Deze rubriek wordt verzorgd door mr. B.K.M. Lauwerier en prof. mr. J. Borgesius

Buitenlandse tijdschriften

Data science, actuariaat en verzekeringsstatistiek

A Dirichlet process mixture regression model for the analysis of competing risk events
F. Ungolo, E.R. van den Heuvel
Insurance: Mathematics and Economics 116 (2024) 95-113
doi.org/10.1016/j. insmatheco.2024.02.004

The analysis of competing risk events is commonplace in the statistical and the actuarial fields. It concerns the probabilistic mechanism of the time to the events an individual is simultaneously exposed. The most common example is the case of an individual who is exposed to several causes of death, such as cancer, cardiovascular disease, and so on. We develop a regression model for the analysis of competing risk events. The joint distribution of the time to these events is flexibly characterized by a random effect which follows a discrete probability distribution drawn from a Dirichlet Process, explaining their variability. This entails an additional layer of flexibility of this joint model, whose inference is robust with respect to the misspecification of the distribution of the random effects. The model is analysed in a fully Bayesian setting, yielding a flexible Dirichlet Process Mixture model for the joint distribution of the time to events. An efficient MCMC sampler is developed for inference. The modelling approach is applied to the empirical analysis of the surrending risk in a US life insurance portfolio previously analysed by Milhaud and Dutang (2018). The approach yields an improved predictive performance of the surrending rates.

Quantile mortality modelling of multiple populations via neural networks
S. Corsaro, Z. Marino, S. Scognamiglio
Insurance: Mathematics and Economics 116 (2024) 114-133
doi.org/10.1016/j.insmatheco.2024.02.007

Quantiles of the mortality rates are relevant in life insurance to control longevity risk properly. Recently, Santolino (2020) adapts the framework of the popular Lee-Carter model to compute the conditional quantiles of the mortality rates. The parameters of the quantile Lee-Carter model are fitted on the mortality data of the population of interest, ignoring the information related to the others. In this paper, we show that more robust parameter estimates can be obtained exploiting the mortality experiences of multiple populations. A neural network is employed to calibrate individual quantile Lee-Carter models jointly using all the available mortality data. In this setting, some common network parameters are used to learn the age and period effects of multiple quantile LC models. Numerical experiments performed on all the countries of the Human Mortality Database validate our approach. The predictions obtained considering the median level appear more accurate than those obtained with the mean models; moreover, those at the tail quantile levels capture the future mortality evolution of the populations well.

Managing Weather Risk with a Neural Network-Based Index Insurance
Z. Chen, Y. Lu, J. Zhang, W. Zhu
Management Science 70 (2024) 7 (vi, 4167-4952, iii-v)
doi.org/10.1287/mnsc.2023.4902

Weather risk affects the economy, agricultural production in particular. Index insurance is a promising tool to hedge against weather risk, but current piecewise-linear index insurance contracts face large basis risk and low demand. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance contract. Neural networks capture a highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal insurance premium and demand. This approach reduces basis risk, lowers insurance premiums, and improves farmers’ utility.

Testing for auto-calibration with Lorenz and Concentration curves
M. Denuit, J. Huyghe, J. Trufus, Th. Verdebout
Insurance: Mathematics and Economics 117 (2024) 130-139
doi.org/10.1016/j.insmatheco.2024.04.003

The estimation of the mean function has attracted a lot of interest in the literature. This task is called regression or supervised learning, and consists in estimating the conditional expectation of a response (or dependent variable) given a set of features (or explanatory variables). Dominance relations and diagnostic tools based on Lorenz and Concentration curves in order to compare competing estimators of the regression function have recently been proposed. This approach turns out to be equivalent to forecast dominance when the estimators under consideration are auto-calibrated. A new characterization of auto-calibration is established, based on the graphs of Lorenz and Concentration curves. This result is exploited to propose an effective testing procedure for auto-calibration. A simulation study is conducted to evaluate its performances and its relevance for practice is demonstrated on an insurance data set. The developments in this paper apply to any regression for the mean setting where balance is desirable at both global and local scales, making auto-calibration relevant. This is for instance the case with the insurance application proposed in this paper, where it is important that sums of estimates (considered as pure premiums) closely match sums of corresponding observations (actual insurance losses) to ensure equilibrium of insurance operations.

