Over the last few years, many insurance companies across the globe have started to integrate workplace automation tools such as robotic process automation (RPA) into their day-to-day business activities and processes. At this point, if you’re the highly competitive insurance industry, this technology is one of your most trusted tools for creating a competitive advantage.
But while RPA is an extremely useful software solution that helps large-scale insurance companies run more smoothly and efficiently, saving them money in the long run, it does have its costs, some more salient than others. For starters, many RPA packages are very expensive, and implementation can sometimes take months. Deciding which specific processes to automate is another issue entirely; it’s almost as if insurance companies need an automated process just to sort out which tasks they should automate.
It's crucial to take a top-down approach, to get a bird’s-eye view of all of processes, to be able to see which will benefit from RPA. This sort of Process Discovery approach can drastically accelerate implementation; we've seen companies cut their automation deployment time by as much as 80%.
Comparing Process Discovery and Process Mining
To understand what Process Discovery does and why that matters, it’s helpful to compare it to another technology that some companies have started using in conjunction with RPA: process mining. Both process mining and Process Discovery can be used to identify a company’s processes automatically, reliably and objectively – and they can both offer key insights to help the company decide which processes to automate and in which order. But the two solution types were developed for different reasons, and they gather data very differently.
See also: 3 Keys to Success for Automation
Whereas process mining tools were created to help organizations get a better understanding of their processes more broadly, Process Discovery was created specifically to help enterprises get more benefit out of RPA and to do so with optimal speed and efficiency,. While some businesses rely on process mining to decide how to use RPA, the field of process mining was not created specifically to be used with RPA.
Additionally, process mining tools gather data on a company’s processes based on system event logs created after a user has performed an action – an approach that can sometimes limit the technology’s impact on successful automation. First of all, extracting processes from logs is a time-consuming project, typically taking between one and four months, and it requires specially trained and qualified personnel. Second, system event logs cannot always measure work processes performed on certain software, such as Citrix and legacy systems. Third, even after a process has been identified and a company has decided to automate it, the company’s employees must still create an automation workflow from scratch before robots can start performing the process – a project that can be slow and time-consuming.
In contrast, Process Discovery can gather data primarily through computer vision, which enables it to monitor a user’s activities in real time based on the information displayed on that user’s computer screen. This approach allows for a single business user to manage a company’s entire process of using Process Discovery, taking only one to three weeks from start to finish. And, because it does not require system event logs, Process Discovery can easily detect processes performed on any application – empowering a company to identify all its processes, regardless of platform.
Perhaps most importantly, each time Process Discovery identifies a process, it can automatically a fully functional automation workflow for it. Then, employees have the option of fine-tuning the workflow before assigning robots to start performing it. This capability is a key factor in Process Discovery’s tendency to slash the time required to identify and automate a work process.
Use Cases For RPA in Insurance
RPA is already being implemented in the insurance industry, with the benefits spanning from the reduction of tedious processes and general costs, to overall reduction in human error. A few specific examples include:
- Claims Processing — which involves a heavy amount of data and is very document-intensive, requiring people to collect a vast amount of information from various sources. Doing claims processing manually can be lengthy, creating issues for both customer service and operations. Process Discovery can help insurers easily find ways to automate their claims processing, while using RPA to quickly gather data from various sources to be used in centralized documents, allowing them to be processed much faster.
- Regulatory Compliance — insurance companies rely on various compliance standards that include HIPAA privacy rules, PCI standards and tax laws, which all continue to change over time. To protect business operations, these compliance standards need to be followed, but often they are hard to keep up with for employees and clients. Through the implementation of Process Discovery, insurance companies can find ways to automate areas of certain compliance processes, to better regulate them with RPA.
- Scalability — as the insurance industry only continues to become more competitive, quick and efficient, scalability is important for the success of any insurance company. RPA can be implemented to ease the experience of scaling up, allowing insurance companies to focus more on the company itself rather than the tedious day-to-day activities that take up employee time. Additionally, Process Discovery can find the daily tasks that can be automated, so that new employees can be onboarded faster and more efficiently, without getting bogged down by having to figure out which of their own processes they should automate.
