Federated Learning: Privacy-Preserving Data Sharing and Underwriting Applications in the Insurance Industry

Abstract

As the digital transformation of the insurance industry accelerates, cross-institutional data sharing (such as between insurance companies and hospitals, credit-reporting agencies) has become a crucial means of enhancing the efficiency and accuracy of underwriting. However, data privacy protection and the problem of data silos have emerged as the main contradictions constraining its development. This paper focuses on the application of federated learning technology in cross-institutional data sharing in the insurance industry. Firstly, it provides a detailed introduction to the basic principles of federated learning, including distributed model training, data encryption techniques, and collaborative learning mechanisms, elucidating the path to achieving “data availability without visibility.” Secondly, it proposes a technical solution for federated learning in insurance underwriting, which addresses the challenges of data privacy protection and sharing through data encryption, distributed model training, and collaborative output of results. Finally, in conjunction with the actual needs of the insurance industry, it explores the extended applications of federated learning in claims, anti-fraud, and other scenarios, and designs a technical architecture diagram and compliance checklist to verify its advantages in privacy protection and data security.