Law-invariant return and star-shaped risk measures
R.J.A. Laeven, E.R. Gianin, M. Zullino
Insurance: Mathematics and Economics 117 (2024) 140-153
doi.org/10.1016/j.insmatheco.2024.04.006

Over the past decades, a large literature has developed the theory of monetary risk measures — monotone and cash-additive functionals — and analyzed their applications in a variety of fields including economics, finance, insurance, operations research and statistics. Whereas monetary risk measures provide absolute assessments of risk, return risk measures provide relative assessments of risk, evocative of the distinct roles played by absolute and relative risk aversion measurements. Law-invariant risk measures — also referred to as law-determined or distribution-invariant risk measures — and stochastic order consistent risk measures play an important role in the theory and applications of risk measures. This is due to their simplicity, tractability and statistical appeal, being statistical functionals. Many well-known risk measures are specific examples of law-invariant risk measures (e.g., Value-at-Risk, Expected Shortfall, the entropic risk measure and the p-norm). This paper presents novel characterization results for classes of law-invariant star-shaped functionals. We begin by establishing characterizations for positively homogeneous and star-shaped functionals that exhibit second- or convex-order stochastic dominance consistency. Building on these characterizations, we proceed to derive Kusuoka-type representations for these functionals, shedding light on their mathematical structure and intimate connections to Value-at-Risk and Expected Shortfall. Furthermore, we offer representations of general law-invariant star-shaped functionals as robustifications of Value-at-Risk. Notably, our results are versatile, accommodating settings that may, or may not, involve monotonicity and/or cash-additivity. All of these characterizations are developed within a general locally convex topological space of random variables, ensuring the broad applicability of our results in various financial, insurance and probabilistic contexts.

Total life insurance:
Logics of anticipatory control and actuarial governance in insurance technology
J. Sadowski
Social Studies of Science 54 (2024) 231-256
Open access
journals.sagepub.com

Calling attention to the growing intersection between the insurance and technology sectors—or ‘insurtech’—this article is intended as a bat signal for the interdisciplinary fields that have spent recent decades studying the explosion of digitization, datafication, smartification, automation, and so on. Many of the dynamics that attract people to researching technology are exemplified, often in exaggerated ways, by emerging applications in insurance, an industry that has broad material effects. Based on in-depth mixed-methods research into insurance technology, I have identified a set of interlocking logics that underly this regime of actuarial governance in society: ubiquitous intermediation, continuous interaction, total integration, hyper-personalization, actuarial discrimination, and dynamic reaction. Together these logics describe how enduring ambitions and existing capabilities are motivating the future of how insurers engage with customers, data, time, and value. This article surveys each logic, laying out a techno-political framework for how to orient critical analysis of developments in insurtech and where to direct future research on this growing industry. Ultimately, my goal is to advance our understanding how insurance—a powerful institution that is fundamental to the operations of modern society—continues to change, and what dynamics and imperatives, whose desires and interests are steering that change. The stuff of insurance is far too important to be left to the insurance industry.

Antidiscrimination Insurance Pricing:
Regulations, Fairness Criteria, and Models
X. Xin, F. Huang
North American Actuarial Journal 28 (2024) 285-319
Open access
tandfonline.com

On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies: direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various fairness criteria have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade and have mostly focused on classification decisions. In this article, we introduce some fairness criteria that are potentially applicable to insurance pricing as a regression problem to the actuarial field, match them with different levels of potential and existing antidiscrimination regulations, and implement them into a series of existing and newly proposed antidiscrimination insurance pricing models, using both generalized linear models (GLMs) and Extreme Gradient Boosting (XGBoost). Our empirical analysis compares the outcome of different models via the fairness–accuracy trade-off and shows their impact on adverse selection and solidarity.

What is fair? Proxy discrimination vs. demographic disparities in insurance pricing
M. Lindholm, R. Richman, A. Tsanakas, M.V. Wütrich
Scandinavian Actuarial Journal 2024 (2024) 935-970
Open access
tandfonline.com

Discrimination and fairness are major concerns in algorithmic models. This is particularly true in insurance, where protected policyholder attributes are not allowed to be used for insurance pricing. Simply disregarding protected policyholder attributes is not an appropriate solution as this still allows for the possibility of inferring protected attributes from non-protected covariates, leading to the phenomenon of proxy discrimination. Although proxy discrimination is qualitatively different from the group fairness concepts discussed in the machine learning and actuarial literature, group fairness criteria have been proposed to control the impact of protected attributes on the calculation of insurance prices. The purpose of this paper is to discuss the relationship between direct and proxy discrimination in insurance and the most popular group fairness axioms. We provide a technical definition of proxy discrimination and derive incompatibility results, showing that avoiding proxy discrimination does not imply satisfying group fairness and vice versa. This shows that the two concepts are materially different. Furthermore, we discuss input data pre-processing and model post-processing methods that achieve group fairness in the sense of demographic parity. As these methods induce transformations that explicitly depend on policyholders' protected attributes, it becomes ambiguous whether direct and proxy discrimination is, in fact, avoided.

