Privacy‑Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
The report highlights Federated Learning's (FL) benefits in claims loss modeling by enabling collaboration across multiple insurance datasets without data sharing. FL addresses data privacy concerns, rarity of claim events, and lack of informative factors. It enhances forecasting effectiveness while preserving data privacy, applicable beyond insurance to fraud detection and catastrophe modeling, fostering future collaborations.