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- Title
Integration of traditional and telematics data for efficient insurance claims prediction.
- Authors
Peiris, Hashan; Jeong, Himchan; Kim, Jae-Kwang; Lee, Hangsuck
- Abstract
While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features. To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset.
- Subjects
INSURANCE claims; TELEMATICS; DATA integration; AUTOMOBILE insurance; FORECASTING
- Publication
Astin Bulletin, 2024, Vol 54, Issue 2, p263
- ISSN
0515-0361
- Publication type
Article
- DOI
10.1017/asb.2024.6