How to cite this paper
Hessami, F. (2018). Business risk evaluation and management of Iranian commercial insurance companies.Management Science Letters , 8(2), 91-102.
Refrences
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Ansah-Adu, K., Andoh, C., & Abor, J. (2011). Evaluating the cost efficiency of insurance companies in Ghana. The Journal of Risk Finance, 13(1), 61-76.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of hydrologic engineering, 5(2), 115-123.
Doskočil, R. (2017). Evaluating the Creditworthiness of a Client in the Insurance Industry Using Adaptive Neuro-Fuzzy Inference System. Engineering Economics, 28(1), 15-24.
Ebrat, M., & Ghodsi, R. (2014). Construction project risk assessment by using adaptive-network-based fuzzy inference system: An empirical study. KSCE Journal of Civil Engineering, 18(5), 1213-1227.
Fathi, M., & & Zamani Osgooy, F. (2014). Advanced MATLAB Programming with GUI and C/C++, Nashr-e Oloom Kanoon
Ghorbani Golsefidi, H. (2016). Solutions for Designing a Risk Evaluation model in Health Insurances Using OLAP (Mellat Insurance), a Master’s Thesis, Payame Noor University (Ministry of Science, Research, and Technology), Payame Noor University of Tehran Province, Tehran West Branch.
Gocić, M., Motamedi, S., Shamshirband, S., Petković, D., & Hashim, R. (2015). Potential of adaptive neuro-fuzzy inference system for evaluation of drought indices. Stochastic environmental research and risk assessment, 29(8), 1993-2002.
Goodarzi, M., & Freitas, M. P. (2010). MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcrip-tase activities of TIBO derivatives. European journal of medicinal chemistry, 45(4), 1352-1358.
Guo, F., & Huang, Y. S. (2013). Identifying permanent and transitory risks in the Chinese property insurance market. The North American Journal of Economics and Finance, 26, 689-704.
Jamshidi, M. (2003). Tools for intelligent control: fuzzy controllers, neural networks and genetic algo-rithms. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 361(1809), 1781-1808.
Hanafi Zadeh, P., & Rastakhiz, N. (2011). A model for classifying the risks of car insurance custom-ers based on risk using data mining techniques, Insurance Research Journal, 2, 55-81.
Karimi, S. M. (2013). Evaluating Iran’s insurance industry and explaining the future outlook. The Seasonal Journal of Economic and Financial Policies, Special Issue on the State’s Economic Rec-ord, 2, 183-202
Lu, Z., Liu, L., Zhang, J., & Meng, L. (2012). Optimal insurance under multiple sources of risk with positive dependence. Insurance: Mathematics and Economics, 51(2), 462-471.
Manouchehri, M. (2009). The Role of Risk in the Insurance Contracts of Individuals, Kanoon Month-ly Journal, 94.
Momeni, M., & Ghayoomi, A. (2011). Statistical Analyses in SPSS, Moalef.
Mousavi, M., Kavian Pour, J., & Sirfian Pour, H. (2009). A fuzzy expert system for risk management of projects. The Fifth International Conference on Project Management.
Nazeri, A. (2013). Developing a risk reduction model in Iran’s car insurance industry using data min-ing techniques, A Master’s Thesis, Faculty of Engineering, Tarbiat Modares University, Ministry of Science, Research, and Technology.
Niemeyer, A. (2015). Safety margins for systematic biometric and financial risk in a semi-Markov life insurance framework. Risks, 3(1), 35-60.
Nikolić, V., Petković, D., Por, L. Y., Shamshirband, S., Zamani, M., Ćojbašić, Ž., & Motamedi, S. (2016). Potential of neuro-fuzzy methodology to estimate noise level of wind turbines. Mechanical Systems and Signal Processing, 66, 715-722.
Nilosey, A. (2016). FPGA based diabetic patient health monitoring using fuzzy neural network. Inter-national Journal of Science and Research, 5(10), 394-396.