A multi-task network approach for calculating discrimination-free insurance prices
M. Lindholm, R. Richman, A. Tsanakas, M.V. Wütrich
European Actuarial Journal 14 (2024) 329-369
Open access
link.springer.com

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models and are thus having an undesirable (and possibly illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such an approach requires full knowledge of policyholders’ protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics and produces prices that are free from proxy discrimination. We demonstrate the proposed method on both synthetic data and a real-world motor claims dataset, in which proxy discrimination can be observed. In both examples we find that the predictive accuracy of the multi-task network is comparable to a conventional feed-forward neural network, when the protected information is available for at least half of the insurance policies. However, the multi-task network has superior performance in the case when the protected information is known for less than half of the insurance policyholders.

Verzekeringseconomie

Can price collars increase insurance loss coverage?
I. Chatterjee, M. Hao, P. Tapadar, R.G. Thomas
Insurance: Mathematics and Economics 116 (2024) 74-94
doi.org/10.1016.2024.02.003

Restrictions on risk classification are common in retail insurance markets, usually in the form of bans on the use of particular variables (e.g. gender, race, genetic tests). Restrictions of this type can align insurance practice with social norms which deprecate discrimination on particular variables. But the use of deprecated variables is not the only public policy issue with risk classification. Another concern is the equity of a wide dispersion in prices for different risks, irrespective of the particular underlying variables which it might reflect; and in particular, the problem of unaffordable insurance for higher risks. For this type of concern, piecemeal restriction of specific variables seems an indirect and hard-to-calibrate form of regulation. Also, if insurers can use other variables which are correlated with the banned ones, the effect of the regulation can be partly undone, unless more prescriptive regulation requires estimation procedures which neutralise this effect. A potentially more direct way of addressing equity concerns is to set an explicit limit on the dispersion of prices for different risks, via what we call a price collar. A price collar (sometimes called ‘partial community rating’) is a maximum ratio, say κ, for the highest and lowest prices an insurer may charge for different risks. A price collar restricts only an insurer's relative prices, not the absolute level. It can be used either in addition to, or instead of, limits on specific variables. One example is the Affordable Care Act in the US, which permits differentiation by a factor of up to 1.5× for tobacco use and 3× for age (and no other factors except coverage tier, geography, and number of dependants). In this paper, we abstract from the reference to specific variables and consider a price collar which limits the overall dispersion of an insurer's prices. The collar regime, which can also be described as partial risk-classification, represents a compromise approach, in between full risk-classification (where prices are fully differentiated for individual risks, which we assume are fully observable via insurers' underwriting procedures), and pooling (where all risk classification is banned and each insurer sets a single price, the same for all risks). A price collar can potentially ameliorate equity concerns, but policymakers also need to consider its efficiency consequences, in particular the adverse selection which may be induced. We take a nuanced view of this: we argue that whilst excessive adverse selection is a concern, a modest degree of adverse selection can actually increase efficiency, if it increases what we call ‘loss coverage’. Loss coverage, defined as expected population losses compensated by insurance, is a public policy criterion for comparing different risk-classification regimes. Using a model with two risk-groups (high and low) and iso-elastic demand, we compare loss coverage under three alternative regulatory regimes: (i) full risk-classification (ii) pooling (iii) a price collar, whereby each insurer is permitted to set any premiums, subject to a maximum ratio of its highest and lowest prices for different risks. Outcomes depend on the comparative demand elasticities of low and high risks. If low-risk elasticity is sufficiently low compared with high-risk elasticity, pooling is optimal; and if it is sufficiently high, full risk-classification is optimal. For an intermediate region where the elasticities are not too far apart, a price collar is optimal, but only if both elasticities are greater than one. We give extensions of these results for more than two risk-groups. We also outline how they can be applied to other demand functions using the construct of arc elasticity.

Stackelberg equilibria with multiple policyholders
M. Ghossoub, M.B. Zhu
Insurance: Mathematics and Economics 116 (2024) 189-201
doi.org/10.1016/j.insmatheco.2024.02.008

Three fundamental concerns of any risk-sharing market are the willingness of agents to engage in an exchange, Pareto efficiency of allocations of the aggregate risk, and a characterization of equilibria in the market. Determining Pareto-optimal allocations requires a model of agents' preferences, and identifying equilibria necessitates, in addition, a model of the market's pricing mechanisms (premium principles).

