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