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The non-life insurance sector operates within a highly competitive and tightly regulated framework, confronting a pivotal juncture in the formulation of pricing strategies. Insurers are compelled to harness a range of statistical methodologies and available data to construct optimal pricing structures that align with the overarching corporate strategy while accommodating the dynamics of market competition. Given the fundamental societal role played by insurance, premium rates are subject to rigorous scrutiny by regulatory authorities. Consequently, the act of pricing transcends mere statistical calculations and carries the weight of strategic and societal factors. These multifaceted concerns may drive insurers to establish equitable premiums, considering various variables. For instance, regulations mandate the provision of equitable premiums, considering factors such as policyholder gender. Or mutualist groups in accordance with respective corporate strategies can implement age-based premium fairness. In certain insurance domains, the presence of serious illnesses or disabilities are emerging as new dimensions for evaluating fairness. Regardless of the motivating factor prompting an insurer to adopt fairer pricing strategies for a specific variable, the insurer must possess the capability to define, measure, and ultimately mitigate any fairness biases inherent in its pricing practices while upholding standards of consistency and performance. This study seeks to provide a comprehensive set of tools for these endeavors and assess their effectiveness through practical application in the context of automobile insurance. Results show that fairness bias can be found in historical data and models, and that fairer outcomes can be obtained by more fairness-aware approaches. [ABSTRACT FROM AUTHOR]

Currently, there is a lot of research in the field of neural networks for non-life insurance pricing. The usual goal is to improve the predictive power of actuarial pricing and behavioral models via neural networks while building upon the generalized linear model, which is the current industry standard. Our paper contributes to this current journey via novel methods to enhance actuarial non-life models with transformer models for tabular data. We build here upon the foundation laid out by the combined actuarial neural network as well as the localGLMnet and enhance those models via the feature tokenizer transformer. The manuscript demonstrates the performance of the proposed methods on a real-world claim frequency dataset and compares them with several benchmark models such as generalized linear models, feed-forward neural networks, combined actuarial neural networks, LocalGLMnet, and the pure feature tokenizer transformer. The paper shows that the new methods can achieve better results than the benchmark models while preserving the structure of the underlying actuarial models, thereby inheriting and retaining their advantages. The paper also discusses the practical implications and challenges of applying transformer models in actuarial settings. [ABSTRACT FROM AUTHOR]

Generally, insurance companies face challenges in determining the Capital Requirement (CR) which is essential for business continuity. This situation could worsen if substantial number and amount of claims are requested by policyholders due to losses from catastrophic events such as floods, tsunamis, and earthquakes. In this paper, we propose a structured model to determine the CR for non-life insurance companies through loss ratios. Firstly, dependence structure among the business lines is modelled using copulas from both Elliptical and Archimedean copulas family with loss ratios from four business lines as the risk factors. The loss ratios are derived from incurred claims and earned premiums data of Malaysia’s non-life insurance businesses, such as Fire insurance, Motor insurance, Marine, Aviation and Transport (MAT) insurance, and Miscellaneous insurance. Subsequently, a combination of popular risk measures such as Value-at-Risk (VaR) and TailValue-at-Risk (TVaR) with a Drawable Vine (D-Vine) copulas model known as hybrid model is developed to estimate insurance companies’ risk capital. Finally, a simulation of Monte Carlo is commanded by calibrating data with our selected model to project the CR. This study contributes to the literature by addressing the problem of determining the appropriate CR for non-life insurance companies, proposing an empirical model based on real non-life insurance company data with a hybrid model built from risk measures and D-Vine copula. [ABSTRACT FROM AUTHOR]

