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Non-Life Insurance Risk Classification Using Categorical Embedding

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References

  • Bengio, Y., R. Ducharme, P. Vincent, and C. Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research 3:1137–55.
  • Brown, R. L. 1988. Minimum bias with generalized linear models. Proceedings of the Casualty Actuarial Society 75:187–217.
  • Chollet, F. 2015. Keras. https://keras.io
  • Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2 (4):303–14. doi:10.1007/BF02551274
  • de Jong, P., and G. Z. Heller. 2008. Generalized linear models for insurance data. Cambridge: Cambridge University Press.
  • Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–86. Minneapolis, MN: Association for Computational Linguistics.
  • Frees, E. W., X. Jin, and X. Lin. 2013. Actuarial applications of multivariate two-part regression models. Annals of Actuarial Science 7 (2):258–87. doi:10.1017/S1748499512000346
  • Frees, E. W., and G. Lee. 2015. Rating endorsements using generalized linear models. Variance 10 (1):51–74.
  • Frees, E. W., G. Meyers, and A. D. Cummings. 2010. Dependent multi-peril ratemaking models. ASTIN Bulletin 40 (2):699–726.
  • Frees, E. W., G. Meyers, and A. D. Cummings. 2011. Summarizing insurance scores using a Gini index. Journal of the American Statistical Association 106 (495):1085–98. doi:10.1198/jasa.2011.tm10506
  • Frees, E. W., G. Meyers, and A. D. Cummings. 2014. Insurance ratemaking and a Gini index. Journal of Risk and Insurance 81 (2):335–66. doi:10.1111/j.1539-6975.2012.01507.x
  • Frees, E. W., P. Shi, and E. A. Valdez. 2009. Actuarial applications of a hierarchical insurance claims model. ASTIN Bulletin 39 (1):165–97. doi:10.2143/AST.39.1.2038061
  • Glorot, X., A. Bordes, and Y. Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Vol. 15 of Proceedings of Machine Learning Research, eds. G. Gordon, D. Dunson, and M. Dudík, 315–23. Fort Lauderdale, FL: PMLR.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge: MIT Press.
  • Guo, C., and F. Berkhahn. 2016. Entity embeddings of categorical variables. arXiv:1604.06737.
  • Haberman, S., and A. E. Renshaw. 1996. Generalized linear models and actuarial science. Journal of the Royal Statistical Society: Series D (The Statistician) 45 (4):407–36.
  • Kim, Y. 2014. Convolutional neural networks for sentence classification. In Proceedings of Conference on Empirical Methods in Natural Language Processing, 1746–51. Doha, Qatar: Association for Computational Linguistics.
  • Klinker, F. 2011. Generalized linear mixed models for ratemaking: A means of introducing credibility into a generalized linear model setting. Casualty Actuarial Society E-Forum 2:1–25.
  • Lai, S., L. Xu, K. Liu, and J. Zhao. 2015. Recurrent convolutional neural networks for text classification. In Twenty-ninth AAAI Conference on Artificial Intelligence, 2267–73. Austin, TX: AAAI Press. doi:10.1609/aaai.v29i1.9513
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi:10.1038/nature14539
  • Lee, G. Y., S. Manski, and T. Maiti. 2020. Actuarial applications of word embedding models. ASTIN Bulletin 50 (1):1–24. doi:10.1017/asb.2019.28
  • Llanas, B., S. Lantarón, and F. J. Sáinz. 2008. Constructive approximation of discontinuous functions by neural networks. Neural Processing Letters 27 (3):209–26. doi:10.1007/s11063-007-9070-9
  • McCulloch, W. S., and W. Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (4):115–33. doi:10.1007/BF02478259
  • Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781.
  • Mildenhall, S. J. 1999. A systematic relationship between minimum bias and generalized linear models. Proceedings of the Casualty Actuarial Society 86:393–487.
  • Ohlsson, E., and B. Johansson. 2006. Exact credibility and Tweedie models. ASTIN Bulletin 36 (1):121–33. doi:10.1017/S0515036100014422
  • Pan, S. J., and Q. Yang. 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22 (10):1345–59. doi:10.1109/TKDE.2009.191
  • Perla, F., R. Richman, S. Scognamiglio, and M. V. Wüthrich. 2021. Time-series forecasting of mortality rates using deep learning. Scandinavian Actuarial Journal 2021:1–27. doi:10.1080/03461238.2020.1867232
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning representations by back-propagating errors. Nature 323 (6088):533–36. doi:10.1038/323533a0
  • Schmidhuber, J. 2015. Deep learning in neural networks: An overview. Neural Networks 61:85–117. doi:10.1016/j.neunet.2014.09.003
  • Shi, P., X. Feng, and J.-P. Boucher. 2016. Multilevel modeling of insurance claims using copulas. The Annals of Applied Statistics 10 (2):834–63. doi:10.1214/16-AOAS914
  • Shi, P., and J. Guszcza. 2016. Frameworks for general insurance ratemaking: Beyond the generalized linear model. In Predictive modeling applications in actuarial science: Vol. II. Case studies in insurance, ed. E. Edward, G. Meyers, and R. A. Derrig, 100–125. Cambridge: Cambridge University Press.
  • Shi, P., and K. Shi. 2017. Territorial risk classification using spatially dependent frequency-severity models. ASTIN Bulletin 47 (2):437–65. doi:10.1017/asb.2017.7
  • Shi, P., and L. Yang. 2018. Pair copula constructions for insurance experience rating. Journal of the American Statistical Association 113 (521):122–33. doi:10.1080/01621459.2017.1330692
  • Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu. 2018. A survey on deep transfer learning. In International Conference on Artificial Neural Networks, ed. V. Kårková, Y. Manolopoulos, B. Hammer, L. Iliadis, and I. Maglogiannis, 270–79. Cham: Springer International Publishing.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, eds. I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, vol. 30, 5998–6008. Red Hook, NY: Curran Associates, Inc.
  • Vincent, G., C. Arthur, L. Sylvain, and D. Marcin. 2022. A fair pricing model via adversarial learning. arXiv preprint arXiv:2202.12008.
  • Werner, G., and C. Modlin. 2016. Basic ratemaking. Cambridge: MIT Press.
  • Yang, L., and P. Shi. 2019. Multiperil rate making for property insurance using longitudinal data. Journal of the Royal Statistical Society: Series A (Statistics in Society) 182 (2):647–68. doi:10.1111/rssa.12419
  • Zhao, Z., P. Shi, and X. Feng. 2021. Knowledge learning of insurance risks using dependence models. INFORMS Journal on Computing 33 (3):1177–96. doi:10.1287/ijoc.2020.1005

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