Risk-based test strategies enable the tester to harmonize the number of specified test cases with imposed time and cost constraints. However, the risk assessment itself often requires a considerable effort of cost and time, since it is rarely automated. Especially for complex tasks such as testing the interoperability of different components it is expensive to manually assess the criticality of possible faults. We present a method that operationalizes the risk assessment for interoperability testing. This method uses behavior models of the system under test and reinforcement learning techniques to break down the criticality of given failure situations to the relevance of single system actions for being tested. Based on this risk assessment, a desired number of test cases is generated which covers as much relevance as possible. Risk models and test cases have been generated for a mobile payment system within an industrial case study.