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

• URL: http://arxiv.org/abs/2402.14983v1
• Date: Thu, 22 Feb 2024 21:46:24 GMT
• Title: Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
• Authors: Panyi Dong, Zhiyu Quan, Brandon Edwards, Shih-han Wang, Runhuan Feng, Tianyang Wang, Patrick Foley, Prashant Shah
• Abstract summary: The report demonstrates the benefits of harnessing the value of Federated Learning (FL) to learn a single model across multiple insurance industry datasets. FL addresses two of the most pressing concerns: limited data volume and data variety, which are caused by privacy concerns. 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.
• Score: 1.8092553911119764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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