In risk-based testing, compromises are often made to release a system in spite of knowing that it has outstanding defects. In an industrial setting, time and cost are often the "exit criteria" and - unfortunately - not the technical aspects like coverage or defect ratio. In such situations, the stakeholders accept that the remaining defects will be found after release, so sufficient resources are allocated to the "stabilization" phases following the release. It is hard for many organizations to see that such an approach is significantly costlier than trying to locate the defects earlier. We performed an empirical investigation of this for one of our industrial partners (a financial company). In this project, significant perfective maintenance was performed on the large information system. Based on changes made to the system, we carried out procedure level code coverage measurements with code level change impact analysis, and a similarity-based comparison of test cases in order to quantitatively check the completeness and redundancy of the tests performed. In addition, we logged and compared the number of defects found during testing and live operation. The data obtained were surprising for both the developers and the customer as well, leading to a major reorganization of their development, testing, and operation processes. After the reorganization, a significant improvement in these indicators for testing efficiency was observed.