The cost of medical treatment is a sudden burden on the person’s budget. To avoid the risk, the health insurance becomes a safeguard. The scenario of nonlife insurance (specifically health insurance) penetration is not encouraging in India. The penetration of non-life insurance (ratio of premium to GDP) has increased from 0.80 per cent in 2013 to 0.94 per cent in 2019. Also, health insurance density is lower in India. The objective of this paper is to analyze the various factors affecting the decision of health insurance take up by household in India. The NSSO 75th round unit level data on health has been taken into consideration. Out of the 113,823 sample households, 113,821 were taken for this study. Multivariate logistic regression analysis is used to analyze the determinants of decision of taking up of health insurance. The results show that, there is a lower share of health insurance take up in rural area than urban. The social group wise difference is found in purchase of health insurance. The richest wealth quintile has greater probability of taking up health insurance than lower wealth quintile; female household head has greater probability of taking up of health insurance than male counterpart. Moreover, level of education affects positively, the health expenditure support from employer, support from government is negatively associated with the taking up of health insurance. Chronic ailments and hospitalization are positively and negatively related with health insurance being taken up respectively. This study suggests that, there is a need of health insurance related awareness and initial support from government to increase penetration of health insurance in India. [ABSTRACT FROM AUTHOR]

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler four-variate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums.

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.

AbstractThe performance of non-life insurers is essential to the economy because of their role in mitigating the risks firms and households face. This study provides a comprehensive overview of studies examining factors affecting non-life insurers’ performance. Based on 235 studies published between 1990 and 2021, the review demonstrates that firm-level factors such as size, organisational form, diversification, capital structure, risk, reinsurance, corporate governance, distribution system, and group affiliation, and external factors such as market structure, macroeconomic, financial, and institutional development are the major determinants of non-life insurers’ performance. Although the empirical evidence on the effect of these factors is generally mixed, firm size, capitalisation, risk, macroeconomic conditions, and, to some extent, corporate governance and market structure issues show a clear relationship with insurer performance. One of the implications of this study is that there may be a need for increased solvency surveillance, especially for smaller insurers, which appear to have a higher risk of insolvency than their larger counterparts.

Underwriting is crucial for insurers’ performance and sustainability, yet empirical evidence on factors influencing its performance has received limited attention. Therefore, this study investigates this phenomenon considering South Africa’s life and non-life insurance sectors between 2013–2019. Using the generalized method of moments and the bootstrap quantile regression techniques for analysis, the findings show that insurance size, market share, and investment income significantly and positively impact underwriting performance. However, premium growth weakens the underwriting performance of both sectors. Also, non-life insurance shows a positive relationship for reinsurance, while life insurers present an inverse relationship. Underwriting risk negatively impacts non-life performance across quantiles, while life insurer risk was negative in higher quantiles. More so, initial solvency levels weaken underwriting performance, while extreme increase improves it, implying a direct U-shaped relationship. Hence, highly solvent insurers are more likely to succeed in underwriting operations. Additionally, market share complements return on assets in promoting underwriting performance. Policy recommendations are discussed. [ABSTRACT FROM AUTHOR]

Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA's effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector. [ABSTRACT FROM AUTHOR]

The traditional modeling of dependence among branches in non-life insurance has historically rested on the assumption of independence between risks. However, recent studies have brought to light that overlooking the interdependence between risks is not prudent. An illustrative example is found in automobile insurance, where a discernible connection exists between the number of claims in the Automobile Damage branch and those in the Automobile Civil Liability branch. This paper aims to delve into the utilization of copula functions for modeling the interrelationships among claims charges across diverse branches within a non-life insurance portfolio. Moreover, it seeks to assess the consequential impacts of these dependencies on the capital requirements of AXA Insurance in the Moroccan market. Our research concentrates on four pivotal branches within AXA Insurance, namely "work accidents", "automobile civil liability", "disability", and "fire, marine, aviation, and transport". Through a thorough analysis encompassing dependency measurements, independence tests, and various graphical representations, we have substantiated the existence of interdependencies among the claim costs associated with these four risk branches. Importantly, our findings underscore that assuming independence leads to a slight underestimation of the capital requirement needed for effective risk accumulation management by the insurer. [ABSTRACT FROM AUTHOR]