Paton, B., Bahna-Nolan, M., Isherwood, J., Scheinerman, D., Schlinsog, J., & Sen, S. (2015). Life in-surance regulatory structures and strategy EU compared with US, a preliminary survey. Centre for risk and insurance studies, Notingham Univercity Business School. pp. 1-92.
Pérez-Gandía, C., Facchinetti, A., Sparacino, G., Cobelli, C., Gómez, E. J., Rigla, M., ... & Hernando, M. E. (2010). Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes technology & therapeutics, 12(1), 81-88.
Riahi-Madvar, H., Ayyoubzadeh, S. A., Khadangi, E., & Ebadzadeh, M. M. (2009). An expert sys-tem for predicting longitudinal dispersion coefficient in natural streams by using ANFIS. Expert Systems with Applications, 36(4), 8589-8596.
Sehat, S. V., & Alavi, S. (2010). The necessity of using risk management knowledge in the third-party insurance and the effect of the new law of the third-party insurance on the relevant risk. The Monthly Journal of News on the World of Insurance, No. 145.
Shahriar, B. (2014). Risk management basics and monitoring insolvency of insurance companies, In-surance Research Center (Affiliated with Central Insurance of Iran)
Soleymani, S., & Sadeghi Shahdani, Fatanat, M. (2014). Designing an insurance joint investment bas-ket to increase reassurance capacity. The Scientific and Research Seasonal Journal of Investment Knowledge, Iranian Financial Engineering Association, 3(9).
Tao, C. W., Taur, J., Chang, J. H., & Su, S. F. (2010). Adaptive fuzzy switched swing-up and sliding control for the double-pendulum-and-cart system. IEEE Transactions on Systems, Man, and Cy-bernetics, Part B (Cybernetics), 40(1), 241-252.
Zadeh, L. A. (2007). Fuzzy logic as the logic of natural languages. In Analysis and Design of Intelli-gent Systems Using Soft Computing Techniques (pp. 1-2). Springer, Berlin, Heidelberg.
Ansah-Adu, K., Andoh, C., & Abor, J. (2011). Evaluating the cost efficiency of insurance companies in Ghana. The Journal of Risk Finance, 13(1), 61-76.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of hydrologic engineering, 5(2), 115-123.
Doskočil, R. (2017). Evaluating the Creditworthiness of a Client in the Insurance Industry Using Adaptive Neuro-Fuzzy Inference System. Engineering Economics, 28(1), 15-24.
Ebrat, M., & Ghodsi, R. (2014). Construction project risk assessment by using adaptive-network-based fuzzy inference system: An empirical study. KSCE Journal of Civil Engineering, 18(5), 1213-1227.
Fathi, M., & & Zamani Osgooy, F. (2014). Advanced MATLAB Programming with GUI and C/C++, Nashr-e Oloom Kanoon
Ghorbani Golsefidi, H. (2016). Solutions for Designing a Risk Evaluation model in Health Insurances Using OLAP (Mellat Insurance), a Master’s Thesis, Payame Noor University (Ministry of Science, Research, and Technology), Payame Noor University of Tehran Province, Tehran West Branch.
Gocić, M., Motamedi, S., Shamshirband, S., Petković, D., & Hashim, R. (2015). Potential of adaptive neuro-fuzzy inference system for evaluation of drought indices. Stochastic environmental research and risk assessment, 29(8), 1993-2002.
Goodarzi, M., & Freitas, M. P. (2010). MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcrip-tase activities of TIBO derivatives. European journal of medicinal chemistry, 45(4), 1352-1358.
Guo, F., & Huang, Y. S. (2013). Identifying permanent and transitory risks in the Chinese property insurance market. The North American Journal of Economics and Finance, 26, 689-704.
Jamshidi, M. (2003). Tools for intelligent control: fuzzy controllers, neural networks and genetic algo-rithms. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 361(1809), 1781-1808.
