How RPA Driven By AI & ML Can Help Insurers
Implementing more automation with artificial intelligence and machine learning will reduce processing times, reduce mistakes, provide better customer experience and increase employee efficiency
Robotic process automation has disrupted the insurance industry by streamlining operations, enhancing customer experience and improving employee productivity. This AI driven automation process replicates repetitive organizational activities like manual back-office and customer facing tasks. The processes in insurance companies are often duplicated, manual, bureaucratized, and time-consuming.
Modern technologies driven by machine learning and artificial intelligence can automate operations in insurance companies thereby cutting costs and increasing productivity. Manual tasks in insurance result in time consuming application and claims processing. This can be crucial in retailing customers and gaining new ones. RPA has provided a light at the end of tunnel as it promises a significant improvement in operational efficiency, reducing claims processing costs in addition to full compliance with regulations.
Underwriting is a tedious process involving a great deal of manual and repetitive actions. It requires enormous data collection from multiple sources so that the policy risk can be measured. This process takes time, an average of 3-4 weeks and it can hinder customer experience and insurers end up losing business. Underwriting becomes an ideal fit for RPA implementation. It can automate the data collection process on the applicant from various sources. One example could be getting the right prescription, medical history from a healthcare provider.
How can insurance companies approach RPA?
One of the safest things to do is by doing RPA pilots. By doing this, they can reduce cost and get a peak on the benefits too. They reduce manual task by almost half and increase the operational capacity. It can help companies to completely rethink and redesign the claims process for example. By going digital with RPA as the driver, time to launch can be as little as a few weeks. It has the capacity to integrate and interact with legacy systems. Replicating the pilot in different markets where the company is operating becomes simpler once the proof of concept is implemented. By significantly decreasing the time to pay claims, insurance companies can reserve less to pay actual claims and has a huge positive impact on their cash flow. Intelligent robots can update the relevant enterprise systems with the data and deliver a report with the complete policy document.
Bots can be used to manage policies across geographies and products. This helps the employees to focus on complex regulations, policies and compliances. For example, bots can validate data, cross check policies, issue invoices and deliver policy documents without any manual intervention. Bots can help in doing an end to end process execution with no human intervention and can automate majority of the processes. Typically, 30-50% of the processes follow a standard workflow and remaining can take an alternate path. In these processes based on the complexity, bots can completely take end to end processes or have partial human intervention or in certain cases full human intervention. Based on the log of the bots’ activity, troubleshooting can be done and analysed.
Bots task can be monitored and recorded at each level. Some of the data captured like number of transactions or exception issues can be monitored to make more process improvement. It helps regulatory compliance as the audit trail is captured. All these process improvements only result in streamlined applications, claims and faster customer service response.
As Marc Andreessen famously said “Software is eating the world”. Advancements in artificial intelligence and machine learning are benefiting insurance companies and can automate processes across the products and business units. What the bots cannot do, by developing machine learning models, new set of capabilities can be used to automate processes which require human intervention. With Machine learning, customer onboarding can be reduced to a few minutes. Employees and customers can now focus more on productive tasks which will help both employee productivity and customer experience.
As a thumb rule, there are few things insurance companies can follow to implement a successful RPA initiative. A thorough testing strategy for the automation needs to be thought out even before implementing RPA. It is better to implement an end to end automated design instead of looking at specific tasks. This can help in getting maximum benefit out of automation. As much as possible, exception work flow need to be automated to get bigger benefits.
There are many processes that can be streamlined and improved with RPA. Implementing more automation with artificial intelligence and machine learning will reduce processing times, reduce mistakes, provide better customer experience and increase employee efficiency.
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