Leveraging a Hybrid SVM-Matrix Factorization Model for Personalized Health Insurance Recommendations: A Financial Analytics Approach

Abstract

In the increasingly competitive insurance industry, personalized recommendation systems are crucial for enhancing customer satisfaction and engagement by delivering tailored product suggestions. This abstract introduces an innovative approach that combines Support Vector Machines (SVM) with Matrix Factorization (MF) techniques to develop a robust insurance recommendation system. The integration of these methods aims to address the challenges of predicting customer preferences and improving recommendation accuracy. The proposed system harnesses the predictive power of SVM to model customer behaviour and preferences. By analysing customer demographic data and historical interaction patterns, SVM provides accurate predictions regarding the likelihood of a customer purchasing specific insurance products. The SVM's strength lies in its ability to handle high-dimensional data and model complex relationships between features, making it well-suited for predicting individual customer preferences with precision. Its kernel methods further enhance its capability to capture non-linear patterns in the data. To complement the predictive modelling, Matrix Factorization is used to refine the recommendation accuracy. Matrix Factorization works by decomposing the large, sparse interaction matrix of customers and insurance products into lower-dimensional latent factors. This decomposition reveals hidden patterns and relationships, uncovering similarities between customers and insurance products that are not immediately apparent from the raw data. By capturing these underlying patterns, MF enhances the system's ability to recommend products that align closely with individual customer preferences, even in cases of limited explicit data. The synergy between SVM and MF in this recommendation system effectively combines the strengths of both techniques: the detailed predictive power of SVM and the pattern recognition capabilities of MF. This hybrid approach not only improves the accuracy of recommendations but also ensures a more personalized and engaging user experience. By leveraging these advanced techniques, the proposed system represents a significant advancement in recommendation technology for the insurance industry, promising to enhance customer satisfaction and foster deeper engagement.