By Justin Lyon, CEO, Simudyne
Justin, can you please tell the Risk Insights readers a little bit about yourself, your experiences and what your current professional focus is?
Following 9/11, I went to MIT to study computer simulation which led me to the realisation that I wanted to spend my life trying to deeply understand how important phenomena around us emerge and how complex adaptive systems work. I wasn’t convinced with the prevalent narratives, and with the explosion of data there had to be a better way for us to explore our environment and plan our best future.
Before Simudyne I worked with institutions and companies helping them to understand how advanced analytics, simulation and artificial intelligence can help businesses. Since then, I’ve incorporated this experience from companies around the world and dedicated myself to making Simudyne what it is today: the next generation simulation platform that is enterprise ready and used by global banks to help manage risk and be profitable. We cracked the engineering challenge of building the software that allows businesses to create high fidelity models of the real world so they can test drive their decisions and see the likely outcomes in a safe virtual environment. In today’s world of complex interdependencies, I don’t think we can do business any other way.
Can you briefly explain the importance of exploring ‘what-if’ scenarios?
Risk management is concerned with ensuring a system is robust against a range of scenarios. One way to do this is to look to history and ensure that today’s system is robust to yesterday’s shocks. But risk managers also need to ensure that their system is robust to future shocks.
To do so, risk managers need to generate hypothetical states of the world. What if Brexit triggers a sell-off in the housing market? Or what if a geopolitical event in the Middle East causes oil prices to double?
Risk managers also want to understand the consequences of their actions. What if I change my pricing strategy? What if I tighten my lending criteria?
So they need to be able to study hypothetical actions under hypothetical scenarios. And they want to be able to this quickly and easily to ensure they have the ability to horizon-scan against a wide-range of possible futures.
At the Risk EMEA 2018 Summit, you will be speaking on your insight regarding ‘An agent-based model approach to balance sheet management’. Why is this a key concern right now? And what are the essential things to remember?
Agent-based models have received increased attention from market participants and regulators alike. These models have enormous potential to better explain some of the complex phenomena witnessed in financial markets. And they also provide a means to gain insight into questions that were previously unanswerable.
The key difference between agent-based and traditional models is that ABM are built from the bottom-up. The modeler attempts to receate their world – and uses the power of computational simulation to generate complex and emergent outcomes that result from the interaction of millions or billions of constituents.
What are some of the key considerations with capital allocation?
Banks continue to adapt to more stringent capital requirements and are increasingly turning their attention to increasing the return on equity they generate for their shareholders. Efficiently allocating scarce capital is more important than ever as banks look to hit their ROE targets.
One way to better allocate capital is to study borrower behavior at a lower level of granularity. For example, portfolios could be better optimized by taking into account dynamics at the individual household or postcode level, rather than the aggregated portfolio level.
Why are agent-based models a key talking point for 2018?
Agent-based models are gaining increasing attention from regulators around the world. Andy Haldane (Chief Economist and Executive Director for Monetary Analysis) at the Bank of England has written a paper calling for the adoption of ABM as a supplement (or even perhaps as an alternative) to DSGE models. And teams at the Bank of England and European Central Bank continue to develop Agent-based Models for policy analysis and stress testing.
The insights that regulators are seeking with these models are also valuable to the industry, and we are seeing significant interest from Tier 1 banks in leveraging this modelling paradigm across a range of use-cases.
How do you see the risk landscape evolving over the next 6-12 months?
I see banks increasingly looking to generate business-as-usual value from the regulatory risk infrastructure they have put in place since the financial crisis. Stress testing is a good example of this – banks have spent tens, if not hundreds, of millions ensuring that they are able to fulfil their stress testing obligations. There is now increased focus on using the data and models developed to generate risk management value day-in, day-out.
Leveraging this infrastructure to run multiple scenarios at the press of a button and explore what-if questions will allow risk managers to get an unprecedented view of the risks on their balance sheets, and ultimately, to make radically better decisions.
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