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Journal articles on the topic 'Life insurance – Mathematics'

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Published: 4 June 2021

Last updated: 29 January 2023

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1

Carroll, Patrick, and E. Straub. "Non-Life Insurance Mathematics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 153, no. 2 (1990): 262. http://dx.doi.org/10.2307/2982815.

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2

Denuit, Michel, and Esther Frostig. "Life Insurance Mathematics with Random Life Tables." North American Actuarial Journal 13, no. 3 (July 2009): 339–55. http://dx.doi.org/10.1080/10920277.2009.10597560.

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3

Ruohonen, Matti. "Non-Life Insurance Mathematics (Erwin Straub)." SIAM Review 32, no. 1 (March 1990): 184–85. http://dx.doi.org/10.1137/1032031.

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4

Hald, Anders. "On the early history of life insurance mathematics." Scandinavian Actuarial Journal 1987, no. 1-2 (January 1987): 4–18. http://dx.doi.org/10.1080/03461238.1987.10413813.

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5

Scadden, R. W. "Non-Life Insurance Mathematics. By Erwin Straub. (Springer-Verlag.)." Journal of the Institute of Actuaries 116, no. 2 (September 1989): 297. http://dx.doi.org/10.1017/s0020268100036611.

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6

Dong, Yang, Hao Wang, and Lihong Zhang. "Stock Return Uncertainty and Life Insurance." Mathematical Problems in Engineering 2020 (July 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/1835146.

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Knightian uncertainty embedded in stock returns causes rising demand for life insurance, as the uncertainty averse agent seeks alternative investment channels. Life insurance demand of middle-aged agent is more sensitive to the uncertainty. Stock return uncertainty reduces the agent’s total wealth and subsequently the propensity of wealthy agent serving as an insurance seller. Rising demand and falling supply of life insurance imply that life insurance is more expensive in the presence of stock return uncertainty. Sensitivity of life insurance demand to the mortality rate and key stock return characteristics also changes with the uncertainty.

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7

Fauziah, Irma. "PERHITUNGAN PREMI ASURANSI JIWA DWIGUNA PASUTRI SEBAGAI PENERAPAN PEMBELAJARAN MATEMATIKA EKONOMI." Phenomenon : Jurnal Pendidikan MIPA 3, no. 1 (February 25, 2016): 99. http://dx.doi.org/10.21580/phen.2013.3.1.177.

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<p>In learning mathematical economics, the calculation of life insurance premiums is a matter concerning the application of a combination of compound interest, probability, differential and integral. Life insurance with multilife concept is the one of ap- plied in actuarial mathematics. A functions, in the actuarial cal- culation, related to death sequence in multilife concept is called as contingent function. Usage that function in calculation of insurance premium will assist the insurer in giving the benet precisely.<br />Contingent probabilities are resulted by multiplication be- tween the force of mortality of life in the last sequence of death which have been determined and probabilities of life all family member in multilife status. Insurance formulation is obtained by mutiplying this probabilities with <em>v</em>t discount factor and they are integrated by using the assumption of a uniform distribution of death throughout the year of age.</p>

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8

Schmidli, Hanspeter. "Optimisation in Non-Life Insurance." Stochastic Models 22, no. 4 (November 22, 2006): 689–722. http://dx.doi.org/10.1080/15326340600878420.

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9

Bacinello, Anna Rita, Enrico Biffis, and Pietro Millossovich. "Pricing life insurance contracts with early exercise features." Journal of Computational and Applied Mathematics 233, no. 1 (November 2009): 27–35. http://dx.doi.org/10.1016/j.cam.2008.05.036.

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10

Biagini, Francesca, and Irene Schreiber. "Risk-Minimization for Life Insurance Liabilities." SIAM Journal on Financial Mathematics 4, no. 1 (January 2013): 243–64. http://dx.doi.org/10.1137/110856836.

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11

Yi, Zhang, and He Wenjiong. "Dual random model of increasing life insurance for multiple-life status." Applied Mathematics-A Journal of Chinese Universities 17, no. 3 (September 2002): 319–25. http://dx.doi.org/10.1007/s11766-002-0011-0.