The study of Pareto-efficient allocations, or contracts, in insurance markets is extensive and vast. There has been renewed interest in varied models of (re)insurance market structures and the behavior that such structures may induce in different agents. For example, the effect of a first-mover's advantage in an insurance market has garnered recent attention in the literature. These models assume that the provider of insurance can determine their pricing mechanisms before the policyholder can select a contract, which allows the insurer to profit by anticipating policyholder behavior. The solution concept in these markets is the notion of a Stackelberg equilibrium (or a Bowley optimum).We examine Pareto-efficient contracts and Stackelberg Equilibria (SE) in a sequential-move insurance market in which a central monopolistic insurer on the supply side contracts with multiple policyholders on the demand side. We obtain a representation of Pareto-efficient contracts when the monopolistic insurer's preferences are represented by a coherent risk measure. We then obtain a representation of SE in this market, and we show that the contracts induced by an SE are Pareto-efficient. However, we note that SE do not induce a welfare gain to the policyholders in this case, echoing the conclusions of recent work in the literature. The social welfare implications of this finding are examined through an application to the flood insurance market of the United States of America, in which we find that the central insurer has a strong incentive to raise premia to the detriment of the policyholders. Accordingly, we argue that monopolistic insurance markets are problematic, and must be appropriately addressed by external regulation.

Is the insurance industry sustainable?
M. Eling
Journal of Risk Finance 25 (2024) 684-703
doi.org/10.1108/JRF-12-2023-0314

This study aims to develop a comprehensive framework for discussing sustainability within the insurance industry, extending the traditional Environmental, Social, and Governance (ESG) dimensions to include economic and technological considerations. This inclusion is vital, recognizing that financial stability and the adoption of innovative technologies are fundamental to meeting other sustainability targets. We base our findings on an extensive literature review, case studies, and interactive workshops with key stakeholders in the insurance industry. Our analytical framework employs Porter's (1985) insurance-specific value chain, complemented by Berliner's (1982) insurability criteria, to distinguish between insurable and non-insurable risks. Our results show that the insurance industry is sustainable because it actively incorporates and contributes to sustainability goals across environmental, social, economic, and technological dimensions. This is illustrated through the identification of 50 distinct contributions across the insurance value chain, showcasing the sector’s unique position to significantly influence the sustainability discourse. Addressing the pressing challenges of sustainability and insurability necessitates a strategic, collective response from the global insurance and risk management community. This paper proposes several policy recommendations, including enhancing risk assessment methodologies, diversifying insurance product offerings, encouraging cross-sectoral collaboration, and prioritizing investments in resilience and preventive measures.

Information Security Awareness in the Insurance Sector:
Cognitive and Internal Factors and Combined Recommendations
M. Djotaroeno, E. Beulen
Information 15 (2024) 505
Open access
mdpi.com

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of ‘How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?’ has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training).

Insights into the complementarity of natural disaster insurance purchases and risk reduction behavior
S. Rufat, P.J. Robinson, J.W. Botzen
Risk Analysis 44 (2024) 1 (141-154)
Open access
onlinelibrary.wiley.com

While flooding is the costliest natural disaster risk, public-sector investments provide incomplete protection. Moreover, individuals are in general reluctant to voluntarily invest in measures which limit damage costs from natural disasters. The moral hazard hypothesis argues that insured individuals take fewer other preparedness measures based on their assumption that their losses will be covered anyway. Conversely, the advantageous selection hypothesis argues that individuals view insurance and other risk reduction measures as complements. This study offers a comprehensive assessment of factors related to the separate uptake of natural disaster insurance and the flood-proofing of homes as well as why people may take both of these measures together. We use data from a survey conducted in Paris, France, in 2018, after several flood events, for a representative sample of 2976 residents facing different levels of flood risk. We perform both main effects regressions and interaction analyses to reveal that home adaptation to flooding is positively associated with comprehensive insurance coverage, which includes financial protection against natural disasters. Furthermore, actual and perceived risks, as well as awareness of official information on flood risk, are found to explain some of the relationship between home adaptation and comprehensive insurance purchase. We suggest several recommendations to policymakers based on these insights which aim to address insurance coverage gaps and the failure to take disaster risk reduction measures. In particular, groups in socially vulnerable situations may benefit from subsidized insurance, low interest loans, and decision aids to implement costly adaptation measures.

