Mainframe automation and AI to detect risk of fraud in healthcare insurance: 4 key considerations
In today’s digital world, the ability to reduce the risk of fraud is key for any industry, but especially so for health insurance providers and healthcare organizations. According to a recent report from Mordor Intelligence, activities related to healthcare fraud in the United States cost $68 billion annually.
As more health insurers and healthcare organizations digitally transform, many are looking for tools that will enable them to modernize their mainframes and leverage automation to reduce the cost of fraud detection, streamline processes and reduce human error.
In this blog, we will discuss four key considerations health insurance and healthcare leaders should consider as they embark on mainframe automation journeys, with a specific focus on detecting and reducing the risk of fraud by using a mainframe RPA and AI together.
Why leverage AI to detect fraud risk?
Traditional enterprise fraud detection has leveraged statistics and rules-based methods to detect fraud risk. While these methods are still relevant and should not be abandoned, with the speed at which fraud is evolving in today’s world, they are no longer enough to adequately protect the organization. Instead, organizations must combine these methods with more advanced technologies that can automatically learn and detect fraudulent activity.
AI is the answer – in today’s world, it’s a must, not an option. But AI is also not one-size-fits-all. Organizations must consider their level of AI maturity when selecting an AI tool that will work for them, considering both supervised and unsupervised methods. In most cases, a combination of these different techniques is the best approach to increase the detection of fraud.
Supervised AI methods are the easiest way to kick off an AI journey. Supervised methods leverage historical information that can be used to train new AI models, enabling them to detect more complex scenarios than traditional statistics/rule-based methods.
However, enterprises can’t assume that they are familiar with all types of fraudulent activities just because they have dealt with fraud in the past. Fraudsters change their behavior all the time. For this reason, unsupervised methods are also critical to protecting the organization. Unsupervised methods look for unusual patterns in data and can return a Fraud Risk Score, which enables teams to gauge the risk involved with an activity before it is conducted. With this information, organizations can determine a threshold at which claims must be reviewed by a person, enabling low-risk processes to run unhindered while ensuring more complex issues get the attention they require.
Why mainframe automation and RPA for fraud risk detection?
While cloud-based solutions have gained popularity, according to a recent Rocket survey, 56% of IT professionals report that the mainframe still makes up most of their IT infrastructure. The mainframe is especially critical to highly regulated industries like healthcare and insurance for a number of reasons ranging from managing security and privacy to the sheer cost of ripping and replacing IT infrastructure. However, traditional approaches to modernizing the mainframe – which are often expensive, time consuming and resource-intensive – are no longer cutting it as the pace of business continues to increase.
To combat this, leaders should look to mainframe automation and modernization solutions that do not require changes to the mainframe itself, but instead come in the form of applications that can integrate both with the mainframe and the rest of the enterprise. With these applications, new process enhancements can be delivered faster and at a lower cost.
For instance, by leveraging RPA solutions to detect the risk of fraud, it dramatically reduces the time and cost to implement new controls that help reduce this risk. Further, RPA brings a higher level of flexibility to implement new controls or improve current ones.
Why a native RPA for mainframe rather than Windows RPA solutions?
While there are plenty of RPA solutions on the market, the majority are Windows-based solutions, which can be a great tool to automate end-user tasks in Windows apps and websites but are not suitable for mainframe automation projects. Windows-based RPA solutions work best for processing low volumes of data, rather than the high volumes seen in mainframe environments. As a result, leveraging these Windows-based solutions on the mainframe often results in automations breaking at run-time, a time-consuming and expensive issue for organizations to fix.
Instead, organizations modernizing their mainframes should use a server-based RPA that is able to run bots that natively connect to the mainframe using the 3270 data stream. This approach brings the highest level of performance, scalability and reliability.
Why is important to use a single low code/no code platform?
While there are numerous ways to incorporate AI into the mainframe, as organizations embark on their mainframe automation journey, low code/no code RPA solutions will be critical to reducing integration and maintenance costs of different systems. With low code/no code solutions, the need for specialized script-writing skills is also eliminated, reducing the amount of time it takes to bring these processes to life.
By leveraging low code/no code RPA specifically designed for the mainframe, like Rocket’s Process Automaton IBM Z edition, health insurers and healthcare organizations can create robots for the mainframe that allow them to incorporate AI skills into existing and new processes in just minutes using a visual graphic interface.
Rocket Software helps customers to modernize their systems using the latest innovations of our products and expert teams. To learn more, check out our suite of Rocket Intelligent Legacy Automation tools.