Using Fuzzy Interest Rates for Uncertainty Modelling in Enhanced Annuities Pricing
الموضوعات : فصلنامه ریاضی
1 - Personal Insurance Research Group, Insurance Research Center, Tehran, Iran
الکلمات المفتاحية: Fuzzy Set Theory, Adjustment multiplier Interest rate, Enhanced annuities, Single premium,
ملخص المقالة :
The modeling of uncertainty resources is very important in insurance pricing. In this paper, fuzzy set theory is implemented to model interest rates as an uncertainty resources for calculating the price of enhanced annuities. In this regard, the single fuzzy premium for a fixed annuity payouts is calculated using adjusted mortality probabilities for an insured with health problems and the results are compared with standard status. As the adjustment multiplier increases, which means that the health problems of the insured are worse, the life expectancy of the person decreases. In addition, as adjustment multiplier increases, the insurance premium decreases, which is due to the adjustment of survival and mortality probabilities based on the individual's health status. Also, to show the validity of the proposed fuzzy method, the random interest rate has been used. The results of the fuzzy and random models are close to each other which indicates the validation of proposed method.
[1] M. Aalaei, Pricing life insurance products in Iran using fuzzy interest rates, Iranian Journal of
Insurance Research, 37 (1) (2022) 43–78.
[2] American Cancer Society, Early Detection, Diagnosis, and Staging, (2022), Available online:
https://www.cancer.org/content/dam/CRC/PDF/Public/8825.00.pdf (accessed on 10 September
2022).
[3] P. L. Brockett, S. L. Chuang, Y. Deng and R. D. MacMinn, Incorporating longevity risk and medical
information into life settlement pricing, Journal of Risk and Insurance, 80 (3) (2013) 799–826.
[4] V. Dedes, How to determine fair value for life insurance policies in a secondary market, (2011).
[5] D. C. M. Dickson, M. R. Hardy and H. R. Waters, Actuarial Mathematics for Life Contingent Risks,
International Series on Actuarial Science, Cambridge University Press, (2013).
[6] V. F. Dolan, Advantages of a Life Expectancy Using Life Insurance Underwriting and Life Settlement Methods in the Legal Setting, (2020), Available online:
https://www.experts.com/content/articles/Vera-Dolan-Life-Expectancy.pdf.
[7] M. Drinkwater, J. E. Montminy, E. T. Sondergeld, C. G. Raham and C. R. Runchey, Substandard
annuities, Technical Report, LIMRA International Inc. and the Society of Actuaries, in Collaboration n
274 M. Aalaei/ IJM2C, 12 - 04 (2022) 265-274.
with Ernst & Young LLP, (2006), Available online: https://www.Soa.org/Files/Research/007289-
Substandard-annuities-full-rpt-REV-8-21.pdf (accessed on 10 January 2022).
[8] N. Gatzert, G. Hoermann and H. Schmeiser, Optimal risk classification with an application to
substandard annuities, North American Actuarial Journal, 16 (4) (2012) 462–486.
[9] N. Gatzert and U. Klotzki, Enhanced annuities: Drivers of and barriers to supply and demand, The
Geneva Papers on Risk and Insurance-Issues and Practice, 41 (1) (2016) 53–77.
[10] G. Hoermann and R. Ruß, Enhanced annuities and the impact of individual underwriting on an
insurers profit situation, Insurance: Mathematics and Economics, 43 (1)(2008) 150–157.
[11] J. A. Sanches, Fuzzy claim reserving in non-life insurance, Computer Science and Information Systems, 11 (2) (2014) 825–838.
[12] J. A. Sanches and L. G. Puchades, Enhanced annuities as a complement to the public retirement
pension: analysis of their implementation in Spain, Revista Galega de Econom´ıa, 29 (3) (2020) 1–19,
(In Spain).
[13] J. A. Sanches and L. G. Puchades, Life settlements: descriptive analysis and quantitative aspects,
Management Letters, 21 (2) (2021) 19–34.
[14] J. A. Sanches and L. G. Puchades, Some computational results for the fuzzy random value of life
actuarial liabilities, Iranian Journal of Fuzzy Systems, 14 (4) (2017) 1–25.
[15] J. A. Sanches and L. G. Puchades, The valuation of life contingencies: A symmetrical triangular
fuzzy approximation, Insurance: Mathematics and Economics 72 (2017) 83–94.
[16] J. A. Sanches and L. G. Puchades, Using fuzzy random variables in life annuities pricing, Fuzzy Sets
and Systems, 188 (2012) 27–44.
[17] J. A. Sanches, L. G. Puchades and A. Zhang, Incorporating Fuzzy Information in Pricing Substandard Annuities, Computers & Industrial Engineering, 145 (2020) 1–10.
[18] A. F. Shapiro, Modeling future lifetime as a fuzzy random variable, Insurance: Mathematics and
Economics, 53 (2013) 864–870.
[19] B. S. Thomson, Monotone convergence theorem for the Riemann integral,The American Mathematical Monthly, 117 (6) (2010) 547–550.
[20] D. Wang, A net premium model for life insurance under a sort of generalized uncertain interest
rates, In: S. Destercke, T. Denoeux, M. Gil, P. Grzegorzewski and O. Hryniewicz (eds), Uncertainty
Modelling in Data Science, SMPS 2018, Advances in Intelligent Systems and Computing, Springer,
Cham, 832 (2019) 224–232.
[21] J. Xu, Dating death: An empirical comparison of medical underwriters in the US life settlements
market, North American Actuarial Journal, 24 (1) (2020) 36–56