A typical insurance workforce commits 15 per cent of their precious time on performing repetitive tasks. These tasks include customer database updates and manual underwriting. By no means are these insignificant activities. They are quite important indeed.

However, from a business development perspective, such activities are not adding new customers to the company’s data list. Additionally, these tasks do not even improve customer experience or save company costs.

Bahaa Abdul Hussein highlights that this is where the concept of RPA (Robotic Process Automation) enters the picture.

RPA as a Starting Point

McKinsey sponsored financial sector case research reveals a potential 200 per cent growth in the return of investment, this too in just the first year of using RPA. Many companies in the insurance sector are already using RPA to automate repetitive tasks and operations.

But RPA is a short-term solution as it still needs human supervision from time to time. After all, a robot cannot think for itself. In addition to RPA, companies also require intelligent process automation.

Many utilities, banking and insurance sectors are deploying RPA technology. Before understanding the limitations of RPA, it is important to know how RPA works.

RPA Mechanism  

The RPA technology relies on User Interface (UI) to produce low-code scripts. These scripts help automate tasks with limited or no variations at all. It is like a bottle-making factory where each bottle undergoes the same process, day in and day out.

Using the same concept, insurance companies find RPA useful in claims processing as it follows a predictable set of processes.

But the problem with RPA bots is that they cannot change according to the change in processes.

Adding Intelligent Automation to RPA

RPA require intelligence which would allow the bots to learn and evolve with the evolving technologies. The combination of Artificial Intelligence (AI) with Machine Learning (ML) is paramount to take things forward.

Using a reliable AI, RPA systems can comfortably handle routine tasks and also deal with exceptions without the need of human intervention.

Between 20 and 30 per cent of all automated processes need some form of manual assistance. By using intelligent automation, such process become self-reliant and dependable.

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