Relevant bibliographies by topics / Insurance – Mathematics

Academic literature on the topic 'Insurance – Mathematics'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Insurance – Mathematics"

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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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Embrechts, P., and C. Klüppelberg. "Some Aspects of Insurance Mathematics." Theory of Probability & Its Applications 38, no. 2 (June 1994): 262–95. http://dx.doi.org/10.1137/1138025.

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Martin-Löf, Anders. "Harald cramér and insurance mathematics." Applied Stochastic Models and Data Analysis 11, no. 3 (September 1995): 271–76. http://dx.doi.org/10.1002/asm.3150110308.

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Martin-Löf, A. "Harald Cramér and Insurance Mathematics." Insurance: Mathematics and Economics 17, no. 3 (April 1996): 234. http://dx.doi.org/10.1016/0167-6687(96)82364-x.

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Embrechts, Paul. "Where mathematics, insurance and finance meet." Quantitative Finance 2, no. 6 (December 2002): 402–4. http://dx.doi.org/10.1080/14697688.2002.0000003.

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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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Lee, Si Won, and Young-Ok Kim. "The Analysis of Linkage between Insurance Mathematics and School Mathematics." East Asian mathematical journal 32, no. 2 (February 29, 2016): 233–51. http://dx.doi.org/10.7858/eamj.2016.019.

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Osei, James Adekorafo, and Emmanuel Yooku. "Designing an Insurance Pricing Model using a Mathematical Approach." Journal of Statistics and Mathematical Concepts 1, no. 1 (February 26, 2023): 17–24. http://dx.doi.org/10.58425/jsmc.v1i1.123.

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Purpose: Every time drivers take to the road, and with each mile that they drive, exposes themselves and others to the risk of an accident. Insurance premiums are only weakly linked to mileage, however, and have lump-sum characteristics largely. The result is too much driving, and too many accidents. The purpose of carrying out this research was to determine a model for calculating the premiums for Pay-As-You-Drive in automobile insurances. Methodology: To price Pay-As-You-Drvie auto insurance, we define a discounted collective risk model while the total number of claim has non-homogeneous Poisson distribution. By applying non-homogeneous Poisson distribution we can enter the mileage to the discounted collective risk model to the premiums for Pay-As-You-Drive in Automobile insurances. We apply the double Double Stochastic Poisson Process for modeling the DCRM. The Double Stochastic Poisson Process provides flexibility by letting the intensity not only depend on time but also by allowing it to be a stochastic process. Findings: By applying the doubly stochastic Poisson process to review the driver’s mileage in the model, the study found the distribution of discounted collective risk model and present the expected value of total loss for calculating the premiums. Recommendation: The current auto insurance pricing systems are inequitable because low-mileage drivers subsidize insurance costs for high-mileage drivers, and low-income people drive fewer miles on average. The study recommends a more efficient pricing systems model to find a good model for calculating the fair auto insurance premium. Keywords: Cox process, martingales, aggregate risk models, PAYD, actuarial mathematics.
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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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Beirlant, J., and S. T. Rachev. "The problem of stability in insurance mathematics." Insurance: Mathematics and Economics 6, no. 3 (July 1987): 179–88. http://dx.doi.org/10.1016/0167-6687(87)90011-4.

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Dissertations / Theses on the topic "Insurance – Mathematics"

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Yamazato, Makoto. "Non-life Insurance Mathematics." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/96535.

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In this work we describe the basic facts of non-life insurance and then explain risk processes. In particular, we will explain in detail the asymptotic behavior of the probability that an insurance product may end up in ruin during its lifetime. As expected, the behavior of such asymptotic probability will be highly dependent on the tail distribution of each claim.
En este artículo describimos los conceptos básicos relacionados a seguros que no sean de vida y luego explicamos procesos de riesgo. En particular, tratamos al detalle el comportamiento asintótico de la probabilidad de que un producto sea declarado en ruina. Como es suponible, el comportamiento en el horizonte depende de la cola de la distribución de las primas.
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Arvidsson, Hanna, and Sofie Francke. "Dependence in non-life insurance." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120621.

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Gong, Qi, and 龔綺. "Gerber-Shiu function in threshold insurance risk models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40987966.

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Wan, Lai-mei. "Ruin analysis of correlated aggregate claims models." Thesis, Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30705708.

