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    [세미나] [ISE Colloquium] Sept. 27th 11:00 / Zoom / Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction / Prof. Himchan Jeong / Simon Fraser University
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    • 2024.09.20
    • 137
    ISE 콜로퀴움이 다음과 같이 진행될 예정입니다.
     
    # 날짜/시간: 2024년 9월 27일 금요일 11:00~12:00
    # 연사: Professor HimChan Jeong, Simon Fraser University
    # 제목: Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction
    The seminar will be given in English.
    # 초록 : 

    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 holds a Ph.D. in Mathematics with a concentration in Actuarial Science from the University of Connecticut, and a M.Sc. (Statistics), B.A. (Business Administration), and B.Sc. (Mathematical Science) from Seoul National University, South Korea. Himchan is also a Fellow of the Society of Actuaries (SOA).

     

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    ISE dept. invites you to the following seminar.

     

    # Time/Date : Sep. 27, 2024 (Fri.) 11:00~12:00

    # Location: Zoom https://kaist.zoom.us/j/82217529529 ID: 822 1752 9529

    # Presenter: Professor HimChan Jeong , Simon Fraser University

    # Title : Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction
    The seminar will be given in English.
    # 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.
     # Bio

    Himchan holds a Ph.D. in Mathematics with a concentration in Actuarial Science from the University of Connecticut, and a M.Sc. (Statistics), B.A. (Business Administration), and B.Sc. (Mathematical Science) from Seoul National University, South Korea. Himchan is also a Fellow of the Society of Actuaries (SOA).