Hanafi Zadeh, P., & Rastakhiz, N. (2011). A model for classifying the risks of car insurance custom-ers based on risk using data mining techniques, Insurance Research Journal, 2, 55-81.
Karimi, S. M. (2013). Evaluating Iran’s insurance industry and explaining the future outlook. The Seasonal Journal of Economic and Financial Policies, Special Issue on the State’s Economic Rec-ord, 2, 183-202
Lu, Z., Liu, L., Zhang, J., & Meng, L. (2012). Optimal insurance under multiple sources of risk with positive dependence. Insurance: Mathematics and Economics, 51(2), 462-471.
Manouchehri, M. (2009). The Role of Risk in the Insurance Contracts of Individuals, Kanoon Month-ly Journal, 94.
Momeni, M., & Ghayoomi, A. (2011). Statistical Analyses in SPSS, Moalef.
Mousavi, M., Kavian Pour, J., & Sirfian Pour, H. (2009). A fuzzy expert system for risk management of projects. The Fifth International Conference on Project Management.
Nazeri, A. (2013). Developing a risk reduction model in Iran’s car insurance industry using data min-ing techniques, A Master’s Thesis, Faculty of Engineering, Tarbiat Modares University, Ministry of Science, Research, and Technology.
Niemeyer, A. (2015). Safety margins for systematic biometric and financial risk in a semi-Markov life insurance framework. Risks, 3(1), 35-60.
Nikolić, V., Petković, D., Por, L. Y., Shamshirband, S., Zamani, M., Ćojbašić, Ž., & Motamedi, S. (2016). Potential of neuro-fuzzy methodology to estimate noise level of wind turbines. Mechanical Systems and Signal Processing, 66, 715-722.
Nilosey, A. (2016). FPGA based diabetic patient health monitoring using fuzzy neural network. Inter-national Journal of Science and Research, 5(10), 394-396.
Paton, B., Bahna-Nolan, M., Isherwood, J., Scheinerman, D., Schlinsog, J., & Sen, S. (2015). Life in-surance regulatory structures and strategy EU compared with US, a preliminary survey. Centre for risk and insurance studies, Notingham Univercity Business School. pp. 1-92.
Pérez-Gandía, C., Facchinetti, A., Sparacino, G., Cobelli, C., Gómez, E. J., Rigla, M., ... & Hernando, M. E. (2010). Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes technology & therapeutics, 12(1), 81-88.
Riahi-Madvar, H., Ayyoubzadeh, S. A., Khadangi, E., & Ebadzadeh, M. M. (2009). An expert sys-tem for predicting longitudinal dispersion coefficient in natural streams by using ANFIS. Expert Systems with Applications, 36(4), 8589-8596.
Sehat, S. V., & Alavi, S. (2010). The necessity of using risk management knowledge in the third-party insurance and the effect of the new law of the third-party insurance on the relevant risk. The Monthly Journal of News on the World of Insurance, No. 145.
Shahriar, B. (2014). Risk management basics and monitoring insolvency of insurance companies, In-surance Research Center (Affiliated with Central Insurance of Iran)
Soleymani, S., & Sadeghi Shahdani, Fatanat, M. (2014). Designing an insurance joint investment bas-ket to increase reassurance capacity. The Scientific and Research Seasonal Journal of Investment Knowledge, Iranian Financial Engineering Association, 3(9).
Tao, C. W., Taur, J., Chang, J. H., & Su, S. F. (2010). Adaptive fuzzy switched swing-up and sliding control for the double-pendulum-and-cart system. IEEE Transactions on Systems, Man, and Cy-bernetics, Part B (Cybernetics), 40(1), 241-252.
Zadeh, L. A. (2007). Fuzzy logic as the logic of natural languages. In Analysis and Design of Intelli-gent Systems Using Soft Computing Techniques (pp. 1-2). Springer, Berlin, Heidelberg.