In this paper, we focus our attention on the most relevant non-market risk in insurance, that is, lapse risk. It is basically linked to the behavior of policyholders facing various situations such as aging, actual economic condition, contract features, and so on. At the same time, policyholder's retention directly impacts the protability of the product itself, thus the protability of the company as a whole. Through the rst part of our analysis, we will recognize some relevant lapse risk factors from a specic dataset including a number of explanatory variables. More importantly, the predictive results from the traditional logistic regression will be compared to those of a bagging classication tree, in order to select the most powerful model. Furthermore, the goal of the second part of the analysis is the valuation of the impact on the protability of a specic insurance product based on the predicted lapse rates. We will observe how signicant the policyholder behavior can be as soon as it is introduced within the prot valuation in a dynamic fashion.
policyholder behavior, lapse risk, machine learning, logistic regression, bagging classication tree, TVOG
Tipo di pubblicazione: