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Applications of Statistics in the Field of General Insurance: An Overview

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  • Yves L. Grize

Abstract

type="main" xml:id="insr12066-abs-0001"> This is an expository paper on applications of statistics in the field of general insurance, also called non-life insurance. Unlike life insurance where advanced statistical techniques have long been part of financial mathematics and actuarial applications, their use is only relatively recent in non-life insurance. The business model of insurance companies, especially those active in non-life insurance, has seen dramatic changes over the last 15 years. The aim of this paper is to convince the readers that especially today non-life insurance is not only an exciting ground to apply existing modern statistical tools but also a fertile environment for new and challenging statistical developments. The activities of an insurance company can be viewed as an industrial process where data management and data analysis play a key role. That is why a fundamental understanding of data-related issues (such as data quality, variability, analysis and correct interpretation) is so essential to the insurance business. These are exactly the tasks where professional statisticians excel. Also, a better understanding of the field of general insurance by statisticians will promote fruitful exchanges between actuaries and statisticians, thereby helping to bring actuarial and statistical professional societies closer to each other. Selected examples are used to cover the essential aspects of general insurance, and all of them are based on the author's experience. The paper concludes with some remarks on the role of statisticians working in general insurance.

Suggested Citation

  • Yves L. Grize, 2015. "Applications of Statistics in the Field of General Insurance: An Overview," International Statistical Review, International Statistical Institute, vol. 83(1), pages 135-159, April.
    • Handle: RePEc:bla:istatr:v:83:y:2015:i:1:p:135-159
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    References listed on IDEAS

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    1. Taylor, Greg, 2001. "Geographic Premium Rating by Whittaker Spatial Smoothing," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 147-160, May.
    2. England, P.D. & Verrall, R.J., 2002. "Stochastic Claims Reserving in General Insurance," British Actuarial Journal, Cambridge University Press, vol. 8(3), pages 443-518, August.
    3. Yanwei Zhang & Vanja Dukic & James Guszcza, 2012. "A Bayesian non‐linear model for forecasting insurance loss payments," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 637-656, April.
    4. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    5. Mack, Thomas, 1993. "Distribution-free Calculation of the Standard Error of Chain Ladder Reserve Estimates," ASTIN Bulletin, Cambridge University Press, vol. 23(2), pages 213-225, November.
    6. Emms, Paul & Haberman, Steven, 2005. "Pricing General Insurance Using Optimal Control Theory," ASTIN Bulletin, Cambridge University Press, vol. 35(2), pages 427-453, November.
    7. Boskov, M. & Verrall, R. J., 1994. "Premium Rating by Geographic Area Using Spatial Models," ASTIN Bulletin, Cambridge University Press, vol. 24(1), pages 131-143, May.
    8. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149.
    9. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    10. Panjer, Harry H., 1981. "Recursive Evaluation of a Family of Compound Distributions," ASTIN Bulletin, Cambridge University Press, vol. 12(1), pages 22-26, June.
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    Cited by:

    1. Benjamin Avanzi & Xingyun Tan & Greg Taylor & Bernard Wong, 2023. "Cyber Insurance Risk: Reporting Delays, Third-Party Cyber Events, and Changes in Reporting Propensity -- An Analysis Using Data Breaches Published by U.S. State Attorneys General," Papers 2310.04786, arXiv.org.
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