Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques

Mavundla, Khulekani and Thakur, Surendra and Adetiba, E. and Abayomi, Abdultaofeek (2024) Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques. Journal of Computer Information Systems Latest Articles.

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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 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.

Item Type:	Article
Uncontrolled Keywords:	Health insurance  cross-selling  customer churn  machine learning algorithms  prediction  model training
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Patricia Nwokealisi
Date Deposited:	07 Nov 2024 09:35
Last Modified:	07 Nov 2024 09:35
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18568

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