This paper contributes to the sparse literature on reverse stress testing, the necessity of which is emphasized by banks' supervisory authorities. While, for regular stress tests, scenarios are chosen based on historical experience or expert knowledge and their influence on the bank's survivability is tested, reverse stress tests aim to find exactly those scenarios that cause the bank to cross the frontier between survival and default. Afterward, the most likely of these scenarios has to be found. We argue that bottom-up approaches, as specific integrated risk management techniques, are ideal candidates for carrying out quantitative reverse stress tests because they model interactions between different risk types already on the level of the individual financial instruments and risk factors. This is exemplified with an extended CreditMetrics model that exhibits correlated interest rates and rating-specific credit-spread risk.