Designing and deploying insurance recommender systems using machine learning

Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever-growing markets, providing product recommendations to new and existing customers is a challenge. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they are adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our systems. We also present a deep-learning-based approach to provide recommendations to prospects (potential customers) where only external marketing data is available at the time of prediction. This article is categorized under: Application Areas > Industry Specific Applications Algorithmic Development > Structure Discovery Algorithmic Development > Bayesian Models Technologies > Machine Learning