Privacy-Enhancing Collaborative Information Sharing through Federated
  Learning -- A Case of the Insurance Industry
Panyi Dong Zhiyu Quan Brandon Edwards Shih-han Wang Runhuan Feng Tianyang Wang Patrick Foley Prashant Shah
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 rating factors, etc.. During each round of FL,
collaborators compute improvements on the model using their local private data,
and these insights are combined to update a global model. Such aggregation of
insights allows for an increase to the effectiveness in forecasting claims
losses compared to models individually trained at each collaborator.
Critically, this approach enables machine learning collaboration without the
need for raw data to leave the compute infrastructure of each respective data
owner. Additionally, the open-source framework, OpenFL, that is used in our
experiments is designed so that it can be run using confidential computing as
well as with additional algorithmic protections against leakage of information
via the shared model updates. In such a way, FL is implemented as a
privacy-enhancing collaborative learning technique that addresses the
challenges posed by the sensitivity and privacy of data in traditional machine
learning solutions. This paper's application of FL can also be expanded to
other areas including fraud detection, catastrophe modeling, etc., that have a
similar need to incorporate data privacy into machine learning collaborations.
Our framework and empirical results provide a foundation for future
collaborations among insurers, regulators, academic researchers, and InsurTech
experts.