Insurance promotes financial resilience for individuals and households, aiding them in risk management and welfare improvement. Despite concerted efforts by countries and international bodies to promote financial inclusion through various programs, the roles of life and non-life insurance remain underemphasized as measure to close the financial gap in most countries. Our research is motivated by the realization that the existing literature lacks a unified measure of financial inclusion that considers the impact of life and non-life insurance. The objective of this study is to examine the roles of insurance and banking services in financial inclusion through an innovative measure. This study employs Principal Component Analysis to compute different sets of a multidimensional Financial Inclusion Index (FI index) that incorporate banking, life, and non-life insurance indicators for 72 countries from 2015 to 2019. When the role of life insurance is considered, some countries experience significant leaps in their financial inclusion levels, while the opposite holds true for most countries regardless of their developmental stage. In the meantime, non-life insurance has a strong positive impact on the overall financial inclusivity of the sample countries, with more than half of them recording higher financial inclusion. These results necessitate a reevaluation of financial inclusion strategies worldwide. We advocate for a more comprehensive approach that integrates insurance services into the financial system alongside traditional banking, especially non-life insurance given its significant contribution in enabling financial inclusion. [ABSTRACT FROM AUTHOR]

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler fourvariate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums. [ABSTRACT FROM AUTHOR]

Estimating the expected capital and its variability is a crucial objective for a non-life insurance company, which enables the firm to develop effective management strategies. Many studies have been devoted to this topic, with simulative approaches being especially employed for solving the complexity of the interacting risks, not manageable through closed-form solutions. In this paper, we present a realistic framework based on Solvency II for the definition of next-year capital of a non-life insurer, including reinsurance treaties and counterparty default risk, in a multi-line of business setting. We determine the mean and variance of the stochastic capital considering both quota share and excess-of-loss reinsurance. We show how these closed-form results enable the analysis of many different real-world strategies, granting the insurer the possibility of choosing the optimal policy without the computational resources and time constraints required by simulative approaches. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the impact of dependencies between climate transition and physical risks on the default probability and profitability of a non-life insurer focusing on the scenario of a delayed and sudden transition. Toward this end, we suggest a simplified modeling approach for scenario analyses for climate risks affecting assets and liabilities, taking into account potential nonlinear dependence structures. Our results show that dependencies on the liability side and between assets and liabilities in the context of physical-transition scenarios can have a significant impact, particularly on the default risk of a non-life insurer. We additionally analyze the mitigating effects of stop loss reinsurance and risk-adjusted pricing, which—if implementable—seem to be an effective risk management measure against physical climate risks in particular. [ABSTRACT FROM AUTHOR]

This paper investigates the institutional determinants of insurance demand in Africa. We used a panel of 42 countries over the period 1996–2017. A system GMM approach was used for the estimations. Consistent with previous results, we find that institutional quality has positive and significant effects on insurance penetration in Africa. Specifically, regulatory quality, rule of law, control of corruption, political stability and absence of violence, and government effectiveness are the five institutional quality indicators that have positive and significant effects on the demand for total insurance and life insurance. However, only regulatory quality, control of corruption and government effectiveness are positively associated with non-life insurance demand. This indicates that governments should improve the business environment and strengthen the political environment to boost insurance development in Africa. [ABSTRACT FROM AUTHOR]

AbstractThis study aimed to examine the macroeconomic and firm-specific determinants of financial performance using 121 listed non-life insurance companies from 48 African countries for the period 2008–2019. Panel data of 1452 observations were examined using both ordinary least squares and two-step System Generalised Method of Moments estimators. The findings of this study show that lagged return on assets, equity capital, operational efficiency and leverage, investment capability and gross domestic product are the statistically significant determinants of financial performance in African non-life insurance companies even though equity capital, operational efficiency and leverage are inversely significant. It is concluded that insurance industries, policymakers, government and investors should take into consideration these significant factors in taking decision and improving their performance. Also, it is recommended that the capital structures of the sector should be restructured to maintain a favourable balance in the equity and debt of the companies. Also, mechanisms such as automated systems that can reduce operational cost should be adopted such that financial performance can be enhanced.

