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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!

The New Age of Check FraudIn the ever-changing landscape of payments fraud, one constant remains: fraudsters will always find a way. Traditional check fraud continues to linger as a loss conduit for financial institutions as well as retailers. The “traditional” activities of counterfeiting, forgeries, alterations and deposit fraud are still prevalent, but a new age of check fraud has already begun.

The ability of technologically savvy fraudsters to install malware on corporate clients and retail accounts has generated a wave of distributed denial of service attacks (DDoS). This approach enables the criminals to access copies of checks online and feed downstream fraudulent activities. In fact, an Association of Financial Professionals (AFP) 2012 survey shows that checks are the payment type most vulnerable to fraud and in many cases is seeing an upward curve in activity.

A Paying Bank Centric-Model

Technologies in image processing combined with data analytics can build a strong wall of protection for the paying bank. In the past, inclearings were posted on Day 1 and available for other functions, yet fraud detection did not take place until Day 2. Prior to new Reg CC laws of next day availability, this was not as much of a concern. However, today financial institutions need to “build-up and move-up” the processes in order to stop new forms of check fraud. Examples include:

  • Route images to high speed image analysis, data analytics and duplicate detection prior to posting
  • Evaluate abnormal dollar amount behavior in conjunction with check stock validation (CSV)
  • Fine tune automated signature verification (ASV) rules for both skilled and unskilled forgeries (this can be challenging when fraudsters possess a copy of the customer signature)
  • Confirm endorsements are present using detection technologies
  • Apply various payee recognition technologies for verification and data mining

Simply put, a paying bank-centric approach enables FI’s to protect themselves as well as their customers as part of an improved risk management approach and a gain in operational efficiencies with reduced suspects. Unfortunately, we still haven’t reached a same-day, reciprocal environment with banks where “I’ve got your back if you have mine”. Hopefully the future will bring us a payer-centric model that is “semi real-time”. When that happens, the paying bank will protect the depositing bank and visa versa by notifying each other prior to posting.

Technological advancements are changing the way check fraud occurs. Banks need to step up their game by using distinctive software and IT processing to control their losses and stop fraudsters in their tracks. The days of a high loss being acceptable are over. New technology and cooperation can help provide banks the resources to prevent crime in an efficient and cost-effective manner.



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