In an interview with CIO Applications, Greg Council, VP of marketing and product management at Parascript, shares his insights on how Parascript has been able to utilize their expertise in machine learning and deep learning technologies to bring advancements to document-based automation.
Please give us insights into how Parascript has been steering innovation in document-based automation?
New ideas have always defined the culture at Parascript. One of our major distinguishing factors in the document automation market is that we build our own machine learning and neural networking technologies. Most other vendors would rely on Google’s TensorFlow, Microsoft’s deep learning and neural network platform, or some other 3rd party platform to support their automation processes. Since we own the underlying machine learning technologies for document automation, we have the ability to tailor solutions well beyond what other vendors can provide. We also license our technologies to other software vendors, system integrators, and service providers so that they can utilize our capabilities in their automation solutions.
In which markets do you find the most traction?
Most of our document automation clients are in the financial services industry. We are actively involved in providing them with technology that facilitates processing automation, contract automation, and also fraud prevention through analysis of documents and even signatures to verify authenticity. Other key sectors include postal operators, major corporations, and government agencies where we streamline and automate sorting of the full mail stream, parcels, and bundles.
Could you share some of the recent industry trends and their impact?
Backtracking to a few decades ago, automation was confined to moving paper-based documents to a digital archive, allowing a user to access documents much faster and systematically. In recent years, with other technologies coming to bear, document automation has taken on more complex workflows and processes. Consider an instance of loan origination.
The most striking factor is our in-house machine learning capabilities that provide a much deeper-level of remediation for a client’s problem
Can you share a case study of how Parascript helped a company overcome their document processing challenges?
One of our clients, a large bank, is quite active in the secondary loan market. They were facing challenges in managing the home mortgages. Most mortgages are originated by companies that aren’t necessarily banks and a major percentage of those loans are sold in the secondary loan market. In some cases, a loan is originated by a particular company and a few months later, it gets sold to another company that services the mortgage. This system required the bank to follow the protocol and redo all the loan documentation review and verification processes, even though the loan is already created and sanctioned. Since the bank relied on manual operations for the verification process, it became a tedious and never-ending cycle of events for the bank, urging them to seek an automation solution that could seamlessly address the problem. To solve this, Parascript created an automated document identification process capable of taking all the documents and automating the identification of documents. While doing so, it would also separate the files and send it to the appropriate staff, who can easily sift through the necessary documents and cross-verify.
How would you describe the unique value proposition of Parascript?
The most striking factor is our in-house machine learning capabilities that provide a much deeper-level of remediation for a client’s problem. The second unique factor is our take on the precision of the automation solution. While most other vendors concentrate on only functional configuration— aiding the software to identify different documents and rules of a company— Parascript spends ample time on tuning and optimizing those systems to enhance the throughput of automation. It is a time-consuming proposition, where we take samples and compare them with the system output document-wise and department-wise, but the result is an advanced breed of document automation that, in certain applications can achieve 99.5 percent output accuracy.
What are Parascript’s future endeavors?
We recently announced the release of our Cascade Classifier. At a very image-based level, classifiers are trained to recognize different documents such as proofs of income and appraisals. However, there would be cases where the classifiers will not be able to differentiate a particular application or document type from another. The only way it can be optimally tuned is by analyzing all of the output, identifying where the system has made mistakes and then making adjustments which can take literally hundreds of hours. Our cascade classifier, offering 100 percent automation of the analysis and tuning, effectively fulfills these obligations. As a result, the final output is 25 percent better than any solution in the market.
With that, we are also geared toward bringing scalability of the document automation solution, making it more accessible to small and medium-sized enterprises. Most of these small companies do not have the technology expertise or time to adequately configure and optimize a document automation system. Thus, they resort to processing methods that are sub-optimal or continue using manual workflows and processes. Our upcoming release is targeted toward addressing this problem. With our new release—an advanced machine learning capability—any mid-sized business can take our software and optimize their processes using various samples to train the software. We believe this capability is going to bring a sea change in the adoption of document automation, especially robotic process automation that increasingly needs access to document-oriented data.