We develop an algorithm to identify potential operational outages in TARGET2. As stated in the Principles of Financial Market Infrastructures, plausible sources of operational risk should be identified in financial market infrastructures (FMIs). For TARGET2 and other FMIs, little is known about the operational outages of participants. This contrasts with system outages, which are generally well understood and documented. Our paper attempts to close this gap by implementing an algorithmic approach to identify participants’ operational outages based on transaction data. Using a series of criteria, we identify time periods during which activity was so low that it indicates a bank’s ability to send payments was partly or fully hindered. This strategy is best suited for larger banks that exhibit stable payment patterns. The identification of false positives (wrongly identified outages) is addressed by focusing on consecutive intervals, which are unlikely to occur due to chance, while the identification of false negatives (undetected outages) is mitigated by employing a relatively broad approach. The data set we construct provides evidence on the potential absence of participants in the absence of other evidence. These results are useful for operators, overseers and researchers, and could also be of interest for supervisors.