An Adaptive Estimation of Distribution Algorithm for Multipolicy Insurance Investment Planning

Abstract

Insurance has been increasingly realized as an important way of investment and risk aversion. Fruitful insurance products are launched by insurers, but there is little research on how to make a proper insurance investment plan for a specific policyholder given different kinds of policies. In this paper, we aim to propose a practical approach to multipolicy insurance investment planning with a data-driven model and an estimation of distribution algorithm (EDA). First, by making use of the insurance data accumulated in the modern financial market, an optimization model about how to choose endowment and hospitalization policies is built to maximize the yearly profit of insurance investment. With the model parameters set according to the real data from insurance market, the resulting plan is practical and individualized. Second, as the optimal solution cannot be achieved by mathematical deduction under this data-driven model, an EDA is introduced. To adapt the EDA for the considered problem, the proposed EDA is mixed with both the continuous and discrete probability distribution models to handle different kinds of variables. In addition, an adaptive scheme for choosing suitable distribution models and an efficient constraint handling strategy are proposed. Experiments under different conditions confirm the effectiveness and efficiency of the proposed model and method.