The Geneva Risk and Insurance Review

Makowsky, Michael D.; Bacon, Kelsey Roberts

doi: 10.1057/s10713-023-00093-2pmid: N/A

As workers’ compensation insurance costs increase, firms have incentive to misclassify employees under ostensibly safer job classifications to lower premiums. Using Occupational Safety and Health Agency accident investigation records, we measure employee risk of fatality while performing tasks reported to investigators as outside of employee duties (“irregular tasks”). We observe standard compensating wage differentials paid for fatal accident risk during regular tasks uncorrelated with insurance costs. Both the share of fatal accidents occurring during irregular tasks and the wage differentials paid for irregular task risk, however, increase when mandated benefits increase workers’ compensation insurance costs.

Golnaraghi, Maryam; Surminski, Swenja; Greig, Chris

doi: 10.1057/s41288-025-00374-zpmid: N/A

Anand, Vaibhav; Poole-Selters, Leah; Spognardi, Alexa Gozdiff; Bekele, Biniam Taddese; de Perez, Erin Coughlan

doi: 10.1057/s41288-025-00355-2pmid: 41081055

Improvements in forecasting technologies create opportunities for anticipatory actions before disasters occur. However, traditional ex post financing and limited operational capacity often prevent countries from acting early. This paper examines anticipatory index insurance, specifically African Risk Capacity’s pilot program in Malawi and Zambia for drought risk, which offers capacity building and forecast-based financing for early actions. However, its benefits are unclear given the trade-off between early actions based on imperfect forecasts and post-disaster relief after certain losses. Since imperfect forecasts increase basis risk, anticipatory index insurance can exacerbate this trade-off. Using a stylized economic model and numerical analysis, we identify conditions under which anticipatory insurance is beneficial. Results show that its primary value lies in building operational capacity for forecast-based actions. However, due to basis risk, the incremental value from insurance payouts may not always justify the premium costs. Our findings can help governments and aid agencies design optimal financing strategies for anticipatory actions.

Pala, Luiz O. O.; Nakamura, Luiz R.; Sabe, Elias M.; Ramires, Thiago G.

doi: 10.1057/s41288-025-00357-0pmid: N/A

Challenges arising from climate variations and other factors in the agricultural insurance market complicate the pricing of insurance products for insurers and affect consumers. Although different methodologies are available in the literature, there is growing demand for more accurate predictive models. In this context, the main aim of this paper is to use distributional regression models to investigate the relationships between climatic factors and insured crop features in terms of both the probability and severity of claims for soybean crops in Minas Gerais, Brazil. The results obtained indicate that the proposed and adopted methodology is crucial, as it allowed both for the identification of covariates that affect only the probability of a claim occurring and variables that affect only the severity of a claim, given that it occurs, and the detection of non-linear relationships that would not be possible based on traditional models.

Florig, Michael; Gossner, Olivier

doi: 10.1057/s10713-024-00107-7pmid: N/A

We examine a general equilibrium investment model in which agents incur management costs for holding assets. We characterize the influence of these costs on equilibrium prices as a weighted average of these costs for market participants. We then propose a correction method for this influence in valuation procedures used under regulatory frameworks, such as Solvency II. For insurers subject to Solvency II, the accounting correction amounts to approximately €130 billion, the equivalent of 1.8% of investments or 14% of own funds. These results not only contribute to the understanding of management costs in market equilibrium, but also highlight a distortion in current practices which discourages the holding of assets that are expensive to manage and typically inaccessible directly by policyholders.

Luo, Laijuan; Zhang, Lianzeng; Zhuang, Yuan

doi: 10.1057/s41288-025-00365-0pmid: N/A

This paper introduces a machine-learning-based flood risk prediction model that integrates data of different granularities, with a focus on predicting flood probabilities under climate change scenarios. Initially trained on historical data, the model utilises CMIP6 projections to estimate monthly flood probabilities for 312 cities in China from 2025 to 2100. Additionally, the SKATER method was used to incorporate spatial adjacency, enabling effective risk zoning across cities. Among the four models tested, LightGBM consistently outperformed GLM, random forest, and neural network in terms of accuracy and adaptability. The results suggest that as climate conditions worsen, moving from SSP1-2.6 to SSP5-8.5, flood probabilities are expected to increase in Ningxia, Shaanxi, Henan, and Hebei, as well as in parts of the southwestern border regions. This study contributes to regional disaster risk management by effectively addressing low-frequency, low-resolution data, and demonstrates strong potential for application across diverse countries and regions worldwide.

