Every time a Life Insurance company writes a new policy, it actually spends money out of its own pocket to cover the costs involved, like, salary of the agent, branch operating costs but the biggest cost is called Reserving - This is a fixed amount that is kept aside in case the policy results in an early claim.
Each new premium contributes towards building the claims reserve that needs to be provisioned for, therefore, in the first two years, every new policy results in a drain on the books. The company makes money once the customer has stayed for some time and that is why it is very important that a customer renews his or her policy every year and stays with the insurance provider for a long period of time.
In the following video, Ashish Kumar will walk you through the complete process as to how this happens.
In the above video, you learned that reaching out to all customers is an extremely cost-intensive effort and hence it is advisable to do a targeted reaching out to the customers who have a high propensity to pay.
One of the more efficient ways to achieve that is through creating an analytics model to predict the customers with a high propensity to renew the insurance policy. You also learned that any analytics project should start with exploratory data analysis as it gives you a clear idea of the relationship between the variables.
In the next segment, you will learn further details about the analytics process and see how analytics is used to solve this problem.