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12

Steffensen, Mogens. "Quadratic Optimization of Life and Pension Insurance Payments." ASTIN Bulletin 36, no. 01 (May 2006): 245–67. http://dx.doi.org/10.2143/ast.36.1.2014151.

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Quadratic optimization is the classical approach to optimal control of pension funds. Usually the payment stream is approximated by a diffusion process. Here we obtain semiexplicit solutions for quadratic optimization in the case where the payment process is driven by a finite state Markov chain model commonly used in life insurance mathematics. The optimal payments are affine in the surplus with state dependent coefficients. Also constraints on payments and surplus are studied.

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13

Steffensen, Mogens. "Quadratic Optimization of Life and Pension Insurance Payments." ASTIN Bulletin 36, no. 1 (May 2006): 245–67. http://dx.doi.org/10.1017/s0515036100014471.

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Quadratic optimization is the classical approach to optimal control of pension funds. Usually the payment stream is approximated by a diffusion process. Here we obtain semiexplicit solutions for quadratic optimization in the case where the payment process is driven by a finite state Markov chain model commonly used in life insurance mathematics. The optimal payments are affine in the surplus with state dependent coefficients. Also constraints on payments and surplus are studied.

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14

Roenganan, Sorrawee, Masnita Misran, and Nattakorn Phewchean. "A Study of Life Internal Rate of Return." WSEAS TRANSACTIONS ON MATHEMATICS 20 (April 2, 2021): 122–33. http://dx.doi.org/10.37394/23206.2021.20.13.

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Life insurance, not included as a part of the legal obligation in some countries, is one of the investment approaches that might not stand high in the public favor for some people since this is a type of investments that the investor cannot know beforehand the exact return, and the returns completely depend on uncertainty of the policy specification in some circumstances. Similar to the other kinds of investment, investors in life insurance products have been seeking a tool for investment evaluation. However, currently there are no accurate tools that can provide the value of the investment in a life insurance product sensitive to the uncertainty. Internal rate of return is the basic tool that buyers or bankers may apply in order to find the rate of return of this type of investment. The investment decision tool is one of the most important keys that investors have utilized upon making their decisions on investments. Therefore, in this research, we propose a new mathematical model with applications for investment decision, being an extension of the internal rate of return by taking into account the life probability, considering different types of life insurance policies, and other factors specified on life insurance investments such as the premium, the death benefit, the maturity value, the sum insured, the lapse rate, the surrender value, the annuity certain, and the lapse rate with different genders and ages. This newly proposed model is named as the "Life Internal Rate of Return" or Life-IRR model. By using the sample data for both males and females aged 30 years old with expected benefit of 100,000 baht for different types of life insurance policies which are endowment plan, whole life plan and retirement plan, the results show that, for males, the highest life rate of returns is that obtained from the retirement plan (3.633692%), and the lowest life internal rates of returns is that obtained from the endowment plan (2.384443%), while the whole life plan offers moderate life rate of returns of 2.427941%. For females, the highest life rate of returns is that obtained from the retirement plan (3.335189%), and the lowest life internal rates of returns is that obtained from the whole life plan (2.104658%), while the endowment plan offers moderate life rate of returns of 2.308062%. The sensitivity analyses of the life internal rates of return perform the natural characteristics of life insurance.

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15

Et. al., Asha Rani,. "Multi Criteria Decision Making (MCDM) based preference elicitation framework for life insurance recommendation system." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1848–58. http://dx.doi.org/10.17762/turcomat.v12i2.1523.

