Classification Ratemaking Using Decision Tree in the Insurance Market of Bosnia and Herzegovina

Anderson, D., Feldblum, S., Modlin, C., Schirmacher, D., Schirmacher, E. and Thandi, N. 2007. A practitioner’s guide to generalized linear models. casualty actuarial society. Towers Watson.Search in Google Scholar

Antonio, K., and Valdez, E. A. 2010. Statistical concepts of a priori and a posteriori risk classification. Advances in Statistical Analysis 96 (2): 187-224.10.1007/s10182-011-0152-7Search in Google Scholar

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1984. Classification and regression trees. New York: Chapman and Hall/CRC.Search in Google Scholar

Brockman, M. J. and Wright, T. S. 1992. Statistical motor rating: making effective use of your data. Journal of the Institute of Actuaries 119: 457–543.10.1017/S0020268100019995Search in Google Scholar

Coskun, S. 2016. Introducing credibility theory into GLMs for ratemaking on auto portfolio. Institute de Actuaries, Actuarial thesis. Centre d’Etudes Actuarielles.Search in Google Scholar

Denuit, M., and Lang, S. 2004. Non-life rate-making with Bayesian GAMs. Insurance: Mathematics and Economics 35(3): 627-647.10.1016/j.insmatheco.2004.08.001Search in Google Scholar

Derrig, R. A. and Francis, L. 2006. Distinguishing the forest from the TREES: A Comparison of tree based data mining methods. Casualty Actuarial Society Forum.Search in Google Scholar

Dionne, G. and Vanasse, C. 1988. A generalization of actuarial automobile insurance rating models: The negative binomial distribution with a regression component. ASTIN Bulletin 19 (2): 199-212.10.2143/AST.19.2.2014909Search in Google Scholar

Dionne, G. and Vanasse, C. 1992. Automobile insurance ratemaking in the presence of asymmetrical information. Journal of Applied Econometrics 7(2): 149-165.10.1002/jae.3950070204Search in Google Scholar

Dugas, C., Bengio. Y., N. Chapados, N, Vincent, P., Denoncourt, G. and Fournier, C. 2003. Statistical learning algorithms applied to automobile insurance ratemaking. Casualty Actuarial Society Forum 1(1): 179-214.10.1142/9789812794246_0004Search in Google Scholar

Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. 1996. From data mining to knowledge discovery in databases. AI Magazine 17 (3): 37-54.Search in Google Scholar

Flynn, M., and Francis, L. A. 2009. More flexible GLMs: zero-inflated models and hybrid models. Casualty Actuarial Society E-Forum, 148-224.Search in Google Scholar

Francis, L. 2001. Neural networks demystified. Casualty Actuarial Society Forum, 253-320.Search in Google Scholar

Frees, E. W. and Lee, G. 2016. Rating endorsements using generalized linear models casualty, Actuarial Society, Variance Advancing the Science of Risk 10(1): 51-74.Search in Google Scholar

Garrido, J., Genest, C. and Schulz., J. 2016. Generalized linear models for dependent frequency and severity of insurance claims. Insurance: Mathematics and Economics 70: 205-215.10.1016/j.insmatheco.2016.06.006Search in Google Scholar

Gallagher, C. A., Monroe, H. M. and Fish, J. L. 1990. An iterative approach to classification analysis. Casualty Actuarial Society, 237-281.Search in Google Scholar

Goldburd, M., Khare, A. and Tevet, D. 2016. Generalized linear models for insurance rating. Casualty Actuarial Society, No. 5, 2nd edition.Search in Google Scholar

Guo, L. 2003. Applying data mining techniques in property/casualty insurance. Casualty Actuarial Society Forum. Available at: https://www.casact.org/pubs/forum/03wforum/03wf001.pdfSearch in Google Scholar

Haberman, S. and Renshaw, A. E. 1996. Generalized linear models and actuarial science. The Statistician 45(4): 407-436.10.2307/2988543Search in Google Scholar

Han, J., Kamber, M. and Pei, J. 2012. Data mining concepts and techniques (3rd ed.). Burlington, USA: The Morgan Kaufmann.Search in Google Scholar

Hastie, T., Tibshirani, R. and Friedman, J. 2001. The elements of statistical learning. New York, USA: Springer.10.1007/978-0-387-21606-5Search in Google Scholar

Hilbe, J. M. 2014. Modeling count data. New York: Cambridge University Press.10.1017/CBO9781139236065Search in Google Scholar

de Jong, P. and Heller, G. Z. 2013. Generalized linear models for insurance data (5th ed.). New York: Cambridge University Press.Search in Google Scholar

Kass, G. V. 1980. An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society 29 (2): 119-127.10.2307/2986296Search in Google Scholar

Kaas, R., Goovaerts, M., Dhaene, J., Denuit, M. 2009. Modern Actuarial risk theory, using R. Berlin: Springer.10.1007/978-3-540-70998-5Search in Google Scholar

Kolyshkina, I., Wong, S. and Lim, S. 2004. Enhancing generalised linear models with data mining. casualty actuarial society. Discussion Paper Program.Search in Google Scholar

McCullagh, P. and Nelder, J. A. 1989. Generalized Linear Models (2nd ed.). London: Chapman & Hall.10.1007/978-1-4899-3242-6Search in Google Scholar

Meyers, G. 2007. Estimating loss costs at the address level. PowerPoint presentation at the CAS Predictive Modeling Seminar.Search in Google Scholar

Nelder, J. A. and Wedderburn, R. W. M. 1972. Generalized linear models. Journal of the Royal Statistical Society 135(3): 370–384.10.2307/2344614Search in Google Scholar

Ohlsson, E. and Johansson, B. 2010. Non-life insurance pricing with generalized linear models. Berlin: Springer-Verlag.10.1007/978-3-642-10791-7Search in Google Scholar

Parodi, P. 2014. Pricing in general insurance (1st ed.). New York: Chapman and Hall/CRC.10.1201/b17525Search in Google Scholar

Renshaw, A. E. 1994. Modeling the claims process in the presence of covariates. ASTIN Bulletin 24 (2): 265-285.10.2143/AST.24.2.2005070Search in Google Scholar

SAS Institute. 2000. Data mining in the insurance industry - solving business problems using SAS enterprise miner software.Search in Google Scholar

Shapiro, A. F. and Jain, L. C. 2003. Intelligent and other computational techniques in insurance. world scientific. https://doi.org/10.1142/544110.1142/5441Search in Google Scholar

Sumathi, S. and Sivanandam, S. N. 2006. Introduction to data mining and its applications. Berlin: Springer-Verlag.10.1007/978-3-540-34351-6Search in Google Scholar

Tevet, D. 2013. Exploring model lift: is your model worth implementing?. Actuarial Review 40(2): 10-11.Search in Google Scholar

Quinlan, J. R. 1986. Induction of decision trees. Machine Learning 1: 81-106.10.1007/BF00116251Search in Google Scholar

Quinlan, J. R. 1993. C4.5 Programs for machine learning. Los Altos: Morgan Kaufmann.Search in Google Scholar

Werner, G. and Modlin, C. 2010. Basic ratemaking (4th ed.). Casualty Actuarial Society.Search in Google Scholar

Williams, B., Hansen, G., Baraban, A. and Santoni, A. 2015. A practical approach to variable selection - a comparison of various techniques. Casualty Actuarial Society E-Forum.Search in Google Scholar

Yao, J. 2008. Clustering in ratemaking: with application in territories clustering. Casualty Actuarial Society Discussion Paper Program, 170–192.Search in Google Scholar