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Journal articles on the topic 'Non-life insurance'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 May 2024

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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

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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3

Lemaire, Jean, and G. C. Taylor. "Claims Reserving in Non-Life Insurance." Journal of Risk and Insurance 55, no. 2 (June 1988): 396. http://dx.doi.org/10.2307/253338.

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4

Buchwalder, Markus, Hans Bühlmann, Michael Merz, and Mario V. Wüthrich. "Valuation portfolio in non-life insurance." Scandinavian Actuarial Journal 2007, no. 2 (June 2007): 108–25. http://dx.doi.org/10.1080/03461230701251455.

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5

Meyers, Glenn, and G. C. Taylor. "Claim Reserving in Non-Life Insurance." Journal of the American Statistical Association 82, no. 397 (March 1987): 362. http://dx.doi.org/10.2307/2289204.

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6

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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7

Carroll, Patrick, and G. C. Taylor. "Claims Reserving in Non-Life Insurance." Journal of the Royal Statistical Society. Series A (General) 150, no. 2 (1987): 175. http://dx.doi.org/10.2307/2981647.

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8

Diers, Dorothea, Martin Eling, Christian Kraus, and Marc Linde. "Multi‐year non‐life insurance risk." Journal of Risk Finance 14, no. 4 (August 9, 2013): 353–77. http://dx.doi.org/10.1108/jrf-04-2013-0036.

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9

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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10

Asongu, Simplice A. "Technology, Education, Life and Non-life Insurance in Africa." International Journal of Public Administration 43, no. 11 (September 2, 2019): 915–25. http://dx.doi.org/10.1080/01900692.2019.1660994.

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11

Lakstutiene, Ausrine, Rasa Norvaisiene, Jurgita Stankeviciene, and Rytis Krusinskas. "The Impact of Financial Crisis on the Non-Life Insurance Market in Lithuania : Will the Market Growth Expectations be Met?" Olsztyn Economic Journal 10, no. 1 (March 30, 2015): 5–18. http://dx.doi.org/10.31648/oej.3130.

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The article analyzes the insurance market trends, which have been determined by the financial crisis, and reveals the development features and prospects of non-insurance market, which accounts for almost 70% of the total insurance market, in Lithuania. The obtained results showed that after the market shrank by a quarter in 2009, the insurance sector is recovering, and while the volume of issued insurance premiums increased in 2013, they still remain lower than during the pre-crisis period. 2010 is a distinctive year for the Lithuanian non-insurance market due to a track of unusual abundance of adverse events. Throughout the analyzed period the structure of the non-insurance market issued portfolios remained the same in Lithuania, and throughout the whole analyzed period non-insurance market is dominated by Mandatory Third Party Liability Insurance (MTPLI), Voluntary vehicle insurance (CASCO) and Property insurances, which, according to estimates and analysts' forecasts, have growth potential.

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12

Camino-Mogro, Segundo, and Natalia Bermúdez-Barrezueta. "Determinants of profitability of life and non-life insurance companies: evidence from Ecuador." International Journal of Emerging Markets 14, no. 5 (December 2, 2019): 831–72. http://dx.doi.org/10.1108/ijoem-07-2018-0371.

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Purpose The purpose of this paper is is to identify the main determinants of insurance profitability on life and non-life segments to obtain which variables affect in each market of the Ecuadorian insurance sector. Design/methodology/approach The authors use a large panel data set with financial information from 2001 to 2017 and estimate the determinants through a panel corrected standard errors regression. Findings The authors found that net premiums, technical reserves, capital ratio and score efficiency are micro-determinants in the life insurance sector, whereas in the non-life sector, the micro-determinants include also claim level and liquidity ratio; moreover, the authors found that HHI is a determinant of profitability only in the life insurance. Among the macro determinants set, the authors found that the interest rate has also a significant impact both in the life and non-life insurance. Originality/value The authors analyze a dollarized emerging country, which is the first time in this kind of studies. The authors also include the structure-conduct-performance and relative market power paradigm as well as the ES hypothesis, calculated through the data envelopment analysis, as determinants of insurance profitability. Finally, this is the first research to examine the determinants of profitability in Latin American and Caribbean insurers.