Transit fares integrating alternative modes as a delay insurance
Y. Zhou, W. Sun, J-D. Schmöcker
Transportation Research Part C: Emerging Technologies 170 (2024) 104745
doi.org/10.1016/j.trc.2024.104745

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

Culture and integration of Eurozone life insurance markets
M. Rubio-Misas
The European Journal of Finance 30 (2024) 6 (597-617)
doi.org/10.1080/1351847X.2023.2227228

This paper provides the first evidence on the role of national culture in the integration of Eurozone life insurance markets. It analyzes seven markets over a sixteen-year sample period that includes the financial crisis. We focus on three cultural values, which are individualism, trust, and hierarchy. The results indicate that collectivism culture increases cost and revenue performance and integration of Eurozone life insurance markets. Trust contributes to this integration, particularly in financial crisis, and egalitarian culture facilitates it in non-crisis time. We find that these relations prevail for unaffiliated single companies, but they weaken or do not even hold for groups of insurers. Our findings are robust with tests designed to alleviate endogeneity concerns.

Verzekeringsvermogensbeheer

Scenario selection with LASSO regression for the valuation of variable annuity portfolios
H. Nguyen, M. Sherris, A.M. Villegas, J. Ziveyi
Insurance: Mathematics and Economics 116 (2024) 27-43
doi.org/10.1016/j.insmatheco.2024.01.006

Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associated valuation of such portfolios for hedging purposes is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In addition to the selection of representative contracts, this paper proposes using LASSO regression to select a set of representative scenarios, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel.

A mean field game approach to optimal investment and risk control for competitive insurers
L. Bo, S. Wang, C. Zhou
Insurance: Mathematics and Economics 116 (2024) 202-217
doi.org/10.1016/j.insmatheco.2024.03.002

We consider an insurance market consisting of multiple competitive insurers with a mean field interaction via their terminal wealth under the exponential utility with relative performance. It is assumed that each insurer regulates her risk by controlling the number of policies. We respectively establish the constant Nash equilibrium (independent of time) on the investment and risk control strategy for the finite n-insurer game and the constant mean field equilibrium for the corresponding mean field game (MFG) problem (when the number of insurers tends to infinity). Furthermore, we examine the convergence relationship between the constant Nash equilibrium of finite n-insurer game and the mean field equilibrium of the corresponding MFG problem. Our numerical analysis reveals that, for a highly competitive insurance market consisting of many insurers, every insurer will invest more in risky assets and increase the total number of outstanding liabilities to maximize her exponential utility with relative performance.

Optimal control under uncertainty:
Application to the issue of CAT bonds
N. Baradel
Insurance: Mathematics and Economics 117 (2024) 16-44
doi.org/10.1016/j.insmatheco.2024.03.004

We consider an insurer or a reinsurer who holds a portfolio in non-life insurance exposed to one or several natural disasters. He can issue one or several CAT bonds in order to reduce the risk taken, see e.g. Cummins (2008) or Cummins (2012) for a general introduction to CAT bonds. The first CAT bonds were issued at the end of the 1990s and the market is globally increasing, with a total risk capital outstanding greater that USD 30 billion at the end of 2017, see ARTEMIS (2018) and Carpenter (2016). CAT bonds give a strong alternative to the classical reinsurance market. We propose a general framework for studying optimal issue of CAT bonds in the presence of uncertainty on the parameters. In particular, the intensity of arrival of natural disasters is inhomogeneous and may depend on unknown parameters. Given a prior on the distribution of the unknown parameters, we explain how it should evolve according to the classical Bayes rule. Taking these progressive prior-adjustments into account, we characterize the optimal policy through a quasi-variational parabolic equation, which can be solved numerically. We provide examples of application in the context of hurricanes in Florida.

Robust asset-liability management games for n players under multivariate stochastic covariance models
N. Wang, Y. Zhang
Insurance: Mathematics and Economics 117 (2024) 67-98
doi.org/10.1016/j.insmatheco.2024.04.001

Asset-liability management (ALM) is one of the classic and important topics in the fields of financial risk management and actuarial science. Over the past few decades, financial institutions, including banks, pension funds and insurance companies, are increasingly focused on addressing challenges related to ALM, which empowers them to enhance liquidity risk management. Typically, ALM involves the continuous adjustment of investment amounts in accordance with financial markets and regulatory requirements so that assets and liabilities are coordinated. This paper investigates a non-zero-sum stochastic differential game among n competitive CARA asset-liability managers, who are concerned about the potential model ambiguity and aim to seek the robust investment strategies. The ambiguity-averse managers are subject to uncontrollable and idiosyncratic random liabilities driven by generalized drifted Brownian motions and have access to an incomplete financial market consisting of a risk-free asset, a market index and a stock under a multivariate stochastic covariance model. The market dynamics permit not only stochastic correlation between the risky assets but also path-dependent and time-varying risk premium and volatility, depending on two affine-diffusion factor processes. The objective of each manager is to maximize the expected exponential utility of his terminal surplus relative to the average among his competitors under the worst-case scenario of the alternative measures. We manage to solve this robust non-Markovian stochastic differential game by using a backward stochastic differential equation approach. Explicit expressions for the robust Nash equilibrium investment policies, the density generator processes under the well-defined worst-case probability measures and the corresponding value functions are derived. Conditions for the admissibility of the robust equilibrium strategies are provided. Finally, we perform some numerical examples to illustrate the influence of model parameters on the equilibrium investment strategies and draw some economic interpretations from these results.