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Ekheden, Erland. "Catastrophe, Ruin and Death - Some Perspectives on Insurance Mathematics." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-103165.

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This thesis gives some perspectives on insurance mathematics related to life insurance and / or reinsurance. Catastrophes and large accidents resulting in many lost lives are unfortunatley known to happen over and over again. A new model for the occurence of catastrophes is presented; it models the number of catastrophes, how many lives that are lost, how many lost lives that are insured by a specific insurer and the cost of the resulting claims, this  makes it possible to calculate the price of reinsurance contracts linked to catastrophic events.  Ruin is the result if claims exceed inital capital and the premiums collected by an insurance company. We analyze the Cramér-Lundberg approximation for the ruin probability and give an explicit rate of convergence in the case were claims are bounded by some upper limit. Death is known to be the only thing that is certain in life. Individual life spans are however random, models for and statistics of mortality are imortant for, amongst others, life insurance companies whose payments ultimatley depend on people being alive or dead. We analyse the stochasticity of mortality and perform a variance decomposition were the variation in mortality data is either explained by the covariates age and time, unexplained systematic variation or random noise due to a finite population. We suggest a mixed regression model for mortality and fit it to data from the US and Sweden, including prediction intervals of future mortalities.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: In press. Paper 4: Submitted.

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Lundvik, Andreas. "Portfolio insurance methods for index-funds." Thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121382.

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Hagsjö, Renberg Oscar, and Oscar Hermansson. "Large claims in non-life insurance." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215492.

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It is of outmost importance for an insurance company to apply a fair pricing policy. If the price is too high, valuable customers are lost to other insurance companies while if it’s too low – it nets a negative profit. To achieve a good pricing policy, information regarding claim size history for a given type of customer is required. A problem arises as large extremal events occur and affects the claim size data. These extremal events take shape in individually large claim sizes that by themselves can alter the distribution for what certain groups of individuals are expected to cost. A remedy for this is to apply what is called a large claim limit. Any claim exceeding this limit is thought of as being outside the scope of what is captured by the original distribution of the claim size. These exceeding claims are treated separately and have their cost distributed across all insurance takers, rather than just the group they belong to. So, where exactly do you draw this limit? Do you treat the entire claim size this way (exclusion) or just the bit that is exceeding the threshold (truncation)? These questions are treated and answered in this master’s thesis for Trygg-Hansa. For each product code, a limit was achieved in addition to which method for exceeding data that was best to use.
Det är oerhört viktigt för ett försäkringsbolag att kunna tillämpa en god prissättning. Är priset för högt så förloras kunder till andra försäkringsbolag, och är den underprisad är det en förlustaffär. För att kunna sätta bra priser krävs information om vilka samt hur stora skador som kan tänkas inträffa för en given kundprofil. Ett problem uppstår när stora extremfall påverkar skadedatan. Dessa extremfall yttrar sig genom enskilda storskador som kan komma att påverka prissättningen för en hel grupp då distributionen för vad gruppen förväntas kosta kan ändras. Detta problem kan lösas genom att införa en storskadegräns till skadedatan. Skador över denna gräns räknas som extremfall och utanför ramen av vad den ursprungliga distributionen för skadorna beskriver. De hanteras separat och låter sin kostnad fördelas över samtliga försäkringstagare. Men vart dras denna gräns? Ska man behandla hela den överstigande kostnaden på detta sätt (exkludering) eller bara den biten av skadan som går över storskadegränsen (trunkering)? Dessa frågor behandlas och besvaras i denna masteruppsats i uppdrag åt Trygg-Hansa. För de olika produkttypkoderna beräknades varsin storskadegräns samt metod för överskridande data.
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Huang, Danwei, and 黃丹薇. "Robustness of generalized estimating equations in credibility models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38842312.

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Lin, Yin, and 林印. "Some results on BSDEs with applications in finance and insurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899831.

