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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]

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]

Previous studies found inconsistent results for insurance-growth nexus. The aim of this study is to examine the relationships between life and non-life insurance with economic growth. The study applies pooled mean group method to examine long-term and short-term insurance-growth nexus over the period of 1980 to 2015. The findings of the study show that there exists a positive and significant relationship between life insurance and economic growth in the long-term and short-term for all selected countries, except when insurance penetration is used as a proxy. However, a positive and significant relationship was observed for non-life insurance and economic growth for all four proxies in the long-term and short-term. The relationship between insurance and economic growth is found to be different across countries and across proxies because of diverse factors such as diversity and variety of insurance products, religious and cultural traditions, level of education, and State involvement, not covered in this research. [ABSTRACT FROM AUTHOR]

Non-life insurance prices may fluctuate due to economic and/or institutional factors; occasionally, the changes are cyclical. While the majority of previous studies relating to insurance price dynamics adopt data from developed economies, this paper uses data from China to provide new evidence. This study tests the long-term and short-term effects of real gross domestic product (GDP), interest rate and rate of stock market return on the prices of different lines of non-life insurance, i.e., property-liability insurance and personal accident insurance. The results indicate that the price dynamics of property-liability insurance are generally similar to those of developed countries, except for the effect of GDP, while price determination of personal accident insurance seems to be affected by a wider range of economic and institutional variables and has its own features. The price dynamics of non-life insurance in China have been identified as being connected to the country-specific economic and institutional environments. [ABSTRACT FROM AUTHOR]

We connect classical chain ladder to granular reserving. This is done by defining explicitly how the classical run-off triangles are generated from individualiidobservations in continuous time. One important result is that the development factors have a one to one correspondence to a histogram estimator of a hazard running in reversed development time. A second result is that chain ladder has a systematic bias if the row effect has not the same distribution when conditioned on any of the aggregated periods. This means that the chain ladder assumptions on one level of aggregation, say yearly, are different from the chain ladder assumptions when aggregated in quarters and the optimal level of aggregation is a classical bias variance trade-off depending on the data-set. We introduce smooth development factors arising from non-parametric hazard kernel smoother improving the estimation significantly. [ABSTRACT FROM AUTHOR]

The determinants of non-life insurance expenditure in a panel data set covering 36 developed countries and 31 developing countries for the period 2000–2011 are analysed. Results of our instrumental variable analysis indicate that economic freedom, income, bank development, urbanization, culture and law systems are the key drivers of the non-life insurance expenditure across countries. However, their impacts differ significantly between the groups of developed and developing countries, suggesting that the heterogeneity among countries in terms of the level of development plays an important role. The global financial crisis is also found to influence the direction of those effects, especially in developed countries. The article yields useful policy and economic implications for governments and multinational non-life insurance companies with regard to the development of the non-life insurance sector, an important engine for economic growth and prosperity. [ABSTRACT FROM AUTHOR]

Corporate customers often hold multiple contracts and this might give dependence between the lapsing times of the single policies. We present a shared gamma frailty model in order to study the time-to-lapse of single car policies for customers holding multiple car contracts with the same insurance company, accounting for measured and time-dependent covariates. Customers with the highest frailty value tend to leave the company earlier than the others and finding these is a central aspect within a company’s customer relationship management strategy. We estimate conditional survival curves which illustrate the decreased survival probability of a customer after a lapse in a single car insurance policy. The individual survival curves are overestimated if the underlying association for cars with the same customer is ignored. Fitting misspecified Cox’s proportional hazards model also results in an underestimation of the standard error of the parameter estimates. [ABSTRACT FROM AUTHOR]

Within a company's customer relationship management strategy, finding the customers most likely to leave is a central aspect. We present a dynamic modelling approach for predicting individual customers’ risk of leaving an insurance company. A logistic longitudinal regression model that incorporates time-dynamic explanatory variables and interactions is fitted to the data. As an intermediate step in the modelling procedure, we apply generalised additive models to identify non-linear relationships between the logit and the explanatory variables. Both out-of-sample and out-of-time prediction indicate that the model performs well in terms of identifying customers likely to leave the company each month. Our approach is general and may be applied to other industries as well. [ABSTRACT FROM PUBLISHER]

This paper aims at studying the asset allocation problem of a non-life insurance company when inflation risk and interest rate risk are taken into account. To this purpose, we apply the risk-minimization theory developed by Follmer & Sondermann (1986) and extended by Møller (2001). We derive the general form of the risk-minimizing strategies when the cumulative payments of the insurer are described, as suggested by Arjas (1989), by a process adapted to the natural filtration of a marked point process and when the inflation and the term structure of interest rates are simultaneously described by the HJM model of Jarrow & Yildirim (2003). We then apply our general results in two collective models and two individual models of non-life insurance payments. [ABSTRACT FROM AUTHOR]

Using the distribution-free chain ladder method, we estimate the total ultimate claim amounts at time I, and after updating the information, at time I+1. The observable claims development result at time I+1 for accounting year (I, I+1] is then defined to be the difference between these two successive best estimate predictions for the ultimate claim. We analyze the uncertainty of this observable claims development result. [ABSTRACT FROM AUTHOR]

Buchwalder et al. (2006) have illustrated that there are different approaches for the derivation of an estimate for the parameter estimation error in the distribution-free chain ladder reserving method. In this paper, we demonstrate that these approaches provide estimates that are close to each other for typical parameters. This is carried out by proving upper and lower bounds. [ABSTRACT FROM AUTHOR]

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between insurance and economic growth in 20 countries for the period 2006–2015. Insurance activity is measured through three distinctive proxies such as net written premiums, penetration and density. The Hausman statistics confirmed that fixed effect model is appropriate for this data-set. This study found a positive and a significant relationship between life insurance, measured through net written premiums and density, and economic growth for developed countries while the same is true for developing countries when insurance is measured through penetration proxy. The results also reveal that non-life insurance has statistically significant, for all three proxies, relationship with economic growth for developing countries whereas, in case of developed countries, the results are only significant when insurance density is used as a proxy for insurance. Moreover, the role of non-life insurance is more significant for developing countries as compared to developed countries. [ABSTRACT FROM AUTHOR]