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The global life insurance industry has shown a phenomenal growth in number of companies, insurance products and their users. The digital revolution has played a pivotal role in the field of insurance too. Increased numbers of companies and insurance plans have increased the complexities and time involved in selection of appropriate policies. At present, major share of policy selling goes to the agents which may be biased and time consuming. The web aggregators too have failed to provide customized and personalized suggestions. Major portion of population still finds the selection of best insurance plan unfriendly and tedious. This huge volume of data requires intelligent system to facilitate efficient and effective retrieval, processing and management of the data from multiple dimensions. This research paper proposes a framework to provide a personalized life insurance recommender system using TOPSIS method of multi-criteria decision making. Point allocation method along with TOPSIS provides preference elicitation and list of recommended policies ranked according to closeness coefficients. Sensitivity analysis in the paper shows the effect of changing the policy features’ preferences (criteria weights) on the final recommended products. The proposed framework helps in achieving computational excellence for efficient decision making with reduced complexity

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16

Waters, H. R. "Life Insurance Mathematics (Second Edition). By H.U. Gerber (Springer, Berlin, 1995) £34." British Actuarial Journal 3, no. 1 (April 1, 1997): 250–51. http://dx.doi.org/10.1017/s1357321700005390.

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17

ASCHENWALD, DIETER, THOMAS SIEGL, and ROBERT F. TICHY. "MAPinsure—A MAPLE Package for Life Insurance." Journal of Symbolic Computation 22, no. 2 (August 1996): 227–34. http://dx.doi.org/10.1006/jsco.1996.0050.

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18

Lemaire, Jean. "Fuzzy Insurance." ASTIN Bulletin 20, no. 1 (April 1990): 33–55. http://dx.doi.org/10.2143/ast.20.1.2005482.

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AbstractFuzzy set theory is a recently developed field of mathematics, that introduces sets of objects whose boundaries are not sharply defined. Whereas in ordinary Boolean algebra an element is either contained or not contained in a given set, in fuzzy set theory the transition between membership and non-membership is gradual. The theory aims at modelizing situations described in vague or imprecise terms, or situations that are too complex or ill-defined to be analysed by conventional methods. This paper aims at presenting the basic concepts of the theory in an insurance framework. First the basic definitions of fuzzy logic are presented, and applied to provide a flexible definition of a “preferred policyholder” in life insurance. Next, fuzzy decision-making procedures are illustrated by a reinsurance application, and the theory of fuzzy numbers is extended to define fuzzy insurance premiums.

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19

Gautama So, Idris, and Rosi Yosevie. "E-Proposal to Expedite Customer's Decision Making on Committing Insurance Transaction." Advanced Science Letters 21, no. 4 (April 1, 2015): 612–17. http://dx.doi.org/10.1166/asl.2015.5985.

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Objectives of this paper are to analyze the current system of policies selling in a particular Life Insurance company, especially for unit link life insurance product to understand the problems or gaps in the company. According to the result of Business Inteligence (Analysis of Insurance Industry) from the particular Life Insurance, assets of the company are big enough to do some investments in technology to gain more premium income, but the fact is premium income of said Life Insurance company was not good enough if it is compared with its competitors. So based on that problem, it is needed to find the requirements of new system as a solution, then the design a new web-based application of e-proposal to customer's decision making of the Life Insurance company. Methodology which is used in this research are colecting data by library research and field research. After that, analysis based on the theory Fishbone Diagram Analysis, Critical Success Factor Analysis, and Object Oriented Analysis will be carried out. On the design phase, Object Oriented Design is used and then evaluate the result of a new system design by Eight Golden Rules of designing user interface. Result of this research is a web based application to make the distribution of proposal faster and more simple. The procedure of new system will also make the information of policies benefit can be easily accessed by Customer and Insurance Agencies, so the company will also gain customer satisfaction and loyalty. In the end, we can conclude that e-proposal is the right solution for the company to drive customer's decision making and finally will improve Life Insurace company's product selling and also increase the premium income.

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20

Pavić Kramarić, Tomislava, Maja Pervan, and Marijana Ćurak. "Determinants of Croatian Non-Life Insurance Companies’ Efficiency." Croatian operational research review 13, no. 2 (December 22, 2022): 149–60. http://dx.doi.org/10.17535/crorr.2022.0011.

