Modeling operational risk in financial institutions using hybrid dynamic Bayesian networks

In this paper we describe the use of hybrid dynamic Bayesian networks (HDBNs) to model the operational risk faced by financial institutions in terms of economic capital. We describe a methodology for modeling financial losses resulting from intentional or accidental events and characterize these by their ability to evade controls and which ultimately lead to increasingly severe financial consequences. The approach presented here focuses on modeling the causes and effects of loss events using a dynamic Bayesian network model based on interactions between failure modes and controls. To calculate the value-at-risk for total losses we apply a new state-of-the-art hybrid Bayesian network algorithm, called dynamic discretization. The algorithm approximates the continuous loss distribution functions required for each loss event at each point in time and is used to aggregate across loss types. In order to illustrate the natural match between the model and the underlying process, including the causal complexity underlying known and possible severe operational risk losses, we apply the generalized model to a financial trading example: rogue trading. We conclude that the statistical properties of the model have the potential to explain recent large-scale loss events and offer improved means of loss prediction.