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Littérature scientifique sur le sujet « Life insurance – Mathematics »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 29 janvier 2023

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Articles de revues sur le sujet "Life insurance – Mathematics"

Carroll, Patrick, et E. Straub. « Non-Life Insurance Mathematics. » Journal of the Royal Statistical Society. Series A (Statistics in Society) 153, n^o 2 (1990) : 262. http://dx.doi.org/10.2307/2982815.

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Denuit, Michel, et Esther Frostig. « Life Insurance Mathematics with Random Life Tables ». North American Actuarial Journal 13, n^o 3 (juillet 2009) : 339–55. http://dx.doi.org/10.1080/10920277.2009.10597560.

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Ruohonen, Matti. « Non-Life Insurance Mathematics (Erwin Straub) ». SIAM Review 32, n^o 1 (mars 1990) : 184–85. http://dx.doi.org/10.1137/1032031.

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Hald, Anders. « On the early history of life insurance mathematics ». Scandinavian Actuarial Journal 1987, n^o 1-2 (janvier 1987) : 4–18. http://dx.doi.org/10.1080/03461238.1987.10413813.

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Scadden, R. W. « Non-Life Insurance Mathematics. By Erwin Straub. (Springer-Verlag.) ». Journal of the Institute of Actuaries 116, n^o 2 (septembre 1989) : 297. http://dx.doi.org/10.1017/s0020268100036611.

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Dong, Yang, Hao Wang et Lihong Zhang. « Stock Return Uncertainty and Life Insurance ». Mathematical Problems in Engineering 2020 (10 juillet 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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Fauziah, Irma. « PERHITUNGAN PREMI ASURANSI JIWA DWIGUNA PASUTRI SEBAGAI PENERAPAN PEMBELAJARAN MATEMATIKA EKONOMI ». Phenomenon : Jurnal Pendidikan MIPA 3, n^o 1 (25 février 2016) : 99. http://dx.doi.org/10.21580/phen.2013.3.1.177.

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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. 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 vt discount factor and they are integrated by using the assumption of a uniform distribution of death throughout the year of age.

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Schmidli, Hanspeter. « Optimisation in Non-Life Insurance ». Stochastic Models 22, n^o 4 (22 novembre 2006) : 689–722. http://dx.doi.org/10.1080/15326340600878420.

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Bacinello, Anna Rita, Enrico Biffis et Pietro Millossovich. « Pricing life insurance contracts with early exercise features ». Journal of Computational and Applied Mathematics 233, n^o 1 (novembre 2009) : 27–35. http://dx.doi.org/10.1016/j.cam.2008.05.036.

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Biagini, Francesca, et Irene Schreiber. « Risk-Minimization for Life Insurance Liabilities ». SIAM Journal on Financial Mathematics 4, n^o 1 (janvier 2013) : 243–64. http://dx.doi.org/10.1137/110856836.

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Thèses sur le sujet "Life insurance – Mathematics"

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, et 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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Hagsjö, Renberg Oscar, et 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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Osman, Abdelghafour Mohamed. « Structured products : Pricing, hedging and applications for life insurance companies ». Thesis, Uppsala University, Department of Mathematics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-119969.

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Pansera, Jérôme. « Local risk minimization, consistent interest-rate modeling, and applications to life insurance ». Diss., University of Iowa, 2008. https://ir.uiowa.edu/etd/15.

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This thesis studies local risk minimization, consistent interest-rate modeling, and their applications to life insurance. Part I considers local risk minimization, which is one possible approach to price and hedge claims in incomplete markets. In this first part, our two main results are Propositions 3.6 and 4.3: they provide an easy way to compute locally risk-minimizing hedging strategies for common life-insurance products in discrete time and in continuous time, respectively. Part II considers consistent interest-rate modeling; that is, interest-rate models in which a change in the yield curve can be explained by a change in the state variable, without changing the parameters of the model. In this second part, we present a single-factor interest-rate model (jointly specified under the physical and the risk-neutral probability measures), which allows for observation errors. Our main result is an algorithm to estimate the hidden values of the state variable, as well as the five parameters of our model. We also outline how our results can be extended to the multi-factor case. Part III combines the results of Parts I and II in a numerical example. In this example, we compute a locally risk-minimizing hedging strategy for a life annuity under stochastic interest rates. We assume that the insurance company is trying to hedge this product by trading zero-coupon bonds of various maturities. Since a perfect hedge is impossible in this case, we obtain (by simulation) the distribution of the cost resulting from the ``mis-hedge''. This distribution is with respect to the physical probability measure, while most of the existing literature considers it under a risk-neutral measure.

