explored here including how to mine the wealth of medical data and interoperability using Intelligent Document Processing.

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Mortgage Lending Automation - Parascript

Investment in “Smart” OCR Is a Must-do for Mortgage Lenders in 2021

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.

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Machine learning software solves claims processing challenges

New Machine Learning Software Solves the Most Difficult Healthcare Claims Processing Problems

Bridging the gap between healthcare today and healthcare tomorrow requires machine learning. Find out how ML solves healthcare claims processing problems.

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Automation with Parascript

Automation: Why Templates Are the Best and Worst Solution

In automation, using that most derided approach—the template—can be useful, but it has its limitations with the best and worst discussed here.

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Intelligent Document Processing with Parascript

Templates vs. the Alternative. Which is better for Intelligent Capture?

Explore if and when templates are useful in Intelligent Capture and how machine learning is used for highly variable documents or unstructured documents.

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Intelligent Capture and Coffee - learn with Parascript

Intelligent Capture & Coffee: Answers to What Everyone Asked

Enterprises created a work-from-anywhere environment and events went exclusively online so when we attended intelligent capture webinars, many of us had the same questions, answered here.

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Predicting Data Types for IDP Software

Predicting Data Types for Intelligent Document Processing Software

How to configure your Intelligent Document Processing (IDP) software so it does what you want it to by leveraging truly intelligent capture.

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Machine learning without training

Is There Machine Learning That Does NOT Require Training?

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.

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