By Paul Burnett, Global Head of Traded Risk Analytics, HSBC
Can you please tell the Risk Insights readers a little bit about yourself, your experiences and what your current professional focus is?
I am a physicist by training, but ultimately a problem solver at heart. I enjoy working on technical issues and coming up with a well-designed solution. This is re-enforced by the fact that I have been involved with models my entire career. I am currently leading the Traded Risk Analytics team for HSBC, responsible for Traded Risk models spanning Market Risk and Counterparty Credit Risk. As such, themes such as FRTB often appear in my inbox!
What, for you, are the benefits of attending a conference like the Risk EMEA 2019 and what have attendees learnt from your session?
The key benefit is to network with a variety of like-minded or not so like-minded professionals. Conferences are a great opportunity to look up from the desk and see what is going on in the wider world. The Risk EMEA 2019 was just that and I enjoyed hearing about a number of broader themes impacting the Risk domain. In my session, I shared my thought process on Model Risk Management. It was an exciting new field where the answers are from clear.
What are the key considerations for effective model risk controls?
Effective governance. By that, I mean model risk needs to be transparent and relevant for it to be understood. Without this, clear and effective decision making cannot be supported.
Please describe some of the challenges involved in maintaining existing models in the face of changes (such as technology) or regulatory pressure?
The biggest challenge is simply the pace and volume of change. Whilst a prototyping environment may be able to adapt and keep up, the bank’s infrastructure and data framework can struggle. As such, there is greater focus on bringing greater innovation into the infrastructure (e.g. Cloud).
Please describe potential benefits of increased use of technology, as well as potential pitfalls?
Banks have always been huge buyers of technology. The key challenge is that the pace of change and innovation in technology is speeding up. Buying the right technology at the right time can therefore be harder. There has also been a lot of attention on ML and AI. This has some clear benefits as well as some well-publicised challenges.
In terms of accuracy and efficiency, what are the key considerations to improve efficiency, limit changes and errors and tracking?
Create a collaborative development environment with sufficient feedback loops. Knowing what is “accurate” can take a few iterations!