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Although a relatively large number of studies have been focused on evaluating the efficiency of insurance companies from different aspects, analysis of factors that determine the achieved level of insurers’ efficiency is still in their inception. While these studies primarily encompass insurance companies operating in developed insurance markets, such research based on the sample of Croatian non-life insurers does not exist. Therefore, this paper is focused on the efficiency drivers of the insurance companies that operate in the Croatian non-life insurance market. The research is based on data for 18 insurance companies in the period from 2009 to 2021. Applying Data envelopment analysis (DEA) and Truncated regression, the research results show that age and ownership influence the efficiency of non-life insurance companies in Croatia, while the companies’ size, leverage, and product diversification are not confirmed as significant determinants of the efficiency.

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21

Vandaele, Nele, and Michèle Vanmaele. "Explicit portfolio for unit-linked life insurance contracts with surrender option." Journal of Computational and Applied Mathematics 233, no. 1 (November 2009): 16–26. http://dx.doi.org/10.1016/j.cam.2008.04.031.

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22

Sigmund, Karl. "Failing phoenix: Tauber, helly, and viennese life insurance." Mathematical Intelligencer 26, no. 2 (March 2004): 21–33. http://dx.doi.org/10.1007/bf02985648.

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23

Villacorta, Pablo J., Laura González-Vila Puchades, and Jorge de Andrés-Sánchez. "Fuzzy Markovian Bonus-Malus Systems in Non-Life Insurance." Mathematics 9, no. 4 (February 9, 2021): 347. http://dx.doi.org/10.3390/math9040347.

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Markov chains (MCs) are widely used to model a great deal of financial and actuarial problems. Likewise, they are also used in many other fields ranging from economics, management, agricultural sciences, engineering or informatics to medicine. This paper focuses on the use of MCs for the design of non-life bonus-malus systems (BMSs). It proposes quantifying the uncertainty of transition probabilities in BMSs by using fuzzy numbers (FNs). To do so, Fuzzy MCs (FMCs) as defined by Buckley and Eslami in 2002 are used, thus giving rise to the concept of Fuzzy BMSs (FBMSs). More concretely, we describe in detail the common BMS where the number of claims follows a Poisson distribution under the hypothesis that its characteristic parameter is not a real but a triangular FN (TFN). Moreover, we reflect on how to fit that parameter by using several fuzzy data analysis tools and discuss the goodness of triangular approximates to fuzzy transition probabilities, the fuzzy stationary state, and the fuzzy mean asymptotic premium. The use of FMCs in a BMS allows obtaining not only point estimates of all these variables, but also a structured set of their possible values whose reliability is given by means of a possibility measure. Although our analysis is circumscribed to non-life insurance, all of its findings can easily be extended to any of the abovementioned fields with slight modifications.

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24

Lee, Ho-Seok. "Life Insurance and Subsistence Consumption with an Exponential Utility." Mathematics 9, no. 4 (February 11, 2021): 358. http://dx.doi.org/10.3390/math9040358.

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In this paper, we derive an explicit solution to the utility maximization problem of an individual with mortality risk and subsistence consumption constraint. We adopt an exponential utility for the individual’s consumption and the martingale and duality method is employed. From the explicit solution, we exhibit how the mortality intensity and subsistence consumption constraint affect, separately and together, portfolio, consumption and life insurance purchase.

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25

Zhao, Ming, Ziwen Li, Yinge Cai, and Weiting Li. "Measurement of Longevity Risk of Life Annuity Based on C-ROSS Framework." Mathematical Problems in Engineering 2020 (September 18, 2020): 1–8. http://dx.doi.org/10.1155/2020/1746413.

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This paper constructs a model to measure longevity risk and explains the reasons for restricting the supply of annuity products in life insurance companies. According to the Lee–Carter Model and the VaR-based stochastic simulation, it can be found that the risk margin of the first type of longevity risk for ignoring the improvement of mortality rate is about 7%, and the risk margin of the second type of longevity risk for underestimating mortality improvement is about 7%. Therefore, the insurer needs to use cohort life table pricing premium and gradually prepares longevity risk capital during the insurance period.

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26

Zheng, Haitao, Junzhang Hao, Manying Bai, and Zhengjun Zhang. "Valuation of guaranteed unitized participating life insurance under GEV distribution." Statistics and Its Interface 11, no. 4 (2018): 603–14. http://dx.doi.org/10.4310/sii.2018.v11.n4.a5.

