Constructing a personalized recommender system for life insurance products with machine-learning techniques

Abstract

The collaborative filtering (CF) recommendation algorithm predicts the purchases of specific users based on their characteristics and purchase history. This study empirically analyzes the possibility of applying CF to the insurance industry using real customer data from South Korea. Using three different CF models, we examined the relevance of applying the CF model to insurance products under various situations by comparing them with logistic-regression-based recommendation models. Through experiments, we empirically show that CF models apply to the insurance industry, especially when customer purchase information is added to the model.