R-1 - Collaborative Modeling Without Sharing Data

Traditional model building typically relies on centralized, local data sources. Federated machine learning is designed to facilitate information sharing without the need to transfer proprietary data. This collaborative framework allows an insurance company to combine data with other parties (insurance companies, InsurTechs, government entities) to build better models. Each private "data owner" independently trains a model using their specific data then these individual models are shared with a central "aggregation server" which consolidates the knowledge from all "data owners" to generate a consensus model. This process enhances the overall effectiveness and efficiency of modeling for a more comprehensive understanding of the targeted population.