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27

Barigou, Karim, and Łukasz Delong. "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks." Journal of Computational and Applied Mathematics 404 (April 2022): 113922. http://dx.doi.org/10.1016/j.cam.2021.113922.

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28

Chan, Leunglung, and Eckhard Platen. "Pricing of long dated equity-linked life insurance contracts." Stochastic Analysis and Applications 34, no. 2 (February 18, 2016): 339–55. http://dx.doi.org/10.1080/07362994.2015.1136563.

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29

De Pril, N., and J. Dhaene. "Some remarks on the definition of the basic building blocks of modern life insurance mathematics." Insurance: Mathematics and Economics 17, no. 1 (August 1995): 59. http://dx.doi.org/10.1016/0167-6687(95)91037-m.

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30

Rivas-Lopez, Maria Victoria, Roman Minguez-Salido, Mariano Matilla Garcia, and Alejandro Echeverria Rey. "Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk." Mathematics 9, no. 19 (October 3, 2021): 2476. http://dx.doi.org/10.3390/math9192476.

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This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

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31

Et. al., Nadia Yas ,. "Implications of Compulsory Car Accident Insurance Comparative Study." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2410–20. http://dx.doi.org/10.17762/turcomat.v12i2.2052.

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Compulsory civil liability insurance on car accidents is one of the most important topics, which aims to achieve traffic safety and reduce car accidents. Today, car accidents constitute a great danger to human life as they cause different types of physical and moral damages. This study, however, attempts to highlight the effectiveness of compulsory insurance in providing legal protection for damages resulting from car accidents. Such protection can be achieved through the process of creating legal mechanism to enable injured people to refer to insurance companies. It is quite apparent that nobody can deny the importance of insurance and its influence on people’s lives. Insurance companies nowadays have become an essential cornerstone in the economy of any country. An insurance contract has legal consequences on the parties, which leads to corresponding obligations. These parties are committed to comply with these obligations otherwise, any violation will be treated as per the prescribed legal sanctions. In conclusion, any research or study on the compulsory insurance system whether in the Emirati law or in Islamic Sharia is highly recommended as part of the attempts to solve and rectify any legal and legitimate problems in this system. An essential objective of the Islamic Sharia is to achieve all fair interests of people. Arab and international legislation consider car insurance compulsory with some exceptions in order to compensate all those affected by a car accident. Arab and other foreign laws as well as the legitimate ones have differed in their choices of car insurance basis. Many legislations in the field of compulsory insurance have limited the right of compensation to body damage. Some other legislations have covered damages of property. Most legislations have not specified the amount of compensation that the insurer is obliged to pay in case of injury, death, or property damages. companies.

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32

Et. al., Ramesh Kumar Satuluri,. "Digital Transformation In Indian Insurance Industry." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 11, 2021): 310–24. http://dx.doi.org/10.17762/turcomat.v12i4.509.

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This paper titled “Digital Transformation in Indian Insurance Industry” is an attempt to give deep insights to all the readers on digital transformation in insurance space. Technological innovations are extensive and all encompassing. Disruptions are not industry specific and insurance industry is no exception to this. Recently regulator published a draft regulation on sandbox concept, which permits carriers to innovate their offering to end user. This is led by fintech and insure tech companies and carriers have structured digital boards to take this revolution forward.Major findings of this paper are usage of block chain technology and data security in insurance industry. With companies constituting digital boards, pandemic has only acted like a tailwind for the digital push wherein entire sales process is migrated to digital way of selling. This move has a multiplier effect on customer reach, cost efficiency and service precision. Customers who are keen to have the best in terms of technological innovation will be delighted with the advancement in digital transformation.Also with big data and analytics, we are coming back to risk based pricing, which is proportionate to the risk borne by the customer. This is still evolving in life insurance as the deployment of wearables is at a nascent stage.Newer technologies like AI and machine learning are facilitating companies register higher growth both on cross and upsell opportunities. This will indisputably have an immense and long term positive impact on the bottom line of most insurance companies thus enhancing profitability.Researcher concludes that digital innovation will surely have a great and positive impact on profitability of insurance companies.

