The likely imposition by regulators of minimum standards for capital to cover 'other risks' has been a driving force behind the recent interest in operational risk management. Much discussion has been centered on the form of capital charges for other risks. At the same time major banks are developing models to improve internal management of operational proceses, new insurance products for operational risks are being designed and there is growing interest in alternative risk transfer, through OR-linked products. The purpose of this paper is to introduce Bayesian belief networks (BBNs) and influence diagrams for measuring and managing certain operational risks, such as transaction processing risks and human risks. BBNs lend themselves to the causal modelling of operational processes: if the causal factors can be identified, the Bayesian network will model the influences between these factors and their contribution to the performance of the process. The ability to refine the architecture and parameters of a BBN through back testing is explained, and the paper also demonstrates the use of scenario analysis in a BBN to identify states that lead to maximum operational losses.