Several distributions and families of distributions are proposed to model skewed data, e.g., with skew-normal and related distributions. Lambert W random variables offer an alternative approach in which, instead of constructing a new distribution, a certain transformation is proposed. Such an approach allows the construction of a Lambert W skewed version from any distribution. Here, we choose the Lambert W normal distribution as a natural starting point and include the Lambert W exponential distribution due to the simplicity and shape of the exponential distribution, which, after skewing, may produce a reasonably heavy tail for loss models. In the theoretical part, we focus on the mathematical properties of obtained distributions, including the range of skewness. In the practical part, the suitability of the corresponding Lambert W transformed distributions is evaluated on real insurance data. Finally, the results are compared with those obtained using common loss distributions. [ABSTRACT FROM AUTHOR]

This paper tests potential determinants of the development of the insurance sector. Using a rich dataset for 24 European countries spanning two decades, we identify a set of macrofinancial factors that are the most robust predictors of growth of gross premiums in the life and non-life insurance sectors. We show that both life and non-life premiums co-move with the business cycle and are positively related to higher savings and a more developed financial system. In addition, we provide new evidence on the role of market concentration and price effects. We find that market concentration matters only for life insurance, whereas the price channel is significant only for non-life insurance. From a policy perspective, our empirical estimates can be used to refine the existing macroprudential stress tests of the insurance sector. [ABSTRACT FROM AUTHOR]

The study assesses the determinants of the financial stability of the Czech commercial insurance companies within the period 2004-2019. The eight macroeconomic determinants of the financial stability of insurance are analysed for both insurers, i.e., with predominated life and non-life insurance. The generalized method of moments is used for empirical analysis. The results show four macroeconomic variables were statistically significant determinants of financial stability in the case of non-life insurers and three determinants influenced the financial stability of life insurers. The study concludes that the determinants of the financial stability of non-life insurers were different that in the case of life insurers. Moreover, the impact of several determinants is also different in case life and non-life insurers in the Czech Republic.

Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector.

Weather-related risk makes the insurance industry inevitably concerned with climate and climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise to compensations for the induced physical damages and business interruptions. In this work, we establish a nationwide, building-specific risk score for water damage associated with pluvial flooding in Norway. We fit a generalised additive model that relates the number of water damages to a wide range of explanatory variables that can be categorised into building attributes, climatological variables, and topographical characteristics. The model assigns a risk score to every location in Norway, based on local topography and climate, which is not only useful for insurance companies but also for city planning. Combining our model with an ensemble of climate projections allows us to project the (spatially varying) impacts of climate change on the risk of pluvial flooding towards the middle and end of the 21st century. [ABSTRACT FROM AUTHOR]

In this digital era, customers in the insurance sector always look for better insurance products and services at an affordable price. When customers are unsure about service, they switch over to a better service provider. This behavior is more relevant to non-life insurance. However, the switching behavior of customers is hampered by certain switchover barriers such as “brand consciousness”, “brand pride”, “brand loyalty”, etc. This study focuses on exploring switching intentions and switching behaviors of adults in India keeping “brand love” as a mediator. A structured questionnaire was employed to collect the primary data from adults having non-life insurance products to analyze switching intentions and switching behaviors. The collected data were analyzed employing SPSS software and Hayes Process Model and appropriate statistical tools. The study results show that the switching intentions of adults vary based on their age, annual income, and education. Mean scores reveal that the lesser the age, the higher the intention to switch over. Further, based on annual income, adults who earn up to Rs 2 lakhs annually have more switching-over intentions (Mean score: 3.9719) followed by adults who earn Rs more than 2 lakhs to 5 lakhs annually (Mean score: 3.7590). Mean scores of education levels regarding switching intentions are higher among more educated adults and less among those who are qualified up to the school level.

