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Time Series Data Mining with an Application to the Measurement of Underwriting Cycles

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Listed:
  • Iqbal Owadally
  • Feng Zhou
  • Rasaq Otunba
  • Jessica Lin
  • Douglas Wright

Abstract

Underwriting cycles are believed to pose a risk management challenge to property-casualty insurers. The classical statistical methods that are used to model these cycles and to estimate their length assume linearity and give inconclusive results. Instead, we propose to use novel time series data Mining algorithms to detect and estimate periodicity on U.S. property-casualty insurance markets. These algorithms are in increasing use in data science and are applied to Big Data. We describe several such algorithms and focus on two periodicity detection schemes. Estimates of cycle periods on industry-wide loss ratios, for all lines combined and for four specific lines, are provided. One of the methods appears to be robust to trends and to outliers.

Suggested Citation

  • Iqbal Owadally & Feng Zhou & Rasaq Otunba & Jessica Lin & Douglas Wright, 2019. "Time Series Data Mining with an Application to the Measurement of Underwriting Cycles," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(3), pages 469-484, July.
    • Handle: RePEc:taf:uaajxx:v:23:y:2019:i:3:p:469-484
    DOI: 10.1080/10920277.2019.1570468
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    Cited by:

    1. Christian Eckert & Christof Neunsinger & Katrin Osterrieder, 2022. "Managing customer satisfaction: digital applications for insurance companies," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 569-602, July.

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