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13

Tokutsune, Yasuyuki. "Non-life Insurance Company and Rating Information." Hokengakuzasshi (JOURNAL of INSURANCE SCIENCE) 2020, no. 650 (September 30, 2020): 650_65–650_84. http://dx.doi.org/10.5609/jsis.2020.650_65.

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14

Schlütter, Sebastian. "Optimal taxation in non-life insurance markets." Geneva Risk and Insurance Review 44, no. 1 (August 15, 2018): 1–26. http://dx.doi.org/10.1057/s10713-018-0035-x.

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15

Cassidy, A. A. "AIDS and the Non-Life Insurance Market." Geneva Papers on Risk and Insurance - Issues and Practice 13, no. 4 (October 1988): 332–41. http://dx.doi.org/10.1057/gpp.1988.25.

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16

Ramlau-Hansen, Henrik. "A solvency study in non-life insurance." Scandinavian Actuarial Journal 1988, no. 1-3 (January 1988): 3–34. http://dx.doi.org/10.1080/03461238.1988.10413835.

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17

Ramlau-Hansen, Henrik. "A solvency study in non-life insurance." Scandinavian Actuarial Journal 1988, no. 1-3 (January 1988): 35–59. http://dx.doi.org/10.1080/03461238.1988.10413836.

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18

Djuric, Zlata. "Collective risk model in non-life insurance." Ekonomski horizonti 15, no. 2 (2013): 163–72. http://dx.doi.org/10.5937/ekonhor1302163d.

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19

Darooneh, A. H. "Non-life insurance pricing: multi-agent model." European Physical Journal B 42, no. 1 (November 2004): 119–22. http://dx.doi.org/10.1140/epjb/e2004-00363-x.

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20

Ohlsson, Esbjörn, and Jan Lauzeningks. "The one-year non-life insurance risk." Insurance: Mathematics and Economics 45, no. 2 (October 2009): 203–8. http://dx.doi.org/10.1016/j.insmatheco.2009.06.001.

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21

Bohnert, Alexander, Nadine Gatzert, and Andreas Kolb. "Assessing inflation risk in non-life insurance." Insurance: Mathematics and Economics 66 (January 2016): 86–96. http://dx.doi.org/10.1016/j.insmatheco.2015.11.003.

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22

de, Andrés-Sánchez. "Fuzzy claim reserving in non-life insurance." Computer Science and Information Systems 11, no. 2 (2014): 825–38. http://dx.doi.org/10.2298/csis121225045a.

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This paper develops several expressions to quantify claim provisions to account in financial statements of a non-life insurance company under the hypothesis of a fuzzy environment. Concretely, by applying the expected value of a fuzzy number and the more general concept of value of a fuzzy number to the ANOVA claim predicting model [2] we estimate claim reserves to account in insurer?s balance sheet and income account.

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23

Abidin, Zaenal, and Emilyn Cabanda. "EFFICIENCY OF NON-LIFE INSURANCE IN INDONESIA." Journal of Economics, Business, and Accountancy | Ventura 14, no. 3 (December 17, 2011): 197. http://dx.doi.org/10.14414/jebav.v14i3.46.

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24

Grize, Yves‐Laurent, Wolfram Fischer, and Christian Lützelschwab. "Machine learning applications in non‐life insurance." Applied Stochastic Models in Business and Industry 36, no. 4 (July 2020): 545–47. http://dx.doi.org/10.1002/asmb.2564.

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25

Beirlant, Jan, and Jozef L. Teugels. "Modeling large claims in non-life insurance." Insurance: Mathematics and Economics 11, no. 1 (April 1992): 17–29. http://dx.doi.org/10.1016/0167-6687(92)90085-p.