Potential of pension funds and insurance companies for investment in resources:
Policies for sustainable transition
B. Liu, H. Sun, S. Xiao
Resources Policy 89 (2024) 104618
doi.org/10.1016/j.resourpol.2023.104618

This study extensively explores the complex relationship between environmental investments made by pension funds and insurance companies and their influence on expenses associated with fossil fuels. Analyzing data from 2015 to 2020 across ten OECD-economies, the research employs the Fully Modified OLS technique to extract meaningful insights. The results reveal that a 1% increase in environmental investmentspropels practices focused on energy conservation and increased efficiency by 0.49%, underscoring a dedicated commitment to sustainability. Additionally, a 1% increase in IT expenditures is associated with a 0.10% improvement in energy efficiency. Conversely, heightened green tax payments exhibit a negative correlation with energy usage, emphasizing the impact of regulatory incentives. To bolster sustainable investments, the paper advocates for robust ESG reporting, support for SMEs, the adoption of green corporate management practices, and improved access to green financial markets.

Verzekeringsproductontwikkeling, -distributie en -marketing

Pollution risks and life insurance decisions:
Microgeographic evidence from the United Kingdom
M.O.Adetutu, K.A. Odusanya, S. Rasciute, E. Stathopoulou
Risk Analysis 44 (2024) 1907-1930
Open access
onlinelibrary.wiley.com

Recent research documents that exposure to air pollution can trigger various behavioral reactions. This article presents novel empirical evidence on the causal effect of pollution risk on life insurance decisions. We create a unique dataset by linking microgeographic air quality information to the confidential UK Wealth and Assets Survey. We identify an inverse N-shape relationship between pollution risk and life insurance adoption by exploiting the orthogonal variations in meteorological conditions. Over a given range above a threshold of exposure, rising pollution is associated with rising demand for life insurance, whereas at lower than the threshold levels of pollution, higher exposure risk reduces demand for insurance. Our findings indicate—for the first time—a nonlinear relationship between local pollution risk and life insurance demand.

Analyse der Einflussfaktoren auf die Risikowahrnehmung und die Kaufentscheidung im Kontext einer Berufsunfähigkeitsversicherung
T. Scholl, K-J. Jeske
Zeitschrift für die gesamte Versicherungswissenschaft 113 (2024) 181-226
doi.org/10.3790/zverswiss.2024.1443301

The understanding of disability insurance is examined by analyzing the factors that influence risk perception. At the same time, it is shown which factors influence the individual’s decision to purchase disability insurance. This raises the question of whether a potential customer’s increased risk perception has a positive impact on the likelihood of purchasing disability insurance. The results of the analysis are used to derive recommendations for the insurance product’s advisory service and for the insurance company’s marketing. The results show that the factors of fear, perceived control, ability to reduce risk and change influence the risk perception of occupational disability among Generation Z. There is no correlation between risk perception and the decision to purchase occupational disability insurance. The purchase decision of Generation Z is mainly based on factors such as price, efficient plan features and awareness of the insurance company.

Cyberspace
A Systematic Review
R. McGregor, C. Reaiche, S. Boyle, G. Corral de Zubielqui
Journal of Computer Information Systems 64 (2024) 157-171
Open access
tandfonline.com

As individuals become increasingly digitally dependent, cyber threats and cyber insurance to mitigate them gain relevance. This literature review conceptualizes a framework for siting Personal Cyber Insurance (PCI) within the context of cyberspace. The lack of empirical research within this domain demonstrates a need to identify and define the scope of PCI in order to allow cyber insurers to understand customer needs, and to conduct effective management and distribution of PCI products and services. We conducted a systematic literature review of 229 articles that were clustered into three meta-level themes: cyberspace, personal cyber risk, and PCI. The literature review indicates a significant paucity of research related to PCI particularly as it is influenced by antecedent risk externalities, the nature of cyberspace itself, the PCI market and operations, and post-cyber event support. The paper concludes with a proposal for a future research agenda.

