Operational risk data sets have two types of sample selection problems: truncation below a given threshold due to data that are not recorded and random censoring above that level caused by data that are not reported. This paper proposes a model for operational losses that improves the internal loss distribution modelling by combining internal and external operational risk data. It also considers the possibility that internal and external data have been collected with a different truncation threshold. Moreover, the model is able to cope with unreported losses by means of an estimated underreporting function.