TY  - EJOU
AU  - Yin, Tao 
AU  - Peng, Changgen 
AU  - Tan, Weijie 
AU  - Xu, Dequan 
AU  - Tang, Hanlin 

TI  - Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 138
IS  - 1
SN  - 1526-1506

AB  - In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data. After determining which entities are shared, the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters. The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model. Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data from both parties without exchanging data. The assessment results of the scheme approach those of the Tweedie regression model learned from centralized data, and outperform the Tweedie regression model learned independently by a single party.
KW  - Rate setting; Tweedie distribution; generalized linear models; federated learning; homomorphic encryption

DO  - 10.32604/cmes.2023.029039