A novel approach for cross-selling insurance products using positive unlabelled learning

Abstract

Successful cross-selling of products is a key goal of companies operating within the insurance industry. Choosing the right customer to approach for cross-purchase opportunities has a direct effect on both decreasing customer churn rate and increasing revenue. Unlike sales data of general products, insurance sales data typically contains only a few products (e.g., private medical insurance, life insurance, etc), it is highly imbalanced with a vast majority of customers with no cross-purchasing information, highly noisy due to varying purchase behaviour between different customers, and has no ground truth for knowing if the majority customers are truly non-cross-sell customers or they are missed opportunities. These data challenges render the building of machine learning models for accurately identifying potential cross-sell customers extremely difficult. This paper proposes a novel approach to solve this challenging problem of cross-sell customer identification using Positive Unlabelled (PU) learning in conjunction with advanced feature engineering on customer demographic data and unstructured customer question-response texts through topic modelling. We implement a bagging approach to iteratively learn the positive samples (the confirmed cross-sells) alongside random sub-samples of the unlabelled set. The proposed approach is extensively evaluated on real insurance data that has been newly collected from a leading insurance company for this study. Evaluation results demonstrate that our approach can successfully identify new potential opportunities for likely cross-sell customers