Leveraging Data Analytics for Lapse Reduction in Life Insurance

Abstract

Abstract Insurance companies operate on the concept of pooling losses among their insureds. An insurer invests the premiums to earn enough money not only to pay for losses but also to operate the company and gain a profit. Thus, the insurance company must reasonably predict the payments that will be made for loss and charge affordable premiums to insure a risk. The term "lapse" refers to the termination of an insurance policy by the policyholder for any reason other than the death of the policyholder. When an insurance policy lapses, it will decrease the performance of the product, the initial year’s expense of the policy may not be covered and it will create a loss of public image. Since retaining existing customers is much cheaper and more profitable than getting a new customer, it is crucial to identify policies which are likely to lapse. Even though the insurance industry uses a number of mathematical, statistical, and financial concepts to understand the behaviour of policyholders and quantify future liabilities and risks, those approaches have major drawbacks. This study focuses on predicting individual policyholder lapse rate and identifying scenarios which reduce lapses in Sri Lankan Insurance industry. To conduct this, firstly data of policyholder need to be collected and analysed. This information is gathered form an Insurance Company. Policies that commence from 2013 to 2022 are included. 32 parameters are considered for the analysis and variable importance is calculated. Then Random Survival Forest (RSF) and Cox net Survival Analysis are used to predict the lapse rate. Those techniques let the model to construct survival functions with different shapes for each insured. Model performance is high in random survival forest compared to cox net survival analysis as it captures linear, non-linear relationships and interactions between many factors. Hence variable importance is calculated using random survival forest. Then scenarios are performed to identify policy characteristics which give low lapse rate, in other words high survival rate. By the findings, it was successfully concluded that, through machine learning teachings, insurance companies will not only be able to predict the lapse rate of individual policyholders but also be able to identify policy characteristics that give a high survival rate.