This study investigated the relationship between insurance and economic growth in Nigeria for the period 1998-2020. This study employed autoregressive distributed lag model (ARDL) estimation techniques in analyzing the data. Real Gross domestic product (RGDP) was used as proxy for economic growth while gross insurance premium (GIP), life insurance premium (LIP), non-life insurance premium (NLIP), human capital (HCAP), physical capital (PCAP), total insurance investment (TINV) and trade openness (OPEN) were used as proxies for insurance sector predictive variables. The result of the ARDL estimate revealed that the first lag of real gross domestic product (RGDP), non-life insurance premium (NLIP) and human capital (HCAP) were positively and statistically significant in explaining the variation in economic growth in Nigeria both in the shortrun and longrun. It is therefore recommended that Nigeria as a nation in seeking the welfare of its citizens must as a matter of priority, need to strengthen, build and develop a stable and vibrant insurance industry to foster all round economic growth and development. More, policies and programmes aimed at improving the quality of human capital such education, health entrepreneurial and skill acquisition should be pursued vigorously in Nigeria. [ABSTRACT FROM AUTHOR]

A well-developed and functioning insurance sector is a pre-requisite for an inclusive economy and growth of any country. The insurance sector has been growing steadily and gradually in India. Insurance is an essential financial service because it provides financial security to individuals and business persons. The efficiency of the insurance companies is measured by the techniques such as financial performance, technical, purely technical and scale efficiencies. This research work aims at analyzing the financial performance of public sector non- life Indian insurance companies and the determinants of such performance. Commission, claims incurred, investment income, the net premium earned, management soundness and operating expenses are considered determinants of the financial performance of non-life insurance companies. Data are collected from the financial year 2009-10 to the financial year 2021-22 to determine financial performance. Data were checked for their normality and stationarity using EViews statistical software. Research results convey that New India Insurance Company Limited performs better in financial performance followed by United India Insurance Company Limited. Common determinants of net profit after tax of public sector non-life insurance companies are claims incurred and net premium earned. [ABSTRACT FROM AUTHOR]

The main goal of insurance company management is to increase shareholdersâ value and implement a strategy that promotes sustainable growth of the company. Well-known possible measures intended to achieve that goal are as follows: share price, economic value, market capitalisation, gross premiums earned and solvency ratio. These measures include efficient capital management as capital expenses could be a major cost position depending on risk appetite and the extent of capital needed to support it. This research focuses on non-life insurers for reserve risk modelling. In the current study, a more accurate risk quantification model has been developed than the standard model provided by the EU regulator under the Solvency II framework. The proposed model provides capital cost gains as well. A case study based on non-life real data set with underwriting in the Baltic countries is discussed with inclusion of pandemic trends that had an impact on economies and customer behaviours. The study considers different non-life reserve distributions for each insurance business line, risk aggregation and the way of choosing the most appropriate type of copula model for non-life reserve risk. Adequate capital is calculated by applying value at risk at 99.5%, which is mandatory in the EU market. The study considers which selected tests have to be implemented in order to choose the most appropriate copula model for reserve risk.

The main aim of the study is to establish the state and development of the leading insurance industry in our country - Non-Life Insurance, as well as to outline its importance as an economic sector and to determine its market structures after Bulgaria's accession to the European Union in 2007. The article traces the dynamics of gross written premiums realised by insurers in Non-Life Insurance during the period 2007-2021. The place of Non-Life Insurance in the Bulgarian economy has been established through the indicators "insurance penetration" and "insurance density". Its product structure for the same period has also been studied. As a result of the research, it is concluded that Non-Life Insurance in Bulgaria occupies a small share of the country's economy; Bulgarian citizens do not spend enough money on insurance, and the product structure of the market is dominated by Motor Insurance. [ABSTRACT FROM AUTHOR]

We present an optimization problem to determine the minimum capital requirement for a non-life insurance company. The optimization problem imposes a non-positive Conditional Value-at-Risk (CVaR) of the insurer’s net loss and a portfolio performance constraint. When expressing the optimization problem in a semiparametric form, we demonstrate its convexity for any integrable random variable representing the insurer’s liability. Furthermore, we prove that the function defining the CVaR constraint in the semiparametric formulation is continuously differentiable when the insurer’s liability has a continuous distribution. We use the Kelley-Cheney-Goldstein algorithm to solve the optimization problem in the semiparametric form and show its convergence. An empirical analysis is carried out by assuming three different liability distributions: a lognormal distribution, a gamma distribution, and a mixture of Erlang distributions with a common scale parameter. The numerical experiments show that the choice of the liability distribution plays a crucial role since marked differences emerge when comparing the mixture distribution with the other two distributions. In particular, the mixture distribution describes better the right tail of the empirical distribution of liabilities with respect to the other two distributions and implies higher capital requirements and different assets in the optimal portfolios. [ABSTRACT FROM AUTHOR]

