Concordance probability in a big data setting: application in non-life insurance
This work addresses the need for efficient discriminatory measures in non-life insurance pricing, but it is incremental as it adapts existing methods to a specific domain.
The authors adapted the concordance probability (C-index) for frequency and severity models in non-life insurance pricing, presenting two estimation procedures to handle large sample sizes, such as those in frequency data.
The concordance probability or C-index is a popular measure to capture the discriminatory ability of a regression model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. Due to the typical large sample size of the frequency data in particular, two different adaptations of the estimation procedure of the concordance probability are presented. Note that the latter procedures can be applied to all different versions of the concordance probability.