Exploring the Synergy of Self-Supervised Learning and Bayesian Networks for Customer Profiling in the Insurance Industry

Abstract

The insurance business is increasingly relying on sophisticated machine learning techniques to better assess risk and profile customers. Since conventional approaches suffer from the issues of sparsity in data and a change in the behavior of customers, solutions need to be more flexible and easier to understand. This research tries to see if using SSL and BN together, through sizable, unlabeled datasets, may improve profiling customers in the insurance industry; better risk prediction; and general decision-making. The proposed methodology, particularly combines Self-Supervised Learning (SSL) with Bayesian Networks (BN), surpasses conventional machine learning techniques, achieving 93% accuracy and 0.92 AUC. This hybrid model excels at handling sparse, unlabeled data, providing more accurate and interpretable consumer profiling to improve insurance decision-making. It uses Bayesian networks for probabilistic modeling and dependency learning to extract features from raw, unlabeled data and integrates SSL. The model is evaluated using metrics such as AUC, recall, accuracy, and precision. Better than the typical approaches in machine learning, the proposed hybrid SSL and BN performs with 93% accuracy, 91% performance, and 0.92 AUC, showing excellent performance in handling sparse data while making correct risk evaluation. In the context of the insurance industry, this method ensures an essential, flexible and readable solution to customer profiling from the integration of SSL and BN. Using unlabeled data and probabilistic reasoning for this method further enhances the field of risk management, decisions and customized solutions.