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Gip, Orreborn Jakob. « Asset-Liability Management with in Life Insurance ». Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215339.

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In recent years, new regulations and stronger competition have further increased the importance of stochastic asset-liability management (ALM) models for life insurance firms. However, the often complex nature of life insurance contracts makes modeling to a challenging task, and insurance firms often struggle with models quickly becoming too complicated and inefficient. There is therefore an interest in investigating if, in fact, certain traits of financial ratios could be exposed through a more efficient model. In this thesis, a discrete time stochastic model framework, for the simulation of simplified balance sheets of life insurance products, is proposed. The model is based on a two-factor stochastic capital market model, supports the most important product characteristics, and incorporates a reserve-dependent bonus declaration. Furthermore, a first approach to endogenously model customer transitions is proposed, where realized policy returns are used for assigning transition probabilities. The model's sensitivity to different input parameters, and ability to capture the most important behaviour patterns, are demonstrated by the use of scenario and sensitivity analyses. Furthermore, based on the findings from these analyses, suggestions for improvements and further research are also presented.
Införandet av nya regelverk och ökad konkurrens har medfört att stokastiska ALM-modeller blivit allt viktigare för livförsäkringsbolag. Den ofta komplexa strukturen hos försäkringsprodukter försvårar dock modelleringen, vilket gör att många modeller anses vara för komplicerade samt ineffektiva, av försäkringsbolagen. Det finns därför ett intresse i att utreda om egenskaper hos viktiga finansiella nyckeltal kan studeras utifrån en mer effektiv och mindre komplicerad modell. I detta arbete föreslås ett ramverk för stokastisk modellering av en förenklad version av balansräkningen hos typiska livförsäkringsbolag. Modellen baseras på en stokastisk kapitalmarknadsmodell, med vilken såväl aktiepriser som räntenivåer simuleras. Vidare så stödjer modellen simulering av de mest väsentliga produktegenskaperna, samt modellerar kundåterbäring som en funktion av den kollektiva konsolideringsgraden. Modellens förmåga att fånga de viktigaste egenskaperna hos balansräkningens ingående komponenter undersöks med hjälp av scenario- och känslighetsanalyser. Ytterligare undersöks även huruvida modellen är känslig för förändringar i olika indata, där fokus främst tillägnas de parametrar som kräver mer avancerade skattningsmetoder.

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Barnholdt, Jacob, et Josefin Grafford. « Predicting Large Claims within Non-Life Insurance ». Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228983.

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This bachelor thesis within the field of mathematical statistics aims to study the possibility of predicting specifically large claims from non-life insurance policies with commercial policyholders. This is done through regression analysis, where we seek to develop and evaluate a generalized linear model, GLM. The project is carried out in collaboration with the insurance company If P&C Insurance and most of the research is conducted at their headquarters in Stockholm. The explanatory variables of interest are characteristics associated with the policyholders. Due to the scarcity of large claims in the data set, the prediction is done in two steps. Firstly, logistic regression is used to model the probability of a large claim occurring. Secondly, the magnitude of the large claims is modelled using a generalized linear model with a gamma distribution. Two full models with all characteristics included are constructed and then reduced with computer intensive algorithms. This results in two reduced models, one with two characteristics excluded and one with one characteristic excluded.
Det här kandidatexamensarbetet inom matematisk statistik avser att studera möjligheten att predicera särskilt stora skador från sakförsäkringspolicys med företag som försäkringstagare. Detta görs med regressionsanalys, där vi ämnar att utveckla och bedöma en generaliserad linjär modell, GLM. Projektet utförs i samarbete med försäkringsbolaget If Skadeförsäkring och merparten av undersökningen sker på deras huvudkontor i Stockholm. Förklaringsvariablerna som är av intresse att undersöka är egenskaper associerade med försäkringstagarna. På grund av sällsynthet av storskador i datamängden görs prediktionen i två steg. Först används logistisk regression för att modellera sannolikheten för en storskada att inträffa. Sedan modelleras storskadornas omfattning genom en generaliserad linjär modell med en gammafördelning. Två grundmodeller med alla förklaringsvariabler konstrueras för att sedan reduceras med datorintensiva algoritmer. Det resulterar i två reducerade modeller, med två respektive en kundegenskap utesluten.

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Rinkevičiūtė, Laima. « Ne gyvybės draudimo analizė Lietuvoje ». Master's thesis, Lithuanian Academic Libraries Network (LABT), 2006. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060606_150230-24295.

