  Surrender poses one of the major risks to life insurance and a sound modeling
of its true probability has direct implication on the risk capital demanded by
the Solvency II directive. We add to the existing literature by performing
extensive experiments that present highly practical results for various
modeling approaches, including XGBoost, random forest, GLM and neural networks.
Further, we detect shortcomings of prevalent model assessments, which are in
essence based on a confusion matrix. Our results indicate that accurate label
predictions and a sound modeling of the true probability can be opposing
objectives. We illustrate this with the example of resampling. While resampling
is capable of improving label prediction in rare event settings, such as
surrender, and thus is commonly applied, we show theoretically and numerically
that models trained on resampled data predict significantly biased event
probabilities. Following a probabilistic perspective on surrender, we further
propose time-dependent confidence bands on predicted mean surrender rates as a
complementary assessment and demonstrate its benefit. This evaluation takes a
very practical, going concern perspective, which respects that the composition
of a portfolio, as well as the nature of underlying risk drivers might change
over time.
