Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques
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Description
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 topperforming
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.
Keywords
QA75 Electronic computers. Computer science