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Insurance market in Lithuania is evolving yet, but this process is quite rapid. The destination of this work – analysis of non life insurance in Lithuania, which we will dispense, when we will interpret statistical information of insurance, also we will analyze paying capacity of non life insurance companies. Insurance companies calculate future’s contribution using data of past period. It would be better to correct contribution according to predictive future’s number of contracts and loss. So the number of contracts and loss, signed by Lithuanian insurance companies each quarter, are studied as time series. Several time series models were created for three principal kinds of insurance (Motor Third Party Liability Insurance, Land vehicles other than railway rolling stock Insurance, Property Insurance) and the one that meets the reality best was selected. We will analyze variation of number of non life insurance companies, number of paid losses, number of signed contributions and number of contracts. After analyses of Insurance market’s indicators, we get strong tendency that Insurance market becomes more stable. After analysis of insurance companies’ paying capacity we got, that two close private companies - “Baltic Polis” and “Industrijos garantas” – was close to bankrupt in 2004 year. After forecasting number of Motor Third Party Liability Insurance’s and Land vehicles other than railway rolling stock Insurance’s contracts we got that Autoregressive model is the best for... [to full text]

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Hardin, Patrik, et Sam Tabari. « Modelling Non-life Insurance Policyholder Price Sensitivity : A Statistical Analysis Performed with Logistic Regression ». Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209773.

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This bachelor thesis within mathematical statistics studies the possibility of modelling the renewal probability for commercial non-life insurance policyholders. The project was carried out in collaboration with the non-life insurance company If P&C Insurance Ltd. at their headquarters in Stockholm, Sweden. The paper includes an introduction to underlying concepts within insurance and mathematics and a detailed review of the analytical process followed by a discussion and conclusions. The first stages of the project were the initial collection and processing of explanatory insurance data and the development of a logistic regression model for policy renewal. An initial model was built and modern methods of mathematics and statistics were applied in order obtain a final model consisting of 9 significant characteristics. The regression model had a predictive power of 61%. This suggests that it to a certain degree is possible to predict the renewal probability of non-life insurance policyholders based on their characteristics. The results from the final model were ultimately translated into a measure of price sensitivity which can be implemented in both pricing models and CRM systems. We believe that price sensitivity analysis, if done correctly, is a natural step in improving the current pricing models in the insurance industry and this project provides a foundation for further research in this area.
Detta kandidatexamensarbete inom matematisk statistik undersöker möjligheten att modellera förnyelsegraden för kommersiella skadeförsärkringskunder. Arbetet utfördes i samarbete med If Skadeförsäkring vid huvudkontoret i Stockholm, Sverige. Uppsatsen innehåller en introduktion till underliggande koncept inom försäkring och matematik samt en utförlig översikt över projektets analytiska process, följt av en diskussion och slutsatser. De huvudsakliga delarna av projektet var insamling och bearbetning av förklarande försäkringsdata samt utvecklandet och tolkningen av en logistisk regressionsmodell för förnyelsegrad. En första modell byggdes och moderna metoder inom matematik och statistik utfördes för att erhålla en slutgiltig regressionsmodell uppbyggd av 9 signifikanta kundkaraktäristika. Regressionsmodellen hade en förklaringsgrad av 61% vilket pekar på att det till en viss grad är möjligt att förklara förnyelsegraden hos försäkringskunder utifrån dessa karaktäristika. Resultaten från den slutgiltiga modellen översattes slutligen till ett priskänslighetsmått vilket möjliggjorde implementering i prissättningsmodeller samt CRM-system. Vi anser att priskänslighetsanalys, om korrekt genomfört, är ett naturligt steg i utvecklingen av dagens prissättningsmodeller inom försäkringsbranschen och detta projekt lägger en grund för fortsatta studier inom detta område.

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Gyllenberg, Felix, et Åström Leonard Rudolf. « INTEREST RATE RISK : A comparative study aimed at finding the most crucial shift in interest rate curves for a life insurance company ». Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160248.

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Risk management is applied in many financial institutions under regulatory supervision. Life insurance companies face many challenges to ensure policy holders of future payouts. The inverted balance sheet of life insurance companies imply that the policy holder pay premiums in advance to the insurance company to later receive payouts at the age of retirement. This means a great responsibility for the life insurance company to be able to meet future liabilities. Due to this, one of the largest risks facing a life insurance company is the interest rate risk. Future liabilities depend on the interest rates and the difference in duration in assets and liabilities creates an imperfect negative correlation between the movements in assets and liabilities when the interest rate change. The bond market holds different types of bonds such as government bonds, housing bonds and corporate bonds with different maturities within each subgroup. The relationship between these subgroups and maturities within these subgroups are interesting to investigate in a forecasting point of view. This relationship is usually referred to as the term structure of interest rates and changes in the term structure are referred to as shifts. This thesis aims to find which of the three shifts, level, slope and curvature, that is most important to capture in interest rate models. This is investigated using three different simulation techniques and the results show that the first shift representing a level shift of the whole term structure has the largest effect on Skandia’s balance sheet.

