Modelling Operational Risk in Financial Institutions using HDBNs

This paper describes the use of Hybrid Dynamic Bayesian Networks (HDBNs) to model the operational risk faced by financial institutions in terms of economic capital. It describes a methodology for modelling financial losses resulting from intentional or accidental events and characterises these by their ability to evade controls and ultimately lead to increasingly severe financial consequences. The approach presented focuses on modelling 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 (VaR) 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 generalised 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.