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26

Tian, Ling, Shi-Jie Jiang, Guochen Pan, and Ning Zhang. "Non-life insurance price dynamics: evidence from the Chinese insurance market." Economic Research-Ekonomska Istraživanja 31, no. 1 (January 2018): 171–87. http://dx.doi.org/10.1080/1331677x.2018.1424557.

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27

Mani Upadhyaya, Yadav, Khom Raj Kharel, Narayan Prasad Aryal, and Basu Dev Lamichhane. "Contribution of the non-life insurance sector to the economic growth of Nepal: Analysis from the EGLS approach." Insurance Markets and Companies 15, no. 1 (March 15, 2024): 30–39. http://dx.doi.org/10.21511/ins.15(1).2024.03.

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Nepal’s non-life insurance sector holds immense potential to drive economic growth and boost the nation towards a secure financial future. Embracing this potential is a goal and a pivotal catalyst for substantial change. The study aims to determine how the non-life insurance sector can drive economic growth in Nepal. The methodology of this study uses quantitative analysis of financial data from 2013 to 2022 from 20 non-life insurance companies in Nepal and econometric modeling to assess the sector’s impact on economic growth. Using Panel EGLS (Estimated Generalized Least Squares) regression analysis, the findings show that with one-unit increments in total investment, total premium, and total tax paid, GDP is expected to change by approximately 591.52, –920.54 and 8,470.65 units, respectively. In contrast, the coefficient for total profit is –910.3477 and is not statistically significant. The study’s main conclusion implies that the insurance sector contributes to the country’s economic growth by investing in productive activities and paying taxes to the government. Still, it also imposes a cost on the economy by charging high premiums to the insured. The profitability of the insurance sector does not affect the GDP, which indicates that the insurance sector is competitive and efficient or that other factors determine the GDP besides the insurance sector. This study contributes to a deeper understanding of the non-life insurance sector’s role in Nepal’s economic development and informs evidence-based policy decisions.

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28

Wang, S. "Risk loads in life/non-life insurance: a unified approach." Insurance: Mathematics and Economics 17, no. 3 (April 1996): 236. http://dx.doi.org/10.1016/0167-6687(96)82373-0.

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29

Chun, Heuiju, and Byunghak Leem. "Face/non-face channel fit comparison of life insurance company and non-life insurance company using social network analysis." Journal of the Korean Data and Information Science Society 25, no. 6 (November 30, 2014): 1207–19. http://dx.doi.org/10.7465/jkdi.2014.25.6.1207.

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30

Pakhnenko, O., O. Zhuravka, V. Podhorna, and A. Sukhomlyn. "ANALYSIS OF COMPETITIVE POSITIONS OF INSURANCE COMPANIES IN THE NON-LIFE INSURANCE MARKET IN UKRAINE." Vìsnik Sumsʹkogo deržavnogo unìversitetu, no. 2 (2019): 88–94. http://dx.doi.org/10.21272/1817-9215.2019.2-11.

