This paper presents recent new ideas on using cause-effect modeling, in the form of Hybrid Dynamic Bayesian Networks (HDBNs), to estimate extreme financial losses resulting from operational failures. The presentation will focus on a particularly important loss process - rogue trading - with the aim of demonstrating the advantage of explicit modeling of banking processes and risk culture over purely statistical models derived from actuarial loss data alone. Value at Risk is calculated by applying a new state-of-the-art HDBN algorithm that approximates continuous loss distributions and aggregates across loss types using a process called dynamic discretization. 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.