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33

Delong, Łukasz, and Russell Gerrard. "Mean-variance portfolio selection for a non-life insurance company." Mathematical Methods of Operations Research 66, no. 2 (March 15, 2007): 339–67. http://dx.doi.org/10.1007/s00186-007-0152-2.

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34

Horáková, Galina, František Slaninka, and Zsolt Simonka. "The Reduction of Initial Reserves Using the Optimal Reinsurance Chains in Non-Life Insurance." Mathematics 9, no. 12 (June 11, 2021): 1350. http://dx.doi.org/10.3390/math9121350.

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The aim of the paper is to propose, and give an example of, a strategy for managing insurance risk in continuous time to protect a portfolio of non-life insurance contracts against unwelcome surplus fluctuations. The strategy combines the characteristics of the ruin probability and the values VaR and CVaR. It also proposes an approach for reducing the required initial reserves by means of capital injections when the surplus is tending towards negative values, which, if used, would protect a portfolio of insurance contracts against unwelcome fluctuations of that surplus. The proposed approach enables the insurer to analyse the surplus by developing a number of scenarios for the progress of the surplus for a given reinsurance protection over a particular time period. It allows one to observe the differences in the reduction of risk obtained with different types of reinsurance chains. In addition, one can compare the differences with the results obtained, using optimally chosen parameters for each type of proportional reinsurance making up the reinsurance chain.

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35

Xie, Yuan-tao, Juan Yang, Chong-guang Jiang, Zi-yu Cai, and Joshua Adagblenya. "Incidence, Dependence Structure of Disease, and Rate Making for Health Insurance." Mathematical Problems in Engineering 2018 (August 12, 2018): 1–13. http://dx.doi.org/10.1155/2018/4265801.

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In order to analyze the two goals under the national strategy of “Healthy China”, this paper attempts to solve the problem of coverage rate and guarantee level of health insurance, as well as the rational allocation of full life cycle health insurance resources. This paper uses pair copula to model the dependence of different disease incidence and proposes an actuarial model for rate making in health insurance based on the dependence captured by pair copula. These are far more accurate than any other model and more proper for covering a basket of several different diseases. The data for the paper was drawn from the experience incidence table of major diseases (malignant tumor, acute myocardial infarction, and stroke sequelae) from the ages 0-65 years in the Chinese life insurance industry. Extending the hypothesis of independence in actuarial modeling, the authors comprehensively use a hierarchical copula theory to extract the dependence structure of risk variable in insurance. The classification rate making technology and survival analysis method in traditional actuarial pricing were also considered. This paper applied the generalized linear model, which is commonly used in nonlife insurance pricing for empirical study of health insurance rate making. The authors discovered that the incidence of major diseases and the single premium rate calculated by the generalized linear model under HAC dependence structure were both significantly different from that calculated by the Manchester United method without dependency. The authors also stated that the rate based on the generalized linear model under HAC dependence structure was a bit different from that without dependency but both were generally the same as that of Care Expert in PICC Health. The underestimation or overestimation of systematic risks and the distortion of the rate system can be eliminated if we combine risk dependence into modeling.

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36

Cavastracci, Stefano. "INTRODUCTION TO NON LIFE MATHEMATICS WITH REFERENCE TO PRICING: CONCEPTS, ORIGIN OF THE MODELS AND A PROPOSAL FOR AN ONGOING MONITORING OF THE PERSONALIZED PREMIUM." JOURNAL OF RISK AND FINANCIAL STUDIES 3, no. 1 (2022): 1–23. http://dx.doi.org/10.47509/jrfs.2022.v03i01.01.

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Pricing is a fundamental topic for a nonlife insurance company. It includes several underwriting activities to set actuarial models. In the past, it was a fundamental part of risk theory and nonlife insurance mathematic and nowadays results in a vast actuarial research field. Since the second half of twentieth century, many papers have been published in actuarial journals. This work originates from the experience of introductory university lectures of the author. After an historical recognition, a proposal is made of a simplified approach to monitor consistency and quality of the personalized premiums over time.