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Considerably much work has been done on Backward Stochastic Differential Equations (BSDEs) in continuous-time with deterministic terminal horizon or stopping times. Various new models have been introduced in these years in order to generalize BSDEs to solve new practical financial problems. One strand is focused on discrete-time models. Backward Stochastic Difference Equations (also called BSDEs if no ambiguity) on discrete-time finite-state space were introduced by Cohen and Elliott (2010a). The associated theory required only weak assumptions. In the first topic of this thesis, properties of non-linear expectations defined using the discrete-time finite-state BSDEs were studied. A converse comparison theorem was established. Properties of risk measures defined by non-linear expectations, especially the representation theorems, were given. Then the theory of BSDEs was applied to optimal design of dynamic risk measures. Another strand is about a general random terminal time, which is not necessarily a stopping time. The motivation of this model is a financial problem of hedging of defaultable contingent claims, where BSDEs with stopping times are not applicable. In the second topic of this thesis, discrete-time finite-state BSDEs under progressively enlarged filtration were considered. Martingale representation theorem, existence and uniqueness theorem and comparison theorem were established. Application to nonlinear expectations was also explored. Using the theory of BSDEs, the explicit solution for optimal design of dynamic default risk measures was obtained. In recent work on continuous-time BSDEs under progressively enlarged filtration, the reference filtration is generated by Brownian motions. In order to deal with cases with jumps, in the third topic of this thesis, a general reference filtration with predictable representation property and an initial time with immersion property were considered. The martingale representation theorem for square-integrable martingales under progressively enlarged filtration was established. Then the existence and uniqueness theorem of BSDEs under enlarged filtration using Lipschitz continuity of the driver was proved. Conditions for a comparison theorem were also presented. Finally applications to nonlinear expectations and hedging of defaultable contingent claims on Brownian-Poisson setting were explored.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Nguyen, Mai. "Machine Learning Algorithmsfor Regression Modeling in Private Insurance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234857.

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This thesis is focused on the Occupational Pension, an important part of the retiree’s total pension. It is paid by private insurance companies and determined by an annuity divisor. Regression modeling of the annuity divisor is done by using the monthly paid pension as a response and a set of 24 explanatory variables e.g. the expected remaining lifetime and advance interest rate. Two machine learning algorithms, artificial neural networks (ANN) and support vector machines for regression (SVR) are considered in detail. Specifically, different transfer functions for ANN are studied as well as the possibility to improve the SVR model by incorporating a non-linear Gaussian kernel. To compare our result with prior experience of the Swedish Pensions Agency in modeling and predicting the annuity divisor, we also consider the ordinary multiple linear regression (MLR) model. Although ANN, SVR and MLR are of different nature, they demonstrate similar performance accuracy. It turns out that for our data that MLR and SVR with a linear kernel achieve the highest prediction accuracy. When performing feature selection, all methods except SVR with a Gaussian kernel encompass the features corresponding to advance interest rate and expected remaining lifetime, which according to Swedish law {Swedish law: 5 kap. 12 § lagen (1998:674) om inkomstgrundad ålderspension} are main factors that determine the annuity divisor. The results of this study confirm the importance of the two main factors for accurate modeling of the annuity divisor in private insurance. We also conclude that, in addition to the methods used in previous research, methods such as MLR, ANN and SVR may be used to accurately model the annuity divisor.
Denna uppsats fokuserar på tjänstepensionen, en viktig del av en pensionärs totala pension. Den utbetalas av privata försäkringsbolag och beräknas med hjälp av ett så kallat delningstal. Regressionsmodellering av delningstalet görs genom att använda den månatliga utbetalda pensionen som svar och en uppsättning av 24 förklarande variabler såsom förväntad återstående livslängd och förskottsränta. Två maskininlärningsalgoritmer, artificiella neuronnät (ANN) och stödvektormaskiner för regression (SVR) betraktas i detalj. Specifikt så studeras olika överföringsfunktioner för ANN och möjligheten att förbättra SVR modellen genom att införa en ickelinjär Gaussisk kärna. För att jämföra våra resultat med tidigare erfarenhet från Pensionsmyndigheten vid modellering och förutsägande av delningstalet studerar vi även ordinär multipel linjär regression (MLR). Även om ANN, SVR och MLR är av olika natur påvisar dem liknande noggrannhet. Det visar sig för vår data att MLR och SVR med en linjär kärna uppnår den högsta noggrannheten på okänd data. Vid variabel urvalet omfattar samtliga metoder förutom SVR med en Gaussisk kärna variablerna motsvarande förväntad återstående livslängd och förskottsränta som enligt svensk lag är huvudfaktorer vid bestämning av delningstalet. Resultatet av denna studie bekräftar betydelsen av huvudfaktorerna för noggrann modellering av delningstalet inom privat försäkring. Vi drar även slutsatsen att utöver metoderna som använts i tidigare studier kan metoder såsom ANN, SVR och MLR användas med framgång för att noggrant modellera delningstalet.
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Books on the topic "Insurance – Mathematics"

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Gerber, Hans U. Life insurance mathematics. 2nd ed. Berlin: Springer, 1995.

