Classification of climate-related insurance claims using gradient boosting

George Tzougas; Viet Dang; Asif John; Stathis Kroustalis; Debashish Dey; Konstantin Kutzkov

doi:10.26360/2022_6

Authors

George Tzougas Heriot-Watt University, Edinburgh (United Kingdom) https://orcid.org/0000-0003-4072-1454
Viet Dang Ar Genesis (United Kingdom)
Asif John Ar Genesis (United Kingdom)
Stathis Kroustalis University of Economics and Business, Athens (Greece)
Debashish Dey WTW (United Kingdom)
Konstantin Kutzkov Teva Pharmaceuticals (United Kingdom)

DOI:

https://doi.org/10.26360/2022_6

Keywords:

cimate-related insurance claims, ensemble learning, decision trees, boosting

Abstract

The aim of this paper is to implement, one of the most representative supervised learning approaches, the decision tree based ensemble method called gradient boosting for classifying the number of claims caused by storms in Greece using data from a major insurance company operating in Greece. Finally, a machine learning algorithm is used to for categorising the number of claims which have been occurred by a “storm event” into 3 categories: “no claims”, “1 claim”, “2 or more claims”.

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