The Application of Machine Learning Algorithms in the Insurance Industry under the Background of Big Data

The rapid development of big data is profoundly reshaping the insurance industry, while traditional actuarial methods face obvious limitations in dealing with high-dimensional, nonlinear, and unstructured data. This paper systematically reviews the current application status of machine learning (ML) in the insurance industry under the background of big data, focusing on four key areas: fraud detection, risk assessment and management, claims issues, and customer segmentation. Integrated empirical research shows that ML methods generally outperform traditional statistical models in terms of prediction accuracy, operational efficiency, and decision support. Meanwhile, this paper identifies four types of real-world challenges that restrict the responsible deployment of machine learning: data quality issues, regulatory and ethical constraints, privacy and security concerns, as well as organizational and operational barriers. Based on the literature review, this paper holds that machine learning does not replace actuarial science, but rather substantially enhances its predictive ability, operational efficiency and strategic decision-making level. Responsible technology application must simultaneously promote the construction of transparency, fairness and compliance. This paper constructs an analytical framework that integrates technical capabilities with practical constraints, providing a structured reference for subsequent research and practical guidance for insurance companies and policymakers to promote the sustainable and compliant application of machine learning.