This paper gives an overview of the new methods and the last trends considering actuarial mathematics. Based on a few papers that we consider relevant to our topic, we picked the information that is interesting to review. We will discuss different mathematical models and their use in the actuarial calculations for life insurance and non-life insurance. [ABSTRACT FROM AUTHOR]

Classical normal asymptotics could bring serious pitfalls in statistical inference, because some parameters appearing in the limit distributions are unknown and, moreover, complicated to estimated (from a theoretical as well as computational point of view). Due to this, plenty of stochastic approaches for constructing confidence intervals and testing hypotheses cannot be directly applied. Bootstrap seems to be a plausible alternative. A methodological framework for bootstrapping not independent and not identically distributed data is presented together with theoretical justification of the proposed procedures. Among others, bootstrap laws of large numbers and central limit theorems are provided. The developed methods are utilized in insurance and psychometry. [ABSTRACT FROM AUTHOR]

Several distributions and families of distributions are proposed to model skewed data, e.g., with skew-normal and related distributions. Lambert W random variables offer an alternative approach in which, instead of constructing a new distribution, a certain transformation is proposed. Such an approach allows the construction of a Lambert W skewed version from any distribution. Here, we choose the Lambert W normal distribution as a natural starting point and include the Lambert W exponential distribution due to the simplicity and shape of the exponential distribution, which, after skewing, may produce a reasonably heavy tail for loss models. In the theoretical part, we focus on the mathematical properties of obtained distributions, including the range of skewness. In the practical part, the suitability of the corresponding Lambert W transformed distributions is evaluated on real insurance data. Finally, the results are compared with those obtained using common loss distributions.

The need for insurance dates back from the distant past. A lot of time has passed from the primitive insurance forms to its institutionalization and legal implementation. The modern insurance market, regardless of its global character is not equally developed. The unevenness of development is one of its main characteristics, often connected with the achieved level of economic development. Numerous factors influences on the investment activities of insurance companies as the carriers of the insurance market. The subject of this paper will be the examination of the connection of payments of insured sums (damages) on the redistribution of the investment portfolio on the capital market. For this purpose will be formulated two hypotheses, separately for the non-life and life insurance market. Examination of the relationship between the considered variables will be conducted by using SPSS statistical software, ie the Chi-squared test. Regardless of proving the hypotheses will not be possible to draw unique conclusions for both markets.

Customer relationship management and marketing analytics have become critical for non-life insurers operating in highly competitive markets. As it is easier to develop an existing customer than to acquire a new one, cross-selling and retention are key activities. In this research, we focus on both car and household-liability insurance products and consider the time a customer owning only a single product takes before buying the other product at the same insurer. Based on longitudinal consumer data from a Swiss insurance company covering the period from 2011 to 2015, we aim to study the factors driving the duration to cross-selling. Given the different dynamics observed in both products, we separately study the car and household-liability insurance customer cohorts. Considering the framework of survival analysis, we provide descriptive statistics and Kaplan–Meier estimates along major customer characteristics, contract history and distribution channel usage. For the econometric analysis of the duration, we compare the results from Cox and accelerated failure time models. We are able to characterize the times related to the buying behavior for both products through several covariates. Our results indicate that the policyholder age, the place of residence, the contract premium, the number of contracts held, and the initial access channel used for contracting influence the duration to cross-selling. In particular, our results underline the importance of the tied agent channel and the differences along the geographic region and the urbanicity of the place of residence. By quantifying the effects of the above factors, we extend the understanding of customer behavior and provide a basis for developing models to time marketing actions in insurance companies. [ABSTRACT FROM AUTHOR]