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The paper explores the practical aspects of forming a competitive environment in the non-life insurance market of Ukraine and analyzes the competitiveness and financial performance of leading insurance companies. Based on the analysis of non-life insurance market concentration indicators, the authors concluded that there is no clear leader in this market, the level of market concentration is negligible. Based on the analysis of non-life insurance market leaders by volume of gross insurance premiums in the whole market and by main types of non-life insurance (CASCO, motor vehicle liability insurance, property insurance, fire and catastrophe risk insurance, CARGO, health insurance) the authors found that the leadership of insurance companies in the market does not mean their leadership in all types of non-life insurance; some insurance companies specialize in certain types of insurance and not being leaders in the insurance market at all occupy leading positions in certain segments of non-life insurance market. In order to provide a general assessment of the competitiveness of individual insurance companies in the non-life insurance market, the following indicators were selected: the volume of gross insurance premiums, gross insurance payments, insurance reserves and the amount of equity. In order to assess the size of market share of an individual insurance company in a more objective way, it is suggested to calculate the average share of the insurance company. The calculations made it possible to identify the leaders of the non-life insurance market in 2018 and to explore the dynamics of changes in their competitive position during 2016-2018. For the three insurance companies that have been identified as the leaders of the Ukrainian market non-life insurance in 2018 (“UNIKA”, “AXA Insurance” and “PZU Ukraine”), the authors analyzed the main indicators of their financial condition, namely the profitability of insurance services, profitability of sales, return on assets, return on equity, overall liquidity, absolute liquidity and autonomy. It was found that all the analyzed insurance companies are profitable, however, among the three leading Ukrainian insurance companies, the most effective in 2018 was the insurance company “PZU Ukraine” and the least profitable – “UNIKA”. Keywords: competitiveness, insurance company, market concentration, market share, competition.

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31

Ilyas, Ashiq Mohd, and S. Rajasekaran. "Productivity of the Indian non-life insurance sector." International Journal of Productivity and Performance Management 69, no. 4 (October 21, 2019): 633–50. http://dx.doi.org/10.1108/ijppm-04-2019-0147.

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Purpose The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of total factor productivity (TFP) over the period 2005–2016. Design/methodology/approach This study utilises Färe‒Primont index (FPI) to access the change in TFP and its components: technical change, technical efficiency and mix and scale efficiency over the observation period. Moreover, it employs the Mann–Whitney U-test to scrutinise the difference between the public and the private insurers in terms of growth in productivity. Findings The results reveal that the insurance sector possesses a very low level of TFP. Also, the results divulge an improvement of 11.98 per cent in TFP of the insurance sector at an annual average rate of 12.41 per cent over the observation period. The growth in productivity is mainly attributable to the improvement of 10.81 per cent in the scale‒mix efficiency. The progress in scale‒mix efficiency is mainly the result of improvements in residual scale and residual mix efficiency. The results also show that the privately owned insurers have experienced a high productivity growth rate than the state-owned insurers. Practical implications The results hold practical implications for the regulators, policymakers and decision makers of the Indian non-life insurance companies. Originality/value This study is the first of its kind to use FPI, which satisfies all economically relevant axioms and tests defined by the index number theory to comprehensively access the change in TFP of the Indian non-life insurance sector.

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32

Lee,Chang-Soo and 김성민. "Assessing Adequacy of Deposit Insurance Target Fund in Non-Life Insurance Sector." Journal of Risk Management 30, no. 2 (June 2019): 33–76. http://dx.doi.org/10.21480/tjrm.30.2.201906.002.

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33

Outreville, J. Francois. "Indexed and non-indexed insurance and the growth of group life insurance." Economics Letters 19, no. 2 (January 1985): 149–53. http://dx.doi.org/10.1016/0165-1765(85)90012-6.

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34

Steffensen, Mogens, and Julie Thøgersen. "PERSONAL NON-LIFE INSURANCE DECISIONS AND THE WELFARE LOSS FROM FLAT DEDUCTIBLES." ASTIN Bulletin 49, no. 1 (January 2019): 85–116. http://dx.doi.org/10.1017/asb.2018.40.

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AbstractWe view the retail non-life insurance decision from the perspective of the insured. We formalize different consumption–insurance problems depending on the flexibility of the insurance contract. For exponential utility and power utility we find the optimal flexible insurance decision or insurance contract. For exponential utility we also find the optimal position in standard contracts that are less flexible and therefore, for certain nonlinear pricing rules, lead to a welfare loss for the individual insuree compared to the optimal flexible insurance decision. For the exponential loss distribution, we quantify a significant welfare loss. This calls for product development in the retail insurance business.

