  Identification and scoring functions are statistical tools to assess the
calibration and the relative performance of risk measure estimates, e.g., in
backtesting. A risk measures is called identifiable (elicitable) it it admits a
strict identification function (strictly consistent scoring function). We
consider measures of systemic risk introduced in Feinstein, Rudloff and Weber
(2017). Since these are set-valued, we work within the theoretical framework of
Fissler, Hlavinov\'a and Rudloff (2019) for forecast evaluation of set-valued
functionals. We construct oriented selective identification functions, which
induce a mixture representation of (strictly) consistent scoring functions.
Their applicability is demonstrated with a comprehensive simulation study.
