Predictive modeling study demonstrates accurate cross-sell forecasting in health insurance policyholders, highlighting the benefit of data synthesis for imbalanced datasets.
This study addresses the need for effective cross-selling in the insurance sector by developing a predictive framework to identify health insurance policyholders likely to purchase vehicle insurance. Leveraging AI techniques, the research aims to provide insurers with tools for targeted marketing and personalized recommendations. A subset of 3,000 records from a public dataset (381,000 samples, 10 features) was analyzed, revealing significant data imbalance. To address this, three data synthesis techniques—SMOTE, SMOTENC, and KMeansSMOTE—were applied. Dimensionality reduction methods (PCA and UMAP) were used for visualization, and feature ranking was conducted using RFC to assess feature importance. By tackling class imbalance and employing advanced visualization and feature analysis, this study enhances the accuracy and fairness of insurance recommendations, benefiting both insurers and policyholders in a data-driven landscape.
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Chu et al. (2025) studied this question.
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