Insurance risk assessment in the face of climate change: Integrating data science and statistics

Abstract

Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events. This article is categorized under: Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Data: Types and Structure > Massive Data

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

“…Abnormality in precipitation is rapidly increasing worldwide (Hao et al, 2019;Li et al, 2019;Lyubchich et al, 2019). Besides, climate change is alternating the hydrometeorological patterns in terms of frequency, irregularity, and severity of precipitation which has led to the rise of the life-threatening hydrological disasters (Hennequin et al, 2018;Serago and Vogel, 2018;Shkolnik et al, 2018).…”

Topological clustering of multilayer networks

“…The proposed methodology for peril maps and vulnerability zoning for the weather-and climate-induced risks developed in this paper offers multiple potential benefits to mitigate the impact of climate change for the insurance industry, policy makers, and society in general. First, insurance companies can adjust the range of insurance products offered in less-or more-vulnerable zones (e.g., extra insurance against moisture in basements or lower premium rates for houses without a basement), offer various "risk-smart" incentives for homeowners (e.g., reduced insurance premiums, if homeowners use a new generation of wind-resistant or energyefficient roofing materials), and improve calculations for net amounts at risk (83)(84)(85). Second, many other businesses start investing in risk-prone areas on multiple fronts, e.g., modified buildings or homes, green energy, and hybrid vehicles.…”

Using Machine Learning Techniques in Multi -Hazards Assessment of Golestan National Park, Iran

“…Precipitation anomalies are increasing rapidly worldwide [10][11][12] , causing signi cant damage to natural resources, agricultural communities, and human structures every year 13,14 . The average annual economic loss due to oods and landslides in Iran is estimated to be $1705,000 15 .…”