2025 AI Insights for Actuaries: A Virtual Symposium - Managerial 1: Federated Learning Use Cases- 5.14.25 Recording

Federated learning (FL) describes a distributed machine-learning framework enabling multiple devices or organizations to collaborate on a machine-learning model without having to share their raw data with each other or with a central server. One promising application of FL lies in the insurance industry, where each firm harvests a vast amount of client and claims data. There has been little to no previous literature on the applications of FL on insurance data. This talk aims to fill the gap and to offer researchers and practitioners an introduction, the pros and cons of FL, as well as potential use cases.