Himchan Jeong
Simon Fraser University
Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction
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.
Bio: Himchan is a Fellow of the Society of Actuaries (SOA) and holds a Ph.D. from the University of Connecticut. He has been actively involved in teaching and conducting research in actuarial science for several years. In recognition for his academic achievements and excellence, he has been awarded the James C. Hickman Scholarship from SOA recently in 2018-2020. His current research interest is predictive modeling for ratemaking and reserving of property and casualty insurance.
DATE: Wednesday, 2/21/24, 4:00 PM, AUST 163
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mb882b7f8a95d36fa4f925df7ca24eb74
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu