Is Robotic Process Automation The Future When It Comes To The Insurance Industry?
Nowadays, insurance companies can either burden their workforce with repetitive and operational tasks or channelize their bandwidths towards more value-added tasks. What a company chooses, determines the pace of its growth.
Processes such as underwriting, claims to process, and policy servicing, bring along with them a plethora of important but mundane and repetitive work, affecting the overall organization’s efficiency. This is where the need to automate systems and manual processes arise.
Robotic process automation (RPA), with the use of software bots to handle routine processes and time-consuming data entry work, is an objective solution for any organization to drive customer-centric strategies and scale up operations.
Why is RPA a Good Fit For the Insurance Sector?
The insurance industry is replete with back-office processes, which are operational, high-volume, and repetitive in nature. Organizations can count on RPA to shoulder these time-consuming processes and free up their workforce to focus on customer service, thereby reducing costs and turnaround time while increasing customer satisfaction.
RPA also helps automate sub-processes within major insurance processes. Moving of data from spreadsheets to core systems, pulling out data from invoices into a core system, scraping of information from the internet and websites, are among some of these sub-processes RPA helps automate.
Further, RPA streamlines the end-to-end process lifecycle through the integration of front-end technologies with existing back-office processes.
More importantly, RPA done alone is not enough. Like any other well-orchestrated process in the organization, RPA also needs context. Automation will do the job faster. Automation with context will do the job more effectively and fast.
Effective RPA Implementation is a Need for the Insurance Sector
Let’s understand how RPA implementation can make a significant difference across critical insurance processes:
New Business and Underwriting: Gathering data from different sources to accurately assess the risk associated with any insurance policy makes underwriting an appropriate area for RPA.
Claims Registration and Processing: The claims process is document and data-intensive and depends on the collection of information from multiple sources. This makes the process lengthy and time-consuming, affecting customer service and competitive advantage.
RPA can help insurers automatically notify those responsible for loss adjustment, hand over tasks to claims handlers, and integrate all the disparate claim information. This helps speed up the process, improve customer experience, and increase ROI.
Policy Administration and Servicing: Policy administration brings together all the functions of an insurance provider, right from quoting rating, and underwriting to the distribution of customer services. Policy administration systems are expensive, high-maintenance, and cannot scale quickly enough to meet the growing customer needs or support business growth.
RPA can help complete the task in one-third of the time taken.
Regulatory Compliance: The insurance sector is guided by strict regulations related to documentation and audit trails. The presence of tedious and error-prone processes can increase the risk of a regulatory breach exponentially.
RPA comes in handy in such situations by replacing the need for devoted staff to go through operations to enforce regulatory compliance manually. RPA also helps make sure that the data is accurate and maintains a complete log of changes. This data helps monitor regulatory compliance on a regular basis through internal reviews.
The insurance industry is more than ready to take advantage of RPA technologies.
While RPA is the technology to embrace, the organizations must remember that its effectiveness depends on the mindful implementation. A thumb-rule to follow while implementing RPA would be to ensure that it is effective only when there are repetitive, rule-based processes are involved. The lesser the need for human judgment, the more the process is suited for an RPA implementation.