[1] Shapiro, A.F.: An overview of insurance uses of fuzzy logic. In: Chen, S.-H., Wang, P.P., Kuo, T.W. (eds.) Computational Intelligence in Economics and Finance, pp. 25–61. Springer, Heidelberg (2007)

[2] Shapiro, F.: Fuzzy logic in insurance. Insur. Math. Econ. 35(2), 399–424 (2004)

[3] Shapiro, F.: Insurance applications of neural networks, fuzzy logic, and genetic algorithms. In: Intelligent and Other Computational Techniques in Insurance: Theory and Applications (2003)

[4] Ana-Maria, B., Ghiorghe, B.: Application of autoregressive models for forecasting marine insurance market. Ovidius Univ. Ann. Ser. Econ. Sci. 13(1), 1125–1129 (2013)

[5] De Alba, E., Nieto-Barajas, L.E.: Claims reserving: a correlated Bayesian model. Ins. Math. Econ. 43, 368–376 (2008)

[6] De Alba, E.: Bayesian estimation of outstanding claims reserves. N. Am. Act. J. 6(4), 1–20 (2002)

[7] De Alba, E.: Claims reserving when there are negative values in the runoff triangle: Bayesian analysis using the three-parameter log-normal distribution. N. Am. Act. J. 10(3), 1–15 (2006)

[8] Gaver, J.J., Paterson, J.S.: Do insurers manipulate loss reserves to mask insolvency problems? J. Acc. Econ. 37, 393–416 (2004)

[9] Antonio, K., Beirlant, J.: Issues in claims reserving and credibility: a semiparametric approach with mixed models. J. Risk Ins. 75, 643–676 (2008)

[10] Bernoth, K., Pick, A.: Forecasting the fragility of the banking and insurance sectors. J. Bank. Finance 35(4), 807–818 (2011)

[11] Abdullah, L., Rahman, M.N.A.: Employee likelihood of purchasing health insurance using fuzzy inference system. Int. J. Comput. Sci. 9(1), 112–116 (2012)

[12] Son, L.H., Linh, N.D., Long, H.V.: A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network. Eng. Appl. Artif. Intell. 29, 33–42 (2014)

[13] Martínez-Miranda, M.D., Nielsen, J.P., Wüthrich, M.V.: Statistical modelling and forecasting of outstanding liabilities in non-life insurance. SORT 36(2), 195–218 (2012)

[14] Wüthrich, M.V., Merz, M.: Stochastic Claims Reserving Methods in Insurance. John Wiley & Sons, West Sussex (2008)

[15] Siddique, N., Adeli, H.: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. John Wiley & Sons (2013)

[16] England, P.D., Verrall, R.J.: Stochastic claims reserving in general insurance. Br. Act. J. 8, 443–518 (2002)

[17] Mulquiney, P., et al.: Artificial Neural Networks in Insurance Loss Reserving (2011)

[18] Mack, T.: Measuring the Variability of Chain Ladder Reserve Estimates, Casualty Actuarial Society Forum, pp. 101–182 (1994)

[19] Kaymak, U.: Defining a financial forecasting model for healthcare insurance companies, Eindhoven University of Technology (2013)

[20] UCI Machine Learning Repository, Insurance Company Benchmark (COIL 2000) Data Set (2015). https://archive.ics.uci.edu/ml/datasets/Insurance+Company+Benchmark+(COIL+2000)

[21] Beaver, W.H., McNichols, M.F., Nelson, K.K.: Management of the loss reserve accrual and the distribution of earnings in the property-casualty insurance industry. J. Acc. Econ. 35, 347–376 (2003)

[22] Zhang, Y., Dukic, V., Guszcza, J.: A Bayesian non-linear model for forecasting insurance loss payments. J. R. Stat. Soc. Ser. A (Stat. Soc.) 175(2), 637–656 (2012)