Browse > Article
http://dx.doi.org/10.14317/jami.2014.343

FUZZY REGRESSION TOWARDS A GENERAL INSURANCE APPLICATION  

Kim, Joseph H.T. (Department of Applied Statistics and Quantitative Risk Management (QRM), College of Business and Economics, Yonsei University)
Kim, Joocheol (Department of Economics and Quantitative Risk Management (QRM), College of Business and Economics, Yonsei University)
Publication Information
Journal of applied mathematics & informatics / v.32, no.3_4, 2014 , pp. 343-357 More about this Journal
Abstract
In many non-life insurance applications past data are given in a form known as the run-off triangle. Smoothing such data using parametric crisp regression models has long served as the basis of estimating future claim amounts and the reserves set aside to protect the insurer from future losses. In this article a fuzzy counterpart of the Hoerl curve, a well-known claim reserving regression model, is proposed to analyze the past claim data and to determine the reserves. The fuzzy Hoerl curve is more flexible and general than the one considered in the previous fuzzy literature in that it includes a categorical variable with multiple explanatory variables, which requires the development of the fuzzy analysis of covariance, or fuzzy ANCOVA. Using an actual insurance run-off claim data we show that the suggested fuzzy Hoerl curve based on the fuzzy ANCOVA gives reasonable claim reserves without stringent assumptions needed for the traditional regression approach in claim reserving.
Keywords
Fuzzy regression; chain ladder; claim reserving;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Kaas, Modern actuarial risk theory: Using R, Springer, New York, 2008.
2 D. Dubois and H. Prade, Analysis of Fuzz Information, Vol. 2, chapter Fuzzy numbers: an overview. CRC-Press, Boca Raton (1988).
3 P. England and R. Verrall, Stochastic claims reserving in general insurance. British Actuarial Journal, Vol. 8 (2002), No. 3, 443-518.   DOI
4 H. Ishibuchi and M. Nii, Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks. Fuzzy Sets and Systems, Vol. 119 (2001), No. 2, 273-290.   DOI   ScienceOn
5 J. Sanchez, Calculating insurance claim reserves with fuzzy regression. Fuzzy sets and systems, Vol. 157 (2006), No. 23, 3091-3108.   DOI   ScienceOn
6 A. Kumar and A. Kaur, Application of linear programming for solving fuzzy transportation problems, Journal of Applied Mathematics and Informatics, Vol. 29 (2011), No. 3-4, 831-846.   DOI
7 H. Maleki and M. Mashinchi, Fuzzy number linear programming: A probabilistic approach (3), Journal of Applied Mathematics and Informatics, Vol. 15 (2004), No. 1-2, 333-341.
8 J. Neter, M. Kutner, C. Nachtsheim, and W. Wasserman, Applied linear statistical models. Irwin, Chicago, 4th edition, 1996.
9 R. Sherman, Extrapolating, smoothing, and interpolating development factors. In Proceedings of the Casualty Actuarial Society, Vol. 71 (1984), 122-155.
10 H. Tanaka, S. Uejima, and K. Asai, Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybern, Vol. 12 (1982), 903-907.   DOI   ScienceOn
11 T. Wright, A stochastic method for claims reserving in general insurance. Journal of the Institute of Actuaries Vol. 117 (1990), 677-731.   DOI