Squeezing efficiency out of decades-old legacy systems can seem like a losing battle for many insurance carriers, where long-term contracts are the nature of the business. Process engineers, both in-house and contracted, have driven efficiency within these operations to the point of diminishing returns. After all, there is only so much that Excel macros and keyboard shortcuts can do to streamline highly manual and repetitive processes.
So it is no surprise that insurance companies, as well as other financial services institutions, took note when robotic process automation (RPA) entered the scene. By automating software application interactions, such as populating data, documenting audit trails and performing calculations, RPA promises to spare humans from performing these menial tasks while boosting accuracy, increasing efficiency and lowering the cost of operations by as much as 40%
Moreover, RPA requires minimal integration with legacy technology. By using RPA to streamline manually intensive operations, from underwriting new business to claims processing, process engineers can once again extend the life of these legacy systems. When properly applied, RPA has strong benefits. However, is RPA really living up to the hype?
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While the application of AI and robotics will eventually drive opportunities for efficiency and improved customer experience, many insurance providers are currently struggling to find value.
Most firms have struggled with deployment. Providers cannot assume RPA can be integrated into operations and deployed without intensive collaboration with the IT organization. Scaling an RPA program so a carrier can achieve real benefits requires IT process disciplines that are core to an IT organization, such as setting up infrastructure, managing security and executing testing. Without this rigor, an RPA implementation may fail to deliver the promised results, and an otherwise promising robotics program is likely to be scrapped.
The second major failure is managing an RPA program without an end-to-end view of the process using experienced process engineers. Without this perspective, companies often spend months creating bots that deliver little real efficiency because they optimize only small portions of a process without reengineering the overall process with the automation in place. This is a critical step on the road to RPA success, otherwise, RPA may not be the silver bullet providers have been expecting.
Making RPA Successful
A strong RPA program includes a cross-functional team that combines process and technology experts to reengineer, develop and integrate with the people in the process. By so doing, the team will maximize the ability to identify the best places to apply robotics. Essentially, as the old saying goes, if you fail to plan, plan to fail.
Additionally, ensuring that IT disciplines are applied will prevent unnecessary rework. Lastly – and most importantly – a cross-functional team will help implement the new processes with proper organizational change management, which will help ensure acceptance.
Using these methods, a business process as a service (BPaaS) platform was recently automated to bring greater efficiencies and a better user experience. By leveraging RPA to automate task entry and acceptance, as well as data extraction, a global provider of employee benefit programs saw a 99% improvement in quality and 78% increase in efficiency.
Interest in RPA is booming, but it is no silver bullet. RPA alone can’t solve network latency and choke points along the information chain. There is much to consider when implementing RPA, including involving cross functional teams who will bring process reengineering expertise, IT rigor and change management know-how to the program.
Currently, process mining tools are finding new utility as they integrate closely with RPA software companies. These tools allow for a more automated approach to opportunity identification and reengineering. By using artificial intelligence to assist process engineers in assessing the processes being executed on the floor, it helps automate the creation of the requirements documents, saving hours of effort and expediting the building of necessary bots.
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The next step is further development and use of artificial intelligence combined with robotics. This combination is already present for the intake processes at insurance companies. For example, using AI to improve optimal character recognition (OCR) and robotics to index and classify customer documents for proper routing within a workflow system is now becoming mature.
Soon, new RPA and AI applications will rapidly emerge with the help of knowledgeable process engineers. For example, detecting anomalies in data using AI can help find fraudulent transactions. RPA can then ensure the relevant information is efficiently presented to the case manager for a thorough and efficient evaluation.
RPA’s promise to enhance the customer and employee experience while improving the bottom line will continue to boost adoption and spur investment, but providers must be strategic to ensure they realize the potential benefits of RPA.