Estimating outstanding claims reserves in the non-life insurance business is often impaired by outlier-contaminated datasets. Widely used methods to eliminate outliers in non-life development triangles are either limiting the number of outliers by robust statistical methods or by change of development factors. However, the whole estimation process is likewise adversely affected so that: (i) the total sum of all triangle payments is not correct or (ii) the difference between the original triangle and its backward estimation via the bootstrap method is ineligible. In this paper, the properties of the heat equation are examined to obtain an outlier smoothing technique for development triangles. The heat equation in two dimensions is being applied on an outlier contaminated dataset where no individual data are available. As a result, we introduce a new methodology to (i) treat outliers in non-life development triangles, (ii) keep the total sum of all triangle payments, and (iii) provide acceptable differences between the original and the backward estimated triangle. Consequently, the outlying values are eliminated and the resulting development triangle could be used as an input for any claims reserving method without a need for further robustification or change of development factors. Additionally, the research on the application of heat equation in one dimension presented in this paper enables one to employ the bootstrap method using Pearson's residuals in cases where the method was originally inapplicable due to development factors being lower than one. [ABSTRACT FROM AUTHOR]

The development of global economy has pushed up the importance of insurance industry in the growth of an economy. This paper intends to study the non-life insurance sector with an objective to identify the macro-economic factors that influence its growth. Time series data of 37 years is considered using Johansen & Engle Granger Cointegration and Ordinary Least Square method. It was found that Final Consumption Expenditure plays a negatively significant role in influencing non-life insurance sector in India. The practical implication of this study lies in controlling the responsible factors through appropriate policy measures to ensure a sustainable growth of the non-life Insurance sector. [ABSTRACT FROM AUTHOR]

In this paper we propose a general framework for modeling an insurance liability cash flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance. In particular, we assume a nontrivial dependence structure between the reference filtration and the insurance internal filtration. We apply these results for pricing and hedging non-life insurance liabilities in hybrid financial and insurance markets, while taking into account the role of inflation under the benchmarked risk-minimization approach. This framework offers at the same time a general and flexible structure, and an explicit and treatable pricing-hedging formula. [ABSTRACT FROM AUTHOR]

In almost all emerging and developed nations, the insurance industry is one of the most important participants of the financial services sector. As a result, the goal of this study is to investigate the firm characteristics and drivers of financial performance using 121 publicly traded non-life insurance companies from 16 Southern African Development Community (SADC) countries during the period from 2008 to 2019. The consolidated least squares and two-step generalized method of moments estimators were used to analyze a panel data set of 1,452 observations. The findings show that a lagged return on assets, equity capital, operational efficiency, leverage and investment capability are statistically significant determinants of financial performance in non-life insurance companies of SADC countries, even though equity capital, operational efficiency, and leverage are inversely significant. The insurance industry, policymakers, the state, and shareholders should consider these important variables when making decisions, and enhance their performance according to the findings. It is also suggested that the industry’s capital structures should be reformed to preserve a favorable balance of equity and debt amongst the businesses. Additionally, measures such as automated systems that may decrease operating costs should be used to improve financial performance.

Insurance in Turkey, has become an industry of strategic importance with a dynamic and growing capital structure. Companies in this sector create both savings and an important source of funds through premiums paid in return for contracts. Therefore, it is seen that the investments and market share of foreign capital companies are increasing day by day. In this study Turkey, 2 models covering the period 1990-2018 in Turkey is formed and Fourier ADL cointegration test was used to investigate the long-term relationship between variables. In the first model, which was established according to the results obtained, a cointegration between variables was determined and long-term parameter coefficients were determined. [ABSTRACT FROM AUTHOR]

