- Himchan Jeong, Department of Statistics and Actuarial Science at Simon Fraser University in Canada
- Integration of Traditional and Telematics Data for Efficient Insurance Claims Predictions
- 02/21/2023
- 4:00 PM - 5:00 PM
- C304 Wells Hall
- Amanda Nickols (nickols2@msu.edu)
Linked 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 concern or adverse selection compared to the data points
with traditional features. To handle this issue, we propose a data integration technique based
on calibration weights. It is shown that the proposed technique can efficiently integrate the
so-called traditional data and telematics data and also cope with possible adverse selection
issues on the availability of telematics data. Our findings are supported by a simulation study
and empirical analysis on a synthetic telematics dataset.
