  Machine learning methods are getting more and more important in the
development of internal models using scenario generation. As internal models
under Solvency 2 have to be validated, an important question is in which
aspects the validation of these data-driven models differs from a classical
theory-based model. On the specific example of market risk, we discuss the
necessity of two additional validation tasks: one to check the dependencies
between the risk factors used and one to detect the unwanted memorizing effect.
The first one is necessary because in this new method, the dependencies are not
derived from a financial-mathematical theory. The latter one arises when the
machine learning model only repeats empirical data instead of generating new
scenarios. These measures are then applied for an machine learning based
economic scenario generator. It is shown that those measures lead to reasonable
results in this context and are able to be used for validation as well as for
model optimization.
