Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry

arXiv:2402.14983v1 Announce Type: cross
Abstract: The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insurance industry datasets without requiring the datasets themselves to be shared from one company to another. The application of FL addresses two of the most pressing concerns: limited data volume and data variety, which are caused by privacy concerns, the rarity of claim events, the lack of informative …

arxiv benefits case claims cs.cr cs.lg datasets federated federated learning industry information information sharing insurance insurance industry learn loss modeling privacy q-fin.rm report sharing single terms value