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35

Steffensen, Mogens, and Julie ThøGersen. "PERSONAL NON-LIFE INSURANCE DECISIONS AND THE WELFARE LOSS FROM FLAT DEDUCTIBLES." ASTIN Bulletin 49, no. 01 (January 2019): 85–116. http://dx.doi.org/10.1017/asb.2019.40.

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AbstractWe view the retail non-life insurance decision from the perspective of the insured. We formalize different consumption–insurance problems depending on the flexibility of the insurance contract. For exponential utility and power utility we find the optimal flexible insurance decision or insurance contract. For exponential utility we also find the optimal position in standard contracts that are less flexible and therefore, for certain nonlinear pricing rules, lead to a welfare loss for the individual insuree compared to the optimal flexible insurance decision. For the exponential loss distribution, we quantify a significant welfare loss. This calls for product development in the retail insurance business.

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36

Hamal, Janga Bahadur. "Factors Affecting Profitability of Nepalese Non-Life Insurance Companies." Journal of Nepalese Business Studies 13, no. 1 (December 31, 2020): 23–35. http://dx.doi.org/10.3126/jnbs.v13i1.34701.

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The study examines the impacts of liquidity ratio, leverage ratio, firm size, age of the firm and total debt on the profitability of non-life insurance companies in Nepal. The dependent variable in the study is the return on asset (ROA), which is used as a measure of profitability. The study is based on secondary data of nine non-life insurance companies studied over a period of ten years, from 2066/67 to 2075/76. The data were collected from the financial statements published annually by the selected non-life insurance companies. Descriptive statistics, correlational analysis and regression models have been employed in order to test the impacts as well as the significance of the selected independent variables on ROA. The study concludes that the profitability of Nepalese non-life insurance companies increases with the increase in liquidity but decreases with the increase in leverage. However, the study establishes the insignificant relationship of firm size, firm age and total debt with profitability for the sector. The study thus suggests that non-life insurance companies should focus on the proper management of capabilities to pay liabilities to enhance profitability and also try to maintain a smaller value of leverage ratio to handle the above-average losses.

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37

Oluwaleye, Taiwo Olarinre, Sunday Stephen Ajemunigbohun, and Kehinde Emmanuel Abiodun. "Underwriting Operations and Financial Performance: Evidence from Non-Life Insurance Firms in Nigeria." Business Economic, Communication, and Social Sciences Journal (BECOSS) 5, no. 3 (October 13, 2023): 167–76. http://dx.doi.org/10.21512/becossjournal.v5i3.10232.

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This study investigated the relationship between underwriting operations and the performance of non-life insurance firms in Nigeria. Specifically, the study examined the combined metrics of claim ratio, insurance premium, and re-insurance claims on the return on assets using the non-life insurance firms as bases in Nigeria. The study engaged data within the period of 2011-2020 extracted from the financial statements of five selected non-life insurance firms and analysis of data was conducted with a panel regression procedure. The result showed that insurance premium had a positive significant effect on return on asset, while re-insurance claim and claim ratio had positive but insignificant effect on return on asset. Therefore, this study concluded that underwriting operations contribute to the performance of non-life insurance firms in Nigeria. Hence, this study recommended that non-life insurance firms should put more effort into harnessing strategies and resources that enhances insurance premium in order to improve financial performance.

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38

Arslan, Pınar. "EFFICIENCY IN NON-LIFE INSURANCE COMPANIES (2006-2016)." e-Journal of New World Sciences Academy 16, no. 1 (January 30, 2021): 45–58. http://dx.doi.org/10.12739/nwsa.2021.16.1.4c0242.

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39

Hanafy, Mohamed. "Application of Generalized Pareto in Non-Life Insurance." Journal of Financial Risk Management 09, no. 03 (2020): 334–53. http://dx.doi.org/10.4236/jfrm.2020.93018.

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40

Dina Manolache, Aurora Elena. "Chain claims reserving methods in non-life insurance." Proceedings of the International Conference on Applied Statistics 1, no. 1 (October 1, 2019): 216–25. http://dx.doi.org/10.2478/icas-2019-0019.

