This research explores the development and application of predictive modeling techniques for insurance premium pricing, aiming to enhance pricing accuracy, risk assessment, and operational efficiency within the insurance industry. With the increasing availability of structured and unstructured data, traditional actuarial methods are being augmented—and in some cases replaced-by advanced machine learning algorithms. This study investigates various modeling approaches including linear regression, decision trees, gradient boosting, and neural networks, comparing their performance in predicting insurance premiums based on customer profiles, historical claims, policy details, and behavioral factors. The research emphasizes data preprocessing, feature engineering, model validation, and interpretability, highlighting the trade-offs between model complexity and transparency. Our findings demonstrate that predictive models can significantly improve premium pricing strategies, reduce adverse selection, and support fairer and more personalized insurance offerings. This paper contributes to the evolving landscape of data-driven decision-making in insurance, providing a framework that balances accuracy, fairness, and regulatory compliance.
Data Analytics, Predictive Modeling, Machine Learning, JavaScript, PHP, MySQL.
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