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AI Innovation

Both the financial and healthcare industries are undergoing an AI evolution. Review our vision for Artificial Neural Networks (ANN) and deep learning targeting these industries.

AI-Based Check Automation

Check recognition & fraud detection are the most important components in today's check processing and omni-channel capture. Learn how OrboAnywhere using OrbNet AI technology reduces costs and mitigates risk.

AI-Based Healthcare Electronification

OrboAccess, powered by OrbNet AI, provides electronification to remits and payments, enabling RCM companies to automate posting, improve research, and deliver business intelligence

About Us

Celebrating 25 years of innovation, OrboGraph has transformed into an AI company delivering targeted automation solutions to the banking and healthcare industries.


From news and events to case studies, trends, and videos, this section provides a range of information resources for payment automation in the banking and healthcare industries.


OrboGraph produces four blog series on a weekly basis covering topics from check processing, fraud prevention, AI technologies, RCM, and healthcare electronification. Select one the blog to the right. We hope you enjoy!

Capabilities of Teller Image CaptureThere are white papers galore written by the industry’s best teller image capture providers which explain the business case behind teller image capture. It’s encouraging to the market that this workflow is catching on in both large financial institutions and community banks. In fact, based on a recent flurry of large banks making decisions to move to teller image capture, the numbers for this market segment should reach nearly 60% per Celent.

One of the most challenging considerations of a major implementation project like teller image capture deals with the installation process and trade-off focusing on major “strategic” features versus the overhead of activating less important “capabilities”. In other words, sometimes the time it takes to fully activate all of the features and fully optimize an application, practically makes it too expensive to even use them. Crazy as it sounds, it’s the truth. Take check (CAR/LAR) recognition as an example. These 11 capabilities around this very critical module are seldom activated in a teller image capture environment. They include:

  1. Activate enhanced reporting for each sub-function in the workflow including throughput of teller functions, recognition results by document
  2. Implement new scanner hardware control technologies as equipment ages to minimize errors
  3. Track rejects and exceptions to build programs to eliminate them
  4. Reduce teller keystrokes and maximize efficiency by implementing a recognition tuning process (there is a big difference between 80 and 95%)
  5. Reduce errors in larger transactions (customer vs. employee) via misread tuning process
  6. Optimize staffing models with volume trending and analysis
  7. Minimize manual monitoring with SLA’s, automated alerting and managed recognition processes
  8. Reduce on-us check fraud in cash checks and split deposits with image analysis and data analytics via the teller line
  9. Reduce deposit fraud on transit checks with image analysis for deposits with endorsement detection
  10. Leverage the images for revenue opportunities and check image data mining
  11. Eliminate all paper possible with a universal document approach

The culmination of these features provides tremendous financial, operational value along with improving both the teller and customer user experience of using teller image capture. However, due to the overall complexity of implementing the full solution, you won’t get them unless you ask!

Check out our Advanced Execution Concepts in Branch and Teller Capture white paper to find out more.

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