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

Deep learning provides many benefits, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications-from credit card fraud detection to recommender systems and customer segmentation. Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped. In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty. We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate. Our findings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches. We infer that it also has great potential to reduce underwriting inefficiencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure. Future work aims to further evaluate its performance, from municipal area-yield, to finer-scale crop-specific producer-scale yield.

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