How Intelligent Process Automation in Insurance Boosts Your Bottom Line
The era of robotic process automation (RPA) coupled with deep learning is creating waves across industries! Leading banks, hedge funds and asset managers have successfully leveraged RPA tools to streamline processes ranging from back-office functions to customer solutions and in the bargain have saved money significantly.
For the insurance sector, especially for auto, property and casualty sectors, RPA can be used to automate processes that have a traceable pattern and can be taught through a series of instructions to a machine. So the typical processes to automate through RPA are repetitive functions like document screening, claims processing and even internal underwriting processes. RPA works on standard logic to perform repetitive tasks and follows set rules to a tee. This reduces the probability of errors to a bare minimum.
Juniper Research on automation in the insurance industry says that almost half of all insurers will invest in RPA by 2024. Another study from McKinsey on digital disruption in the insurance industry, found that AI automation can cut the cost of insurance claims processing by up to 30%.
Implementing cost-savings through RPA
RPA tools can be used to drive strategic decision making, underwriting processes, fraud detection and prevention, claims processing and handling, and even customer-servicing functions. RPA would benefit businesses by notching up substantial cost savings as repetitive tasks are performed by bots which works out cheaper than having employees perform the same set of routine tasks. Employees get freedom from mundane tasks and instead can focus on tasks which require the human factor such as customer service, customer relationship management or more analytical and ROI-driven tasks. This can drive higher revenues and lead to an improvement in bottom line.
Read more about how you can implement intelligent process automation to transform insurance processes.
Originally published at https://www.simplesolve.com on April 22, 2021.
