Can Weather Forecasts Predict Norwegian Home Insurance Claims?

Abstract

Insurance companies are urgently seeking robust solutions to minimize the risks associated with property damage in response to more frequent extreme weather and increased regulation. This need is particularly significant in Norway, where extreme precipitation is common. Therefore, shortterm prediction of future claims would be of great value for proactive decision-making and damage prevention. However, previous research focuses mainly on linking past insurance claims to past precipitation or on longer-term changes in insurance claims over the next few decades. Here, we develop a model that predicts the number of short-term Norwegian water damage property claims using precipitation forecasts from the European Center for Medium Range Weather Forecasting (ECMWF) from 2014-2021. The insurance dataset is unique in that it includes private claims data from a large insurance provider, Tryg Forsikring, where each claim is categorized as either a Natural Perils claim or not.The results show that precipitation forecasts can be used to predict the number of insurance claims within a few days. Although the signal is weak, with an AUC score of 0.65, it is better than chance alone. Specifically, the predictive skill is not very sensitive to details of the predictors derived from the weather forecast, and simpler logistic regression models perform as well as more complex machine learning models such as XGBoost and Neural Networks. The skill of the claims model can be largely explained by the skill of the precipitation forecast. Overall, our results suggest that using weather forecasts to predict insurance damage claims is possible, and demonstrate the potential to be used for operational decision making, such as in an early-warning system.

Insurance companies are urgently seeking robust solutions to minimize the risks associated with property damage in response to more frequent extreme weather and increased regulation. This need is particularly significant in Norway, where extreme precipitation is common. Therefore, shortterm prediction of future claims would be of great value for proactive decision-making and damage prevention. However, previous research focuses mainly on linking past insurance claims to past precipitation or on longer-term changes in insurance claims over the next few decades. Here, we develop a model that predicts the number of short-term Norwegian water damage property claims using precipitation forecasts from the European Center for Medium Range Weather Forecasting (ECMWF) from 2014-2021. The insurance dataset is unique in that it includes private claims data from a large insurance provider, Tryg Forsikring, where each claim is categorized as either a Natural Perils claim or not.The results show that precipitation forecasts can be used to predict the number of insurance claims within a few days. Although the signal is weak, with an AUC score of 0.65, it is better than chance alone. Specifically, the predictive skill is not very sensitive to details of the predictors derived from the weather forecast, and simpler logistic regression models perform as well as more complex machine learning models such as XGBoost and Neural Networks. The skill of the claims model can be largely explained by the skill of the precipitation forecast. Overall, our results suggest that using weather forecasts to predict insurance damage claims is possible, and demonstrate the potential to be used for operational decision making, such as in an early-warning system.