Various modeling approaches in auto insurance pricing

다양한 모형화를 통한 자동차 보험가격 산출

  • Kim, Myung-Joon (Automobile Insurance Product Pricing Dept., Samsung Fire & Marine Insurance Co.) ;
  • Kim, Yeong-Hwa (Department of Statistics, Chung-Ang University)
  • 김명준 (삼성화재해상보험주식회사) ;
  • 김영화 (중앙대학교 자연과학대학 통계학과)
  • Published : 2009.05.31


Pricing based on proper risk has been one of main issues in auto insurance. In this paper, we review how the techniques of pricing in auto insurance have been developed and suggest a better approach which meets the existing risk statistically by comparison. The generalized linear model (GLM) method is discussed for pricing with different distributions. With GLM approach, the distribution of error assumed plays an main role for the best fit corresponding to the characteristics of dependent variables. Tweedie distribution is considered as one of error distributions in addition to widely used Gamma and Poisson distribution. With these different types of error assumption for estimating the proper premium in auto insurance, various modeling approaches are possible. In this paper, various modeling approaches with different assumptions for estimating proper risk is discussed and also real example is given by assuming different.


  1. Bailey, R. A. and Leroy, J. S. (1960). Two studies in automobile insurance ratemaking. Proceedings of the Casualty Actuarial Society, 47, 192-217.
  2. Bailey, R. A. (1963). Insurance rates with minimum bias. Proceedings of the Casualty Actuarial Society, 50, 4-11.
  3. Feldblum, S. and Brosius, J. E. (2002). The minimum bias procedure - A partitioner's guide. Proceedings of the Casualty Actuarial Society.
  4. Jorgensen, B. and Paes de Souza, M. C. P. (1994). Fitting Tweedie's compound model to insurance claims data. Scandinavian Actuarial Journal, 1, 69-93.
  5. Kaas, R., Goovaerts, M. J., Dhaene, J. and Denuit, M. (2001). Modern actuarial risk theory, Kluwer, Dordrecht.
  6. Kim,Y-H. and Kim, Ki Su .(2009). Small area estimation of the insurance benefit for customer segmentation. Journal of the Korean Data & Information Science Society, 20, 77-87.
  7. Murphy, K. P., Brockman, M. J. and Lee, P. K. W. (2000). Using generalized linear models to build dynamic pricing systems. Casualty Actuarial Forum, Winter 2000.
  8. Smyth, G. K. and Jorgensen, B. (2002). Fitting Tweedie's compound model to insurance claims data: Dispersion modelling. ASTIN Bulletin, 32, 143-157.
  9. Tweedie, M. C. K (1984). An index which distinguishes between some important exponential families in statistics applications and new directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference, 579-604.