The paper will provide an assessment of the current techniques used to assess operational risk in the financial services industry globally.  Addressing the limitations of current approaches, a new innovative framework for the assessment and analysis of operational risk is outlined, based upon the use of complex systems sciences.  The techniques of cognitive mapping and Bayesian networks will be used to develop structural / causal models for the assessment of operational risk.  These frameworks enable the complex inter-relationships between operational risk and capital outcomes to be directly linked to the states of business drivers and risk factors, such that the full range of non-linear behaviour can be captured.

Case study examples will be used to demonstrate the framework.  In particular, a case study developed for operational losses arising from rogue derivative traders, which effectively define the extreme tail of an operational loss distribution, will be discussed in some detail.  Case studies will also be used to demonstrate how the various dimensions of risk management can be addressed in one consistent framework, spaning risk assessment, capital assessment, stress testing, scenario testing, reverse stress testing, risk appetite and risk limit setting.  A case study on the use of phylogenetic techniques will also be discussed to understand the relatedness of the characteristics defining various large derivative losses and rogue trader events, which provides insight into the assessment of emerging operational risks.

Compared to the paper, the presentation will focus proportionately more on innovations in operational risk, as opposed to the current state of play across global financial services industries.