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Check out our reservoir of information related to check recognition and healthcare payment technologies. We frequently update this section with the latest news, trends, and analysis of the banking and healthcare industries.

Reg CC

Compilation of resources to help you navigate Reg CC

Platform Modernization

Both the financial and healthcare industries are undergoing modernization initiatives in check payments and remittance.  See how OrboGraph is using AI, self-learning and deep learning models to drive innovation in these industries to deliver workflow automation.

Healthcare Payments

OrboAccess automates remittance and payment posting as well as enables full research and business intelligence analysis for RCM companies, clearinghouses, billers, and providers.

Check Processing

OrboAnywhere automates paper originated payments (i.e. checks, money orders, drafts) and remittances for balancing and posting while reducing risk and losses in the areas of check fraud, payment negotiability and compliance.

Operationalizing AI & Self Learning in Check

Operationalizing AI and Self Learning in Check

Neural networks (NN), CAR, LAR and ICR have had a significant positive impact in the evolution of check processing into an omnichannel capture environment.  If it wasn’t for the efficiencies of check recognition and image analysis, industry-wide adoption of image capture may have never happened because much of the labor savings was driven by amount and field processing automation.


Throughout the years, NNs have illustrated strengths and weaknesses. As an example, OrboGraph operationalized and deployed a 3rd party NN engine in 1999.  At the time, our approach to courtesy amount recognition (CAR) handwriting recognition included algorithmic programming coupled with this NN system.  For several years, this combination was the best in the market, performing at a rate of 60-75% read rate. However, this NN became outdated as OrboGraph created higher performing solutions which utilized legal amount recognition (LAR).  This foundation created a path to virtually 100% recognition.

Applying AI, Self Learning and Deep Learning to Check

On a related topic, the industry has seen a recent spike in check fraud for financial institutions which deploy legacy fraud prevention techniques. To help solve this problem, FI's can leverage image analysis and transaction analysis improvements coupled with self learning capabilities.


The underlying image analysis technologies include:

  • Check stock validation (CSV) for counterfeit detection
  • Automated signature verification (ASV) for forgery identification
  • CAR/LAR discrepancy and payee name verification (PNV) for alteration detection


In order for these image analysis technologies to be effectively deployed, self learning enables the system to adapt to the image and transactional characteristics of each account, storing these behaviors along with image snippets for real-time fraud detection capabilities.  The result is a fraud detection process which identifies a high degree of fraudulent items with minimal suspect levels.


OrboGraph continues to invest in intelligent payment automation technologies for check processing including AI, deep learning and self learning within the OrboAnywhere suite. It is our quest to drive straight-through processing for the industry. To learn more about OrboAnywhere, click the button below.

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Platform Modernization

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