Operational risk management: Using loss-data more effectively

Abstract

Using operational risk loss-data is paying off. It can now be demonstrated that the operational risk in processing transactions is containable. Continuous infrastructure improvements (such as via lessons learned, concentration analysis or Six-Sigma projects) and diligent preparation of changes (especially outsourcings and new products) are the instruments of choice to do so.The common causes of operational risks continue to be mishaps in the client interface or trader fraud. Operational risk remains rather low correlated with credit and market risk, and thus hardly increased during the financial crisis. This is despite the fact that operational risk within credit and market risk contributed to some of the biggest losses the industry has experienced over recent years.Based on the loss-data gathered over the last 10–15 years, the operational risk discipline has matured sufficiently to inform strategic and investment decision-taking, in the same way as credit or market risk has done for many years. The operational risk inherent in investments (such as acquisitions or new products) can now be priced, as well as divestments (such as outsourcings or reduced controls), and can provide a business case of prevented operational risk losses to justify IT developments or increased resources for new controls. This requires sophisticated capital modelling, plenty of correlation and root-cause research, and dedicated development of predictive and sensitive key risk indicators.Using loss-data creates transparency and validates many pre-perceptions around operational risk. At the same time, it drives the continuous improvement of the loss-data capturing process with regards to completeness and correctness. Clear governance, systematic data reconciliation and quality assurance are key elements to establishing the foundations for operational risk managers to earn their seat at the decision-making table in a modern financial organisation.