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Machine learning and AI play a pivotal role in uncovering new business revenue opportunities and delivering efficiencies to the banking and healthcare sectors. In both industries, there is tremendous potential to apply machine learning to data and images, which are generated from a wide range of clinical, administrative, commercial, and financial sources. These industry segments are looking for solutions that can reduce manual labor, mitigate risk, and leverage data for analysis.
OrboGraph delivers solutions to the financial and healthcare industries looking to automate posting, reduce manual processing, reduce risk, and leverage data for analysis. Their core competency in image processing and image analysis allows them to bring innovative solutions that utilize deep learning, convolutional neural networks, and recurrent neural networks considered a subclass of machine learning. In an interview with CIO Applications, Joe Gregory, VP, Marketing of OrboGraph shares the company’s capabilities and how they innovate the banking and healthcare industries with their solutions.
Could you give us a brief overview of your company?
OrboGraph delivers solutions to the financial and healthcare industries looking to automate posting, reduce manual processing, mitigate risk and leverage data for analysis. OrboGraph automates these processes with intelligent recognition technologies so payment information can be used in downstream systems. OrboGraph continues to expand its capabilities in payments automation and fraud detection by building new artificial intelligence, machine learning, self-learning, artificial neural networks, and deep-learning models that feed application layers.
We play a pivotal role in today’s payments and banking landscape. Over 4,000 financial institutions and corporations utilize our solutions across their enterprises and within service bureaus to automate financial processes like deposit processing of paper-originated negotiable items, i.e. checks, money orders, and preauthorized drafts.
OrboGraph has made notable contributions to the healthcare sector as well and soon to be processing thousands of healthcare providers, physician groups, hospitals via our partnerships with service bureaus, revenue cycle companies, billers, clearinghouses, and financial institutions. These companies rely on the OrboAccess, cloud-based platform to streamline the revenue cycle by improving healthcare payment and remittance processing. We convert PDF and paper-based EOBs/EOPs, correspondence letters and patient payments into electronic output to automate cash posting of receivables into patient management systems and hospital information systems.
What are some of the recent trends that you expect to have an impact on the Machine Learning space this year, and how is OrboGraph planning to leverage these trends and also evolve?
We have a committed and dedicated content and market research team. This team closely monitors the financial and healthcare industries to focus our product development, but also to create an informative blog series called Modernizing RCM with AI and Modernizing Omnichannel Check Fraud Detection.
As we analyze the markets, several trends were identified, which have tremendous potential for improvement. First, our strategy in healthcare is to electronify EOBs/EOPs, checks, and correspondence letters via our AI/ML/ Deep Learning platform.. . This dataset can feed business intelligence (BI) tools as well as machine learning systems to identify trends and predict outcomes based on that data.
OrboGraph continues to expand its capabilities in payments automation and fraud detection by building new artificial intelligence, machine learning, self-learning, artificial neural networks, and deep learning models that feed these application layers
In the banking industry, machine learning has seen tremendous acceptance for transactional analysis. These systems are applied for fraud detection and moving into compliance, including AML/BSA. However, there are limitations in the prediction and identification of fraud without image analysis. The OrboAnywhere platform will perform data extraction from check images, along with image analysis for detection of counterfeit checks, forged checks and altered checks. These image analyzer scores are then populated into downstream machine learning systems, which merge our data with the transactional analysis trends. The result is an optimized system using both machine learning and image analysis scores.
What are some of the challenges CIOs face while looking for ML-based solutions, and how is OrboGraph effectively addressing these issues?
One of the main challenges is identifying how to deploy systems, which deliver on business case expectations. There is a trade-off in complexity as well. Does the organization desire a short-term win which is easy to install, or do they prefer an enterprise strategy and plan a multi-year deployment? A highly sophisticated solution across multiple silos will be much more complicated to deploy.
Our AI and machine learning solutions are delivering a strong business justification, focusing targeted payments. By doing this, we can deliver high value for a system that is easier to deploy. Additionally, delivering downstream systems with better data is accomplished.
Could you please cite one or two case studies on how you have enabled clients to overcome hurdles and attain desired outcomes with your innovative array of solutions?
We have successfully deployed our EOB/ EOP conversion technologies enabling electronification for multiple claims clearinghouses in the healthcare sector. These clearinghouses are now able to offer a fully featured, turnkey solution for both the outgoing and incoming claims, feeding data into downstream systems like HIS, PM, and contract management.
In the area of check processing, we recently installed our new AI-based, check recognition solution at one of the top service bureaus in the industry. We are targeting 99 percent recognition rates with 99.5 percent accuracy levels. We are currently optimizing system performance and anticipate exceeding these targets.
What are the strategies that you employ to thwart the market competition, and according to you, what are the differentiating factors of OrboGraph that give it a competitive edge?
In the payments and fraud prevention or detection space, there are many companies with initiatives of using machine learning for data analysis. We are one of few who are focusing on AI, Machine Learning and Deep Learning for image recognition and object detection in this space.
Major players in both the healthcare and financial industries are looking toward platform modernization as a way to help achieve full electronification, digital transformation, and enable straight-through processing as a means to reduce costs and eliminate manual intervention. By delivering 99+ percent automation rates with higher levels of accuracy, we are positioned to fill these needs for these major industries.
What does the future hold for your organization?
We have witnessed significant growth over the past three years in both our OrboAnywhere and OrboAccess solutions. We are looking to expand market penetration rates and potentially diversify our product portfolio as a means to continue growth for the company. OrboGraph will be introducing the OrbNet AI Innovation Lab soon. A primary goal for the innovation lab is to formalize a process where Artificial Neural Network (ANN)-based products are developed with faster time to market with optimal performance levels.
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