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37

Ferreiro, Ana M., Enrico Ferri, José A. García, and Carlos Vázquez. "Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios." Mathematics 9, no. 5 (February 25, 2021): 472. http://dx.doi.org/10.3390/math9050472.

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Starting from an original portfolio of life insurance policies, in this article we propose a methodology to select model points portfolios that reproduce the original one, preserving its market risk under a certain measure. In order to achieve this goal, we first define an appropriate risk functional that measures the market risk associated to the interest rates evolution. Although other alternative interest rate models could be considered, we have chosen the LIBOR (London Interbank Offered Rate) market model. Once we have selected the proper risk functional, the problem of finding the model points of the replicating portfolio is formulated as a problem of minimizing the distance between the original and the target model points portfolios, under the measure given by the proposed risk functional. In this way, a high-dimensional global optimization problem arises and a suitable hybrid global optimization algorithm is proposed for the efficient solution of this problem. Some examples illustrate the performance of a parallel multi-CPU implementation for the evaluation of the risk functional, as well as the efficiency of the hybrid Basin Hopping optimization algorithm to obtain the model points portfolio.

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38

Kabanov, Yuri, and Nikita Pukhlyakov. "Ruin probabilities with investments: smoothness, inegro-differential and ordinary differential equations, asymptotic behavior." Journal of Applied Probability 59, no. 2 (June 2022): 556–70. http://dx.doi.org/10.1017/jpr.2021.74.

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AbstractThis study deals with the ruin problem when an insurance company having two business branches, life insurance and non-life insurance, invests its reserves in a risky asset with the price dynamics given by a geometric Brownian motion. We prove a result on the smoothness of the ruin probability as a function of the initial capital, and obtain for it an integro-differential equation understood in the classical sense. For the case of exponentially distributed jumps we show that the survival (as well as the ruin) probability is a solution of an ordinary differential equation of the fourth order. Asymptotic analysis of the latter leads to the conclusion that the ruin probability decays to zero in the same way as in the already studied cases of models with one-sided jumps.

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39

Albrecher, Hansjörg, and Daily Amir Dalit. "On effects of asymmetric information on non-life insurance prices under competition." International Journal of Data Analysis Techniques and Strategies 9, no. 4 (2017): 287. http://dx.doi.org/10.1504/ijdats.2017.088355.

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Karam, E., and F. Planchet. "Operational Risks in Financial Sectors." Advances in Decision Sciences 2012 (December 5, 2012): 1–57. http://dx.doi.org/10.1155/2012/385387.

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A new risk was born in the mid-1990s known as operational risk. Though its application varied by institutions—Basel II for banks and Solvency II for insurance companies—the idea stays the same. Firms are interested in operational risk because exposure can be fatal. Hence, it has become one of the major risks of the financial sector. In this study, we are going to define operational risk in addition to its applications regarding banks and insurance companies. Moreover, we will discuss the different measurement criteria related to some examples and applications that explain how things work in real life.

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Khan, Mohammad Farhan, Farnaz Haider, Ahmed Al-Hmouz, and Mohammad Mursaleen. "Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company." Mathematics 9, no. 12 (June 12, 2021): 1369. http://dx.doi.org/10.3390/math9121369.

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Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

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Nguyen, Thai, and Mitja Stadje. "Nonconcave Optimal Investment with Value-at-Risk Constraint: An Application to Life Insurance Contracts." SIAM Journal on Control and Optimization 58, no. 2 (January 2020): 895–936. http://dx.doi.org/10.1137/18m1217322.

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Dorobantu, Diana, Yahia Salhi, and Pierre-E. Thérond. "Modelling Net Carrying Amount of Shares for Market Consistent Valuation of Life Insurance Liabilities." Methodology and Computing in Applied Probability 22, no. 2 (June 22, 2019): 711–45. http://dx.doi.org/10.1007/s11009-019-09729-1.

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Kraft, Holger, and Mogens Steffensen. "The Policyholder’s static and dynamic decision making of life insurance and pension payments." Blätter der DGVFM 29, no. 2 (September 5, 2008): 211–44. http://dx.doi.org/10.1007/s11857-008-0060-4.

