City Research Online

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.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript version of the following article, accepted for publication in North American Actuarial Journal. 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, 23:3, 469-484. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Data science, Algorithms, Big Data, Periodicity, Artificial Intelligence
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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