Predicting cross-selling health insurance products using machine-learning techniques

Abstract

This study delves into the utilization of Machine Learning (ML) techniques for predicting health insurance cross-selling behavior in South African consumers. The main goal is to create a robust ML model that assists health insurance companies in pinpointing potential customers with higher probabilities of purchasing additional health insurance products. Employing quantitative methodology, the study extracted consumer data and applied various ML algorithms such as random forest, K-nearest neighbors, XGBoost classifier, and logistic regression using Python. Tailored feature engineering techniques were employed to enhance predictive accuracy. Analyzing 1,000,000 customer records with 16 features, Random Forest emerged as the top-performing model, achieving an accuracy score of 0.99 and F1 score of 1.00. The study reveals that customers aged 25–70, with prior insurance and longer service history, are more inclined to purchase additional health insurance products. These findings provide actionable insights for refining marketing strategies, boosting customer acquisition, and increasing revenue.