Studies from Guizhou Normal University Further Understanding of Mathematics (Federated Learning Model for Auto Insurance Rate Setting Based On Tweedie Distribution): Mathematics

2024 FEB 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators discuss new findings in Mathematics. According to news originating from Guiyang, People’s Republic of China, by NewsRx correspondents, research stated, “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.”

Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, Guizhou Province Science and Technology Plan Project, Research Project of Guizhou University for Talent Introduction, Cultivation Project of Guizhou University, Open Fund of Key Laboratory of Advanced Manufacturing Technology, Ministry of Education.

Our news journalists obtained a quote from the research from Guizhou Normal University, “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.”

According to the news editors, the research concluded: “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.”

This research has been peer-reviewed.

For more information on this research see: Federated Learning Model for Auto Insurance Rate Setting Based On Tweedie Distribution. Computer Modeling in Engineering & Sciences, 2024;138(1):827-843. Computer Modeling in Engineering & Sciences can be contacted at: Tech Science Press, 871 Coronado Center Dr, Sute 200, Henderson, NV 89052, USA.

The news correspondents report that additional information may be obtained from Changgen Peng, Guizhou Normal University, Guizhou Big Data Acad, Guiyang 550025, People’s Republic of China. Additional authors for this research include Tao Yin, Weijie Tan, Dequan Xu and Hanlin Tang.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.32604/cmes.2023.029039. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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