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Livres sur le sujet "Life insurance – Mathematics"

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

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Versicherungsmathematiker, Vereinigung Schweizerischer, dir. Life insurance mathematics. 3^e éd. Berlin : Springer, 1997.

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Gerber, Hans U. Life insurance mathematics. 2^e éd. 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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Actuaries, Association of Swiss, dir. Non-life insurance mathematics. Berlin : Springer-Verlag, 1988.

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Non-life insurance mathematics. Berlin : Springer-Verlag, 1988.

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Straub, Erwin. Non-life insurance mathematics. 2^e éd. 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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Chapitres de livres sur le sujet "Life insurance – Mathematics"

Gerber, Hans U. « Life Insurance ». Dans 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 ». Dans 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 ». Dans 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. « Life Annuities ». Dans Life Insurance Mathematics, 35–47. Berlin, Heidelberg : Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02655-7_4.

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Gerber, Hans U. « Life Annuities ». Dans Life Insurance Mathematics, 35–47. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03460-6_4.

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Gerber, Hans U. « Life Annuities ». Dans Life Insurance Mathematics, 35–47. Berlin, Heidelberg : Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03153-7_4.

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Gerber, Hans U. « Multiple Life Insurance ». Dans 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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Gerber, Hans U. « Multiple Life Insurance ». Dans Life Insurance Mathematics, 83–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03460-6_8.

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Gerber, Hans U. « Multiple Life Insurance ». Dans Life Insurance Mathematics, 83–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03153-7_8.

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Gerber, Hans U. « The Mathematics of Compound Interest ». Dans 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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Actes de conférences sur le sujet "Life insurance – Mathematics"

Heilpern, Stanisław. « Multiple Life Insurance - Pension Calculation ». Dans 17-th AMSE. Applications of mathematics in economics. International Scientific Conference : Poland, 27-31 Agust, 2014. Conference proceedings full text papers. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, 2014. http://dx.doi.org/10.15611/amse.2014.17.12.

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Sari, D. J., D. Lestari et S. Devila. « Pricing life insurance premiums using Cox regression model ». Dans PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132461.

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Naufal, N., S. Devila et D. Lestari. « Generalized linear model (GLM) to determine life insurance premiums ». Dans PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132463.

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Fojtík, Jan, Jiří Procházka, Pavel Zimmermann, Simona Macková et Markéta Švehláková. « Alternative Approach for Fast Estimation of Life Insurance Liabilities ». Dans Applications of Mathematics and Statistics in Economics. International Scientific Conference : Szklarska Poręba, 30 August- 3 September 2017. Publishing House of Wroclaw University of Economics, 2017. http://dx.doi.org/10.15611/amse.2017.20.11.

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Ng, S., D. Lestari et S. Devila. « Generalized linear model for deductible pricing in non-life insurance ». Dans PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132465.

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Millennium, Ratih Kusuma, et Rosita Kusumawati. « The simulation of claim severity and claim frequency for estimation of loss of life insurance company ». Dans PROCEEDINGS OF THE 4TH INTERNATIONAL SEMINAR ON INNOVATION IN MATHEMATICS AND MATHEMATICS EDUCATION (ISIMMED) 2020 : Rethinking the role of statistics, mathematics and mathematics education in society 5.0 : Theory, research, and practice. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0110652.

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Siswono, Galuh Oktavia, Wawan Hafid Syaifudin et Wisnowan Hendy Saputra. « A mathematical modelling in determining the portion of Tabarru’ fund for sharia life insurance based on wakalah scheme ». Dans THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc) : A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0112031.

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Ulyah, Siti Maghfirotul, Marisa Rifada, Elly Ana, Christopher Andreas, Ilma Amira Rahmayanti et Salsabylla Nada Apsariny. « Forecasting premium adequacy to claim paid ratio in life insurance industry with COVID-19 effect using multilayer perceptron neural network ». Dans THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc) : A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0111946.

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Samsudin, Humaida Banu. « Failure factors in non-life insurance companies in United Kingdom ». Dans PROCEEDINGS OF THE 20TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES : Research in Mathematical Sciences : A Catalyst for Creativity and Innovation. AIP, 2013. http://dx.doi.org/10.1063/1.4801258.

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Samsudin, Humaida Banu. « The Lexis plot for run-off non-life insurance companies in United Kingdom ». Dans PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4882528.

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