Advanced analytics pricing for the calculation of post-covid19 scenarios in automobile insurance

Abstract

The reduction in mobility during the COVID19 pandemic has led to a reduction in the accident claims rate in motor insurance. Insurance companies will need to calculate pricing scenarios for possible changes in transportation habits, using data from 2020. We show how some Machine Learning methods (decision trees and gradient boosting) can be used to evaluate pricing scenarios and we propose a strategy to correct the circumstances of exposure to risk that have occurred during the pandemic. We conclude that it is possible to use the existing information during the lockdown period provided that changes in the portfolios can be identified and corrected, and assessing whether or not the impact is homogeneous by risk groups