A Life Insurance Policy Bundling Recommendation System Masters Student Paper Competition Submission

Abstract

We have researched and developed a life insurance bundling recommendation system that identifies among current home or auto insurance policy holders who is most likely to add a life insurance policy product to their existing plans as well as when to optimally recommended a life product to the customer. The motivation for this study is that insurance product bundling is a common practice in this industry. However, the implementation process of matching customers to the right products is not widely known and likely could be improved using analytical frameworks found in other domains. Generally, the life insurance business does not have integrated predictive analytics that can recommend and price policies in the same way as other insurance areas. For example, the property and casualty industry often utilize a combination of generalized linear models, credibility techniques, and credit scoring models as part of its modeling techniques for driving business decisions (Abrokwah, 2016). However, we posit that an empirically validated methodological design for the cross-product bundling recommendation process in the insurance industry is an area that necessitates deeper analytical investigation. In collaboration with a major insurance company, we develop and deploy a recommendation engine that uses current policy holder information as features into an ensemble of predictive models to identify when to offer a life policy (single premium, term, or whole life) bundle recommendation that is mostly likely to be purchased. Our solution has provided the insurance company a more efficient, analytically driven, and scalable approach to sell additional products that their customers really want and increase their business revenue. We believe our methodology connects the recommendation system literature to the insurance industry and can be easily adapted by practitioners in this field.

Keywords: Insurance, Recommendation Systems, Predictive Modeling, Analytics, Bundling