Goussebaïle, Arnaud; Louaas, Alexis

doi: 10.1057/s10713-024-00106-8pmid: N/A

Solvency regulation can prevent insurers from making decisions that are detrimental to policyholders. However, it can also discourage the purchase of insurance for catastrophic risks by causing prohibitive insurance loading due to high reinsurance coverage constraints. This paper examines this delicate trade-off. We show that a solvency regulation allowing some level of insurer default in catastrophic states can be a first-best policy. The default rate of this first-best policy varies depending on the risk line and market conditions. Our numerical simulations indicate that it is possible to closely approximate the first-best policy by implementing a straightforward solvency regulation, considering insurers’ Expected Shortfall and Value at Risk, the reinsurance loading, and policyholders’ risk aversion. Therefore, reforming current solvency regulations in this direction could improve policyholders’ welfare.

Gao, Hang; Yang, Shuohua; Liu, Xinli

doi: 10.1057/s41288-025-00360-5pmid: N/A

Weather parametric insurance relies on weather indices rather than actual loss assessments, improving claims efficiency, reducing moral hazard, and improving fairness. In the context of increasing climate change risks, despite growing interest and demand, the market share of weather parametric insurance remains limited due to inherent basis risk—the mismatch between actual loss and payout, manifesting as ‘actual loss without payout’ or ‘payout without acutal loss’. This paper isolates basis risk as a structural feature of parametric trigger, decoupling it from financial impacts on insurers or policyholders to focus on its fundamental properties. Through novel empirical research using Monte Carlo simulations of diversified contract portfolios, we demonstrate that: (1) portfolio basis risk and basis risk volatility will decrease as the number of contracts increases, (2) basis risk follows deterministic patterns based on the ratio of ‘exposure-weather station distance’ to ‘hazard footprint radius’, providing geometry-driven reference, and (3) hazard event severity does not significantly impact basis risk, suggesting that catastrophic disaster severity should not hinder parametric insurance development. While basis risk is inherent, these results demonstrate its manageability through portfolio strategy and geospatial optimisation, while offering particularly actionable value for insurers, who are uniquely positioned to implement large-scale diversification strategies that individual policyholders usually cannot practically achieve.

Jansen, Dennis W.; Liu, Liqun

doi: 10.1057/s10713-025-00111-5pmid: N/A

Risk is often understood as the likelihood of a disastrous event despite the predominant notion of risk measured by the dispersion in the distribution of an outcome variable. In a mean-disaster framework, this paper proposes a measure of disaster aversion and studies the three often-encountered problems of decision making under risk: self-protection, portfolio choice, and insurance demand. Clear-cut intuitive comparative statics results are obtained for each of these decisions with respect to the effect of the strength of disaster aversion.

Kelly, Mary; Kleffner, Anne; Medders, Lori

doi: 10.1057/s41288-025-00347-2pmid: N/A

Financing losses from catastrophic events remains a significant challenge for governments worldwide. Private insurance markets have limitations in covering these losses, so many governments have intervened in both primary and reinsurance markets to support private insurers through government-private partnerships. This paper analyzes global catastrophe insurance (CI) programs, focusing on their resilience and adaptability to changing conditions. Three detailed case studies demonstrate the effectiveness of these programs under stress conditions. In addition to the nature of the peril, factors influencing program resilience include whether coverage is mandatory, government participation in paying losses, risk-sharing catastrophe insurance pools, and solvency requirements for private insurers. The study emphasizes the crucial role of government involvement in sustaining CI programs, particularly in the face of increasing climate-induced natural disasters, highlighting the necessity of insurance professionals’ involvement in this field.