Assessment of operational loss data and its implications for capital modeling

Assessment of operational loss data and its implications for capital modeling

Ruben, can you please tell the Risk Insights readers about yourself and your professional experiences?

My background is in mechanical engineering. After getting my PhD, I spent 10 years on the faculty of an American university, teaching and conducting research in fluid mechanics and thermodynamics. I then changed my career path, got a Masters degree in economics, went into banking about 17 years ago and have since worked in various areas, including asset management, corporate finance, audit, operational risk analytics and model risk.

At the New Generation Operational Risk Summit in March you will be assessing operational loss data and its implications for capital modelling. Why do you believe this is a key talking point?

Operational risk is a relatively new area and the analytics we see today that support it go back only as far as 10 to 15 years. My nearly 10 years of experience in operational risk analytics has shown me that this area is overly complicated because there is no established theory to back it up. We are being bombarded with data from all sides and, in the absence of an undisputed theory, it is a huge challenge to put all these data together in a way that they can be used appropriately in a model. The capital modelling that comes with it is another big challenge all by itself. My presentation is about a different way to look at and assess operational loss data. It is important to look at data in different ways, especially now that operational risk capital modelling is undergoing some major changes.

See more on what’s being discussed at New Generation Operational Risk: Europe

Why is it important to introduce a method for dealing with operational loss data?

The method I’m introducing to operational risk modelling is not a theory, but a mathematical procedure that has been around for almost 140 years and which can be used to look at operational loss data in a different light. The approach is very popular in the area of experimental fluid mechanics, where, among other things, it is used to extrapolate the results of tests conducted on models to actual prototypes. I’m applying this method to operational risk to help distinguish between the many different types and behaviours that we see in the loss data.

How do you see the role of the operational risk professional changing over the next 6-12 months?

Notwithstanding cyber risk, which has lately gained a considerable amount of attention among operational risk professionals, and concentrating on operational risk modelling for our purposes here, the aim has more recently revolved around making capital modelling more transparent, simple and consistent across different banks. These characteristics are all contained within the newly proposed SMA operational risk model.

Although it is still in its early days and going through serious criticisms, the SMA will eventually transform into the new generation of capital model and be accepted by the majority in the field. When this is all accomplished, risk managers can become more actively involved in the analytics, which will, in turn, give them a deeper understanding of the processes that underlie operational losses and their relationships with capital. A better understanding of the loss processes and their impact on capital can then help risk managers focus their efforts more efficiently to control losses and reduce capital requirements.