Can Weather Forecasts Predict Norwegian Home Insurance Claims?
Master thesis
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https://hdl.handle.net/11250/3158972Utgivelsesdato
2024Metadata
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- Master Thesis [4490]
Sammendrag
Insurance companies are urgently seeking robust solutions to minimize the risks associated withproperty damage in response to more frequent extreme weather and increased regulation. This needis 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 damageprevention. However, previous research focuses mainly on linking past insurance claims to pastprecipitation or on longer-term changes in insurance claims over the next few decades. Here, wedevelop a model that predicts the number of short-term Norwegian water damage property claimsusing 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 datafrom a large insurance provider, Tryg Forsikring, where each claim is categorized as either a NaturalPerils claim or not.The results show that precipitation forecasts can be used to predict the numberof 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 ofthe predictors derived from the weather forecast, and simpler logistic regression models performas well as more complex machine learning models such as XGBoost and Neural Networks. Theskill 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, anddemonstrate the potential to be used for operational decision making, such as in an early-warningsystem. Insurance companies are urgently seeking robust solutions to minimize the risks associated withproperty damage in response to more frequent extreme weather and increased regulation. This needis 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 damageprevention. However, previous research focuses mainly on linking past insurance claims to pastprecipitation or on longer-term changes in insurance claims over the next few decades. Here, wedevelop a model that predicts the number of short-term Norwegian water damage property claimsusing 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 datafrom a large insurance provider, Tryg Forsikring, where each claim is categorized as either a NaturalPerils claim or not.The results show that precipitation forecasts can be used to predict the numberof 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 ofthe predictors derived from the weather forecast, and simpler logistic regression models performas well as more complex machine learning models such as XGBoost and Neural Networks. Theskill 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, anddemonstrate the potential to be used for operational decision making, such as in an early-warningsystem.