Reforming the Swiss Pension System:
Understanding Public Opinion to Enable Targeted Communication Efforts
C. Pugnetti, M. Amrein, M. Moor, J. Portmann
Zeitschrift für die gesamte Versicherungswissenschaft 113 (2024) 227-255
doi.org/10.3790/zverswiss.2024.1443302

While generally well-run, the Swiss pension system is in need of reforms to be effective in the longer term. The first pillar, like that of many other countries, is facing demo- graphic challenges undermining its effectiveness and support across generations, while the second pillar is plagued by structural inefficiencies and low returns on investment. Given the direct-democracy approach in Switzerland reforms need to be supported in detail by the broader population and not just by technocrats; this has made reforms slow. Understanding the profile of citizens not supporting reforms and focusing communication is therefore critical to accelerating the pace of technically necessary reforms. This research uses the data from an Raiffeisen Pension Barometer survey to identify such profiles. We find that the profiles are different among the three pillars, and that only a few factors play a significant role. High income and high-skill employment are the most significant indicators of a lower perceived need for reform, as is residency in Western Switzerland. While impacting confidence in the system, age at the granularity in the survey does not generally impact the perceived need for reform.

Corporate Reputation, Salesperson Trustworthiness and Customer Loyalty in the Life Insurance Industry
Chr. Schäfer, I. Held, M. Kosch, S. Bergmann, M.E. Böhm, K-N. Kross
Zeitschrift für die gesamte Versicherungswissenschaft 113 (2024) 283-315
doi.org/10. 3790/zverswiss.2024.1443304

This study analyses the effects of corporate reputation and trustworthiness in the salesperson on the customer loyalty of 215 German customers who obtained endowment insurance. We use a cross-sectional study design. Corporate reputation is captured by the rational attitude of competence and the emotional likeability of the insurer by customers. The results show that perceived likeability and trustworthiness in the salesperson both play an equally dominant role in customer satisfaction and loyalty followed by perceived core service quality. Surprisingly the competence level has no direct effect on customer loyalty. Our results show that a trustworthy salesperson can compensate for weaknesses in a company’s perception of competence. Finally, the results suggest that life insurers’ strategic management should focus on strong emotional bonds and the formation of a trustful sales force. Assistance services can offer customers an early positive product experience, usually within the year-long saving phase of a life insurance product. Our findings underscore the need for insurers to be perceived as innovative, fair, socially engaged, and being represented by a highly trusted salesperson to achieve high levels of customer loyalty.

Measuring Travel Insurance Literacy:
Effect on Trust in Providers and Intention to Purchase
G. Luna-Cortés, M. Brady
Journal of Travel Research 63 (2024) FirstOnline 12-01-2024
Open access
journals.sagepub.com

Recent research suggests that some deficiencies in the insurance industry might be associated with tourists’ lack of knowledge. However, the literature does not present a tool that captures objective knowledge of travel insurance literacy. This research presents a comprehensive, multi-step scale development process resulting in a 15-item scale, which measures objective knowledge about travel insurance terms, regulations, and processes. The results show that travel insurance literacy is associated with trust in sellers. Although no relationship was found between travel insurance literacy and perceived travel risks, these two constructs influence purchase intention. The results also show that there is no relationship between subjective knowledge (self-assessment literacy) and objective knowledge (ratings on the scale). Hence, the research presents a new measurement tool that differs from self-assessment scales prevailing in the tourism literature, opening new opportunities for literacy constructs in the field. Finally, the findings present key managerial implications for the insurance industry.

Insurance needs of digital nomads and predictions for future
M. Cetin
Worldwide Hospitality and Tourism Themes 16 (2024) 3 (379-382)
doi.org/10.1108/WHATT-03-2024-0065

This study aims to determine the impact of digital nomadism on the relationship between tourism and traditional insurance models, the opportunities and challenges that digital nomadism brings to the insurance industry and future predictions. A review approach was used, utilizing industry reports, statistics, market research and previous studies. Digital nomadism will directly bring about the development of personalized insurance products and services. The journey that began with digital health insurance will propel the industry to entirely new horizons. The study offers a new perspective in this field by addressing the relationship between digital nomadism and the insurance industry.