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Abstract Considering that the reliability of reserves valuation directly influences the financial strength of an insurance company, the main aim of this paper is to present a claims reserving estimation for a Romanian non-life insurer based on the most popular chain methods which are typically used in practice for the estimation of outstanding claims reserves in general insurance industry: Standard Chain Ladder and Munich Chain Ladder both on the claims incurred data and claims paid data. The tail development factors have been estimated based on the curve-fitting methods. The obvious advantage of these methods is represented by its simplicity of the practicality application. The results of the research under two chain claims reserving models reveal significant differences between the Standard Chain Ladder and Munich Chain Ladder with respect to the claims reserves level. Probably the Standard Chain Ladder based on paid method underestimates the outstanding loss liabilities and Standard Chain Ladder based on Incurred method overestimates the claims reserves. The claims reserves predictions under the Paid Munich Chain Ladder and Incurred Munich Chain Ladder are between the two Standard Chain Ladder outstanding loss liabilities estimates. The results of the tail extrapolation shown that the incorporation of the tail factors can have a significant impact on claims predictions.

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41

Norberg, Ragnar. "Prediction of Outstanding Liabilities in Non-Life Insurance." ASTIN Bulletin 23, no. 1 (May 1993): 95–115. http://dx.doi.org/10.2143/ast.23.1.2005103.

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AbstractA fully time-continuous approach is taken to the problem of predicting the total liability of a non-life insurance company. Claims are assumed to be generated by a non-homogeneous marked Poisson process, the marks representing the developments of the individual claims. A first basic result is that the total claim amount follows a generalized Poisson distribution. Fixing the time of consideration, the claims are categorized into settled, reported but not settled, incurred but not reported, and covered but not incurred. It is proved that these four categories of claims can be viewed as arising from independent marked Poisson processes. By use of this decomposition result predictors are constructed for all categories of outstanding claims. The claims process may depend on observable as well as unobservable risk characteristics, which may change in the course of time, possibly in a random manner. Special attention is given to the case where the claim intensity per risk unit is a stationary stochastic process. A theory of continuous linear prediction is instrumental.

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42

Hössjer, Ola, Bengt Eriksson, Kajsa Järnmalm, and Esbjörn Ohlsson. "Assessing Individual Unexplained Variation in Non-Life Insurance." ASTIN Bulletin 39, no. 1 (May 2009): 249–73. http://dx.doi.org/10.2143/ast.39.1.2038064.

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AbstractWe consider variation of observed claim frequencies in non-life insurance, modeled by Poisson regression with overdispersion. In order to quantify how much variation between insurance policies that is captured by the rating factors, one may use the coefficient of determination, R2, the estimated proportion of total variation explained by the model. We introduce a novel coefficient of individual determination (CID), which excludes noise variance and is defined as the estimated fraction of total individual variation explained by the model. We argue that CID is a more relevant measure of explained variation than R2 for data with Poisson variation. We also generalize previously used estimates and tests of overdispersion and introduce new coefficients of individual explained and unexplained variance.Application to a Swedish three year motor TPL data set reveals that only 0.5% of the total variation and 11% of the total individual variation is explained by a model with seven rating factors, including interaction between sex and age. Even though the amount of overdispersion is small (4.4% of the noise variance) it is still highly significant. The coefficient of variation of explained and unexplained individual variation is 29% and 81% respectively.

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43

KAWAGUCHI, Masaaki. "Strong Wind Damage and The Non-Life Insurance." Wind Engineers, JAWE 1995, no. 64 (1995): 49–59. http://dx.doi.org/10.5359/jawe.1995.64_49.

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44

Ramakrishnan, A., and S. Sudalaimuthu. "Application of ANN in non-life insurance industry." International Journal of Advanced Operations Management 3, no. 3/4 (2011): 239. http://dx.doi.org/10.1504/ijaom.2011.045457.