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Yan, Shi, Yaodong Zhou, and Youlu Zhang. "Analysis of Balance of Income and Expenditure and Optimal Retirement Age of Pension Insurance Co-Ordination Account Based on Improved Machine Learning Algorithm." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/5870893.

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Since the turn of the twenty-first century, the issue of aging has gained international attention. Both developed and developing nations are currently dealing with this issue. To ensure the sustained and healthy growth of the economy and society in the face of an aging society, it is especially important to establish a scientific old-age insurance system and a reasonable retirement system. We are all aware that the key indicators for the state to control the old-age insurance system in the old-age insurance system are the income and expenditure balance of the old-age insurance pooling account and the analysis of the ideal retirement age. In this paper, a better machine algorithm is used. By independently learning the rules present in a large amount of data and gaining new experience and knowledge, machine learning (ML) can increase computer intelligence and give computers decision-making abilities comparable to those of humans. In general, a machine learning algorithm uses the laws it derives from data to predict unknown data after automatically analysing the data. This study’s findings suggest that the ideal retirement age and life expectancy are positively correlated, with the ideal retirement age’s growth rate 12.57 percent higher than that of life expectancy.

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Qian, Linyi, Hailiang Yang, and Rongming Wang. "Locally risk-minimizing hedging strategies for unit-linked life insurance contracts under a regime switching Lévy model." Frontiers of Mathematics in China 6, no. 6 (February 1, 2011): 1185–202. http://dx.doi.org/10.1007/s11464-011-0100-6.

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Tzougas, George, Natalia Hong, and Ryan Ho. "Mixed Poisson Regression Models with Varying Dispersion Arising from Non-Conjugate Mixing Distributions." Algorithms 15, no. 1 (December 30, 2021): 16. http://dx.doi.org/10.3390/a15010016.

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In this article we present a class of mixed Poisson regression models with varying dispersion arising from non-conjugate to the Poisson mixing distributions for modelling overdispersed claim counts in non-life insurance. The proposed family of models combined with the adopted modelling framework can provide sufficient flexibility for dealing with different levels of overdispersion. For illustrative purposes, the Poisson-lognormal regression model with regression structures on both its mean and dispersion parameters is employed for modelling claim count data from a motor insurance portfolio. Maximum likelihood estimation is carried out via an expectation-maximization type algorithm, which is developed for the proposed family of models and is demonstrated to perform satisfactorily.

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Kirch, Michael, and Alexander Melnikov. "Efficient Hedging and Pricing of Life Insurance Policies in a Jump-Diffusion Model." Stochastic Analysis and Applications 23, no. 6 (November 2005): 1213–33. http://dx.doi.org/10.1080/07362990500292692.

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Albeverio, S., V. Steblovskaya, and K. Wallbaum. "Valuation of Equity-Linked Life Insurance Contracts Using a Model with Interacting Assets." Stochastic Analysis and Applications 27, no. 5 (August 28, 2009): 1077–95. http://dx.doi.org/10.1080/07362990902844462.

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Ahmad, Zubair, Eisa Mahmoudi, G. G. Hamedani, and Omid Kharazmi. "On modeling heavy tailed medical care insurance data via a new member of T-X family." Filomat 36, no. 6 (2022): 1971–89. http://dx.doi.org/10.2298/fil2206971a.

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Heavy tailed distributions are worthwhile in modeling heavy tailed data. The researchers are often in search of such distributions to provide best fit to heavy tailed data. In this article, a new T-X family member called, a new exponential cosine-X family is introduced. A special sub-model of the proposed family, called, a new exponential cosine Weibull distribution is studied in detail. Some mathematical properties along with the useful series expansion of distribution and density functions of the proposed class are obtained. Two useful characterizations of this family are also provided. We consider the maximum likelihood and Bayesian estimation procedures to estimate the parameters of the proposed family. Monti Carlo simulation study is done to access the behavior of these estimators. For the illustrative purposes, a real-life application of the proposed family to a heavy tailed medical care insurance data set is provided. Finally, Bayesian analysis and performance of Gibbs sampling for the medical care insurance data are also carried out.

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