Endogenous Information and Simplifying Insurance Choice
Z.Y. Brown, J. Jeon
Econometrica 92 (2024) 881-911
doi.org/10.3982/ECTA18555

In markets with complicated products, individuals may choose how much time and effort to spend understanding and comparing alternatives. Focusing on insurance choice, we find evidence consistent with individuals acquiring more information when there are larger consequences from making an uninformed choice. Building on the rational inattention literature, we develop and estimate a parsimonious demand model in which individuals choose how much to research difficult-to-observe characteristics. We use our estimates to evaluate policies that simplify choice. Reducing the number of plans can raise welfare through improved choice as well as savings in information costs. Capping out-of-pocket costs generates larger welfare gains than standard models. The empirical model can be applied to other settings to examine the regulation of complex products.

Verzekeringsrecht

De impact van het nieuwe verbintenissenrecht op de verzekeringssector
J. Amankwah, M. Schouteden
Rechtskundig Weekblad 88 (2024) 4 (123)
rw.be

Sinds 1 januari 2023 geldt in België een nieuw verbintenissenrecht, zoals vervat in Boek 5 van het Burgerlijk Wetboek. De auteurs onderzoeken de betekenis daarvan voor het verzekeringscontractenrecht van de wet van 4 april 2024.

Addressing the notion of trust around ChatGPT in the high-stakes use case of insurance
J. Ressel, M. Völler, F. Murphy, M. Mullins
Technology in Society 78 (2024) 102644
Open access
sciencedirect.com

The public discourse concerning the level of (dis)trust in ChatGPT and other applications based on large language models (LLMs) is loaded with generic, dread risk terms, while the heterogeneity of relevant theoretical concepts and empirical measurements of trust further impedes in-depth analysis. Thus, a more nuanced understanding of the factors driving the trust judgment call is essential to avoid unwarranted trust. In this commentary paper, we propose that addressing the notion of trust in consumer-facing LLM-based systems across the insurance industry can confer enhanced specificity to this debate. The concept and role of trust are germane to this particular setting due to the highly intangible nature of the product coupled with elevated levels of risk, complexity, and information asymmetry. Moreover, widespread use of LLMs in this sector is to be expected, given the vast array of text documents, particularly general policy conditions or claims protocols. Insurance as a practice is highly relevant to the welfare of citizens and has numerous spillover effects on wider public policy areas. We therefore argue that a domain-specific approach to good AI governance is essential to avoid negative externalities around financial inclusion. Indeed, as a constitutive element of trust, vulnerability is particularly challenging within this high-stakes set of transactions, with the adoption of LLMs adding to the socio-ethical risks. In light of this, our commentary provides a valuable baseline to support regulators and policymakers in unravelling the profound socioeconomic consequences that may arise from adopting consumer-facing LLMs in insurance.

Occupational Pension Provision in Germany
R.P. Dombek
Zeitschrift für die gesamte Versicherungswissenschaft 113 (2024) 257-281
doi.org/10.3790/zverswiss.2024.1443303

This paper analyses the forms and prevalence of external implementation channels of occupational pension provision after the introduction of the 2018 Occupational Pension Strengthening Act (OSPA) in Germany and their determinants. First, the most important pension reforms of the German old-age pension system since 2001 are briefly explained, before the term occupational pension in Germany is defined and the labour, tax and social security law foundations of occupational pension provision in Germany are explained. Furthermore, the Occupational Strengthening Pensions Act is explained in the further course of this paper. After the external implementation channels of the German occupational pension system are presented, an analysis of the prevalence of external implementation channels, such as direct insurance and pension funds follows. In the final part, we will examine whether the Act to Strengthen Occupational Pensions is the right political tool for strengthening occupational pension provision or whether it would even make sense to introduce compulsory occupational pension provision.

Verzekeringsgeneeskunde

Direct to Consumer Biomarker Testing for Alzheimer Disease – Are We Ready for the Insurance Consequences?
J.J. Arias, M. Manchester, J. Lah
JAMA Neurology 81 (2024) 2 (107-108)
doi.org/10.1001/jamaneurol.2023.4811

Quest Diagnostics introduced the first-to-market direct-to-consumer (DTC) biomarker test for Alzheimer disease (AD) on July 31, 2023. The test provides consumers a blood draw at the company’s laboratory and a report on their amyloid status without a clinician order. The advancement of DTC tests in blood-based biomarkers raises controversy, including concerns around accuracy and misinterpretation.The promise of DTC testing for AD biomarkers may be lauded by advocates pushing for earlier diagnoses and individuals’ right to know. Early diagnosis of AD through DTC or clinical evaluations could provide benefits, including increased monitoring and preventive care. Additionally, DTC tests could reduce barriers that impede a timely diagnosis (eg, access to dementia specialists). However, DTC tests are not without hazard, particularly given gaps in discriminatory protections for individuals at risk of developing AD with known biomarker status.This perspective piece evaluates one area of concern—potential insurance discrimination.