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45

Prosperetti, Luigi. "Economies of Scale in Italian Non-Life Insurance." Geneva Papers on Risk and Insurance - Issues and Practice 16, no. 3 (July 1991): 282–92. http://dx.doi.org/10.1057/gpp.1991.20.

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46

Doganjić, Jelena. "Premium adequacy risk management in non-life insurance." Ekonomski pogledi 17, no. 4 (2015): 85–98. http://dx.doi.org/10.5937/ekopog1501085d.

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47

Kawaguchi, Masaaki. "Wind Damage and Non-Life Insurance in Japan." IFAC Proceedings Volumes 31, no. 28 (September 1998): 153–57. http://dx.doi.org/10.1016/s1474-6670(17)38489-6.

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48

Norberg, R. "Prediction of outstanding liabilities in non-life insurance." Insurance: Mathematics and Economics 13, no. 2 (November 1993): 159. http://dx.doi.org/10.1016/0167-6687(93)90897-x.

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49

Maringe, Noah. "A double-edged sword: The role of insurable interest in non-indemnity insurance in the light of the Covid-19 pandemic in Zimbabwe." De Jure 55, no. 1 (June 10, 2022): 1–12. http://dx.doi.org/10.17159/2225-7160/2022/v55a3.

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The on-going Covid-19 pandemic has affected many facets of life including the business of insurance. It raises fresh questions as to who can insure the life of another because it has led to the loss of many lives. This brings one to the requirement of insurable interest in non-indemnity insurance. This paper argues that the requirement should be removed as it adds confusion to this branch of insurance law. It further limits the categories of people who can insure the lives of others. The Insurable Interest Bill of England attempts to expand the circumstances where one can have insurable interest. It is a step in the right direction as it represents liberalisation of that requirement. However, there are persuasive decisions in both Zimbabwe and South Africa which held that the existence of insurable interest should be a mere factor in deciding whether a contract is one of insurance or a mere wager. It is submitted that this decision can still be arrived at in some cases without even considering the existence of insurable interest. Removing the requirement of insurable interest has precedence as it has happened in other jurisdictions such as New Zealand and Australia. In fact, the intention of the Life Assurance Act 1 774 has not been achieved in real practice. The legal principles surrounding insurable interest in non-indemnity insurance are not only confused but they are confusing. Thus, the paper proposes for other facts which may be taken into account in determining whether a contract is one of insurance and not a wager. Such factors may include the age of the parties, the intention of the parties, the relationship of the parties and the consent of the insured. The emphasis should be on determining whether a contract is not a wager rather than to rely solely on the presence or absence of insurable interest.

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Ćurak, Marijana, Sandra Pepur, and Dujam Kovač. "Does financial literacy make the difference in non-life insurance demand among European countries?" Ekonomski pregled 71, no. 4 (2020): 359–82. http://dx.doi.org/10.32910/ep.71.4.3.

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Abstract:

While risk aversion and affordability of insurance are considered as the most important determinants of non-life insurance demand, understanding and knowledge of complex non-life insurance products are less researched. Studies on insurance demand conducted at the cross-section level, which include education, usually use it as a proxy for risk aversion and to a limited extent as a measure of financial literacy. Moreover, the general level of education does not accurately reflect the level of understanding of sophisticated insurance instruments. Consequently, the main aim of this research is to analyse the impact of financial literacy on the demand for non-life insurance by applying a more precise measure of financial literacy. The empirical analysis is based on the dataset of 38 European countries in the period from 2010 to 2016 and is done using the panel data analysis technique. Research findings confirm that financial literacy makes the difference in non-life insurance demand among European countries, while controlling for other economic, social/cultural, market structure and institutional determinants of non-life insurance demand. The paper contributes to the literature on non-life insurance demand, especially the one on the relationship between financial literacy and the demand for non-life insurance.

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