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A data set for modeling claims processes—TSA claims data

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  • Mary Kelly
  • Zilin Wang

Abstract

This data insight highlights the Transportation Security Administration (TSA) claims data as an underused data set that would be particularly useful to researchers developing statistical models to analyze claim frequency and severity. Individuals who have been injured or had items damaged, lost or stolen may make a claim for losses to the TSA. The federal government reports information on every claim from 2002 to 2017 at https://www.dhs.gov/tsa-claims-data. Information collected includes claim date and type and site as well as closed claim amount and disposition (whether it was approved in full, denied, or settled. We provide summary statistics on the frequency and the severity of the data for the years 2003 to 2015. The data set has several unique features including severity is not truncated (there is no deductible), there are significant mass points in the severity data, and the frequency data shows a high degree of auto correlation if compiled on a weekly basis, and substantial frequency mass points at zero for daily data.

Suggested Citation

  • Mary Kelly & Zilin Wang, 2020. "A data set for modeling claims processes—TSA claims data," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 23(3), pages 269-276, September.
    • Handle: RePEc:bla:rmgtin:v:23:y:2020:i:3:p:269-276
    DOI: 10.1111/rmir.12155
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    References listed on IDEAS

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    1. Garrido, J. & Genest, C. & Schulz, J., 2016. "Generalized linear models for dependent frequency and severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 205-215.
    2. Lee, Gee Y. & Shi, Peng, 2019. "A dependent frequency–severity approach to modeling longitudinal insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 115-129.
    3. Shi, Peng & Feng, Xiaoping & Ivantsova, Anastasia, 2015. "Dependent frequency–severity modeling of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 417-428.
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