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Gerber, Hans U. Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03153-7.

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Gerber, Hans U. Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02655-7.

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Gerber, Hans U. Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03460-6.

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Gerber, Hans U. Life insurance mathematics. Berlin: Springer-Verlag, 1990.

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Versicherungsmathematiker, Vereinigung Schweizerischer, ed. Life insurance mathematics. 3rd ed. Berlin: Springer, 1997.

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Straub, Erwin. Non-life insurance mathematics. 2nd ed. Berlin: Springer, 1997.

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Mikosch, Thomas. Non-Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88233-6.

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Olivieri, Annamaria, and Ermanno Pitacco. Introduction to Insurance Mathematics. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21377-4.

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Olivieri, Annamaria, and Ermanno Pitacco. Introduction to Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16029-5.

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Book chapters on the topic "Insurance – Mathematics"

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Hickman, James C., and Edward W. Frees. "Insurance Mathematics." In The New Palgrave Dictionary of Economics, 6614–20. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2288.

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Hickman, James C., and Edward W. Frees. "Insurance Mathematics." In The New Palgrave Dictionary of Economics, 1–7. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2288-1.

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Gerber, Hans U. "Life Insurance." In Life Insurance Mathematics, 23–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02655-7_3.

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Gerber, Hans U. "Life Insurance." In Life Insurance Mathematics, 23–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03460-6_3.

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Gerber, Hans U. "Life Insurance." In Life Insurance Mathematics, 23–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03153-7_3.

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Gerber, Hans U. "The Mathematics of Compound Interest." In Life Insurance Mathematics, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02655-7_1.

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Gerber, Hans U. "The Mathematics of Compound Interest." In Life Insurance Mathematics, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03460-6_1.

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Gerber, Hans U. "The Mathematics of Compound Interest." In Life Insurance Mathematics, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03153-7_1.

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Klüppelberg, Claudia. "Developments in Insurance Mathematics." In Mathematics Unlimited — 2001 and Beyond, 703–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56478-9_36.

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Gerber, Hans U. "Multiple Life Insurance." In Life Insurance Mathematics, 83–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02655-7_8.

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Conference papers on the topic "Insurance – Mathematics"

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Aziz, Nazrina, and Shahirah Abdul Razak. "Survival analysis in insurance attrition." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136402.

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Chen, Khoo Wooi, and Chan Lay Guat. "Unemployment insurance: A case study in Malaysia." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136426.

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Putra, Tri Andika Julia, Donny Citra Lesmana, and I. Gusti Putu Purnaba. "Prediction of Future Insurance Premiums When the Model is Uncertain." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.054.

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Alwie, Ferren, Mila Novita, and Suci Fratama Sari. "Risk measurement for insurance sector with credible tail value-at-risk." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136427.

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Raeva, E., and V. Pavlov. "Planning outstanding reserves in general insurance." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 9th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’17. Author(s), 2017. http://dx.doi.org/10.1063/1.5007381.

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Pavlov, V., and V. Mihova. "An application of survival model in insurance." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 10th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’18. Author(s), 2018. http://dx.doi.org/10.1063/1.5064883.

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Stoilov, Todor, and Krasimira Stoilova. "Planning insurance expenditures in animal husbandry management." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2022 (MATHTECH 2022): Navigating the Everchanging Norm with Mathematics and Technology. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0184310.

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Iqbal, M., F. Novkaniza, and M. Novita. "Pricing unit-linked insurance with guaranteed benefit." In INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2016 (ISCPMS 2016): Proceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4991243.

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Jiang, Zhiheng, Jiahao Fan, and Qiyi Wang. "Pricing Model of Insurance Product for Autistic Persons." In ICoMS 2022: 2022 5th International Conference on Mathematics and Statistics. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3545839.3545851.

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Pahrany, Andi Daniah, Lita Wulandari Aeli, and Utriweni Mukhaiyar. "Value at risk analysis using automated threshold selection method in property insurance." In THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS (ICOMATHAPP) 2022: The Latest Trends and Opportunities of Research on Mathematics and Mathematics Education. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0193668.

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