Predictive Modeling for Life Insurance

The use of advanced data mining techniques to improve decision making has already taken root in property and casualty insurance as well as in many other industries [1, 2]. However, the application of such techniques for more objective, consistent and optimal decision making in the life insurance industry is still in a nascent stage. This article will describe ways data mining and multivariate analytics techniques can be used to improve decision making processes in such functions as life insurance underwriting and marketing, resulting in more profitable and efficient operations. Case studies will illustrate the general processes that can be used to implement predictive modeling in life insurance underwriting and marketing. These case studies will also demonstrate the segmentation power of predictive modeling and resulting business benefits.