Applications of Metaheuristics in Insurance

When calculating different profitability measures for a life insurance company, one of the most important parameters to know is the probability of a policy being in force at any given time after the start of risk bearing. These probabilities are given by the survival function. In this paper, we examine data from a Hungarian insurance company, in order to build models for the survival functions of two life insurance products. For survival function estimation based on the unique parameters of a new policy, Cox regression is used. However, not all parameters of a new policy are relevant in estimating the survival function. Therefore, application of model selection algorithms is needed. Furthermore, if the exact effects of the policy parameters for the survival function can be determined, the insurance company can direct its sales team to acquire policies with positive technical results. When traditional model selection techniques proposed by the literature (such as best subset, stepwise and regularization methods) are applied on our data, we find that the effect of the selected predictors for survival cannot be determined, as there is a harmful degree of multicollinearity. In order to tackle this problem, we propose adding the hybrid metaheuristic from Láng et al. (2017) to the Cox regression in order to eliminate multicollinearity from the final model. On the test sets, performance of the models from the metaheuristic rivals those of the traditional algorithms with the use of noticeably less predictors. These predictors are not significantly correlated and are significant for survival, as well. It is shown in the paper that with the application of metaheuristics, we could produce a model with good predicting capabilities and interpretable predictor effects. These predictor effects can be used to direct the sales activities of the insurance company.