The main goal of insurance company management is to increase shareholders’ value and implement a strategy that promotes sustainable growth of the company. Well-known possible measures intended to achieve that goal are as follows: share price, economic value, market capitalisation, gross premiums earned and solvency ratio. These measures include efficient capital management as capital expenses could be a major cost position depending on risk appetite and the extent of capital needed to support it. This research focuses on non-life insurers for reserve risk modelling. In the current study, a more accurate risk quantification model has been developed than the standard model provided by the EU regulator under the Solvency II framework. The proposed model provides capital cost gains as well. A case study based on non-life real data set with underwriting in the Baltic countries is discussed with inclusion of pandemic trends that had an impact on economies and customer behaviours. The study considers different non-life reserve distributions for each insurance business line, risk aggregation and the way of choosing the most appropriate type of copula model for non-life reserve risk. Adequate capital is calculated by applying value at risk at 99.5%, which is mandatory in the EU market. The study considers which selected tests have to be implemented in order to choose the most appropriate copula model for reserve risk. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the profitability and expense ratio of the insurance business conducted by commercial insurance companies operating in the Czech insurance market from 2004 to 2019. The evaluation shall apply computations such as the return on assets (ROA), the ratio of underwriting profit to assets (UPA), and the expense ratio indicator (ER). Calculations shall be performed for non-life insurance. The value of the UPA indicator was stable during the whole period. By contrast, the ROA indicator fluctuated from 2004 to 2011. The ROA indicator stabilized between 2012 and 2017 and then between 2017 and 2019 ROA grew significantly. The value of both indicators (ROA, UPA) decreased from 2015 to 2017 and increased from 2017-2018. The operating expense ratio of insurance operations (ER) showed a growth tendency. When the average values of the selected indicators were compared to the Czech market, it was determined that the insurance companies Allianz, Česká pojišťovna ZDRAVÍ and PNP Paribas Cardif had achieved the best result and were the most efficient. [ABSTRACT FROM AUTHOR]

This study seeks to interrogate the impact of claim management on the profitability of non-life insurance companies in Ghana covering the years 2013-2020. The study employs the fixed effects and random effects techniques with data covering the period 2013-2020. Hausman test is used as an appropriate approach to conduct this study. Our work measured profitability utilizing interpretive financial tools such as return on assets (ROA) and returns on equity (ROE). The results of our research indicates that claim ratio and retention ratio have a profound adverse impact on return on asset. In terms of return on equity (ROE), claim ratio and expense ratio negatively and significantly affect profitability. This study recommends that non-life insurance companies should institute an effective claim processing system with highly qualified, experienced, trained and technical staff to ensure efficiency in claims management. [ABSTRACT FROM AUTHOR]

Copyright of Bilecik Seyh Edebali University Journal of Social Sciences is the property of Bilecik Seyh Edebali University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pacha: Revista de Estudios Contemporáneos del Sur Global is the property of PACHA REVISTA DE ESTUDIOS CONTEMPORANEOS DEL SUR GLOBAL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we compare different spatial models for modelling the risk premium for water damage insurance on the level of the policyholder. We evaluate four models that take the spatial variability into account: (1) the Intrinsic Conditional Auto-Regressive (ICAR) model; (2) the Besag, York, Mollier (BYM) model; (3) the independent random effects model; and (4) a spatial spline model. The models are compared on a huge data set from the Norwegian insurance company Gjensidige containing seven million observations of policyholders during the period 2011–2018. While Bayesian methods are most frequently used for inference in Gaussian Markov Random Field models, we take a frequentist approach and estimate the model parameters using Laplace approximated restricted maximum likelihood. Using the R package mgcv, we compare the different models for claim frequency, claim size and combined in a risk premium model in a comprehensive cross-validation study. Practical measures such as the loss ratio lift, double lift and Gini index are used to compare performance. Finally, we also compare mgcv with INLA and show that for reasonable big data sets we get identical estimates at a much lower computational cost. [ABSTRACT FROM AUTHOR]

This paper studies the factors behind the capital structure of insurance companies. We used financial reports of non-life and composite insurance companies in Serbia between 2006 and 2019. In particular, we apply a panel-data approach to examine the relationship between leverage, defined as the ratio of technical reserves to capital and various firm-level characteristics. The coefficients estimated using the individual fixed-effects model indicate a significant and negative influence of profitability, growth and liquidity measures on leverage and a significant and positive influence of company size. The results indicate that the tradeoff theory and the pecking order theory are relevant in explaining the non-life insurer capital structure in Serbia.