Can Using Software to Map Financial Risks Predict the Next Downturn?
Preparing to face the next financial crisis may well be about doing a better job of anticipating potential risks. Kimmo Soramäki, founder and CEO of the London-based Financial Network Analytics, or FNA, has been doing just that. FNA designs platforms, software and algorithms that help central bankers, regulators and other institutions better visualize complex issues such as interconnectedness, systemic risks and even warning signs of financial crimes like money laundering. Some users, like Canada’s payments and settlements organization, have used this technology to visualize potential liquidity pressures at banks and help reduce them. Soramäki founded and serves as editor-in-chief the Journal of Network Theory in Finance, which serves as a forum for central banks and others in the financial services ecosystem to learn new techniques to address risks and share experiences. He started his career as an economist at the Bank of Finland, where in 1997 he developed the first simulation model for interbank payment systems. During the financial crisis of 2007-2008, he advised Group of 10 (G-10) central banks in modeling interconnections and systemic risk. In a recent conversation with Knowledge@Wharton, Soramäki discussed how modeling, simulations and analytics could help make the financial system safer and more efficient. (He will be discussing how to future-proof financial market infrastructures with artificial intelligence and machine learning at the upcoming Sibos conference in Sydney next month. This interview is part of an editorial collaboration between Knowledge@Wharton and The SWIFT Institute.)
AT THAT TIME, CHINA was not well integrated with the rest of the world. When we looked at clusters of countries that interact a lot with each other, China was in the same cluster as the U.S., with the U.S. leading that cluster. Others included the European cluster, an African cluster as well, and then a cluster around the countries of the former Soviet Union that [maintained] strong trade links with one another. If we were to do that research again, China would probably start to form its own cluster very soon, if it hasn’t already, around Southeast Asia.
Knowledge@Wharton:
How could central banks gain a deeper understanding of risks using advanced analytics, perhaps on your platform?
Soramäki:
The Hong Kong Monetary Authority established HKTR, one of the [many] trade repositories that collect trade information and have been set up all over the world. In Europe, it’s under a directive of the EMIR (European Market Infrastructure Regulation). In the U.S., trade repositories fall under the Dodd-Frank Act.
Much of the work of the central banks or institutions that have been looking at that data is in understanding the data. It takes a long time to understand questions such as: “What do I have? How much of the global market does it cover? How should I interpret all the numbers I see? Are there errors? Are they real? Do they come from different sources? How do I integrate all of this together?”
Much research has been done on it, but I haven’t seen a lot of the operationalization of that data into ongoing monitoring yet, mainly because it’s such a new dataset. The research is focused on issues like: How much of the market does it actually cover, because it might be that some institutions are taking risks in one part of the market that we see in the data, and uploading them in other parts of the markets, for which we don’t see the data. We need to be very careful about the conclusions we draw before we fully understand what we have in our hands. Knowledge@Wharton: You also do simulations on your platform. What types of simulations are now possible of complex financial systems? Could you also give an example or two of what insights are being gained that would not have been possible through previous technology?
Soramäki: The simulations need to be domainspecific, because the devil is always in the details. We have been focusing on simulations of financial market infrastructures. It’s something that I’ve been doing for over 20 years. In the late 1990s, I programmed or started what is called the Bank of Finland Payment System Simulator, which also has been used by many institutions, mostly for research purposes. In our software, we also have a simulator that allows you to pretty much simulate any type of financial market infrastructure, [such as] a payment system or a central counterparty or a security settlement system. When you’re designing a new system, you want to simulate it. You want to try out how the system works and try out different features of the system.
Payments Canada has a multi-year modernization project for the Canadian payment systems. They are moving from a large value payment system called LVPS into a new system, which is based on real-time gross settlement, which is the international norm. They were concerned that the liquidity requirements of the banks would increase as a consequence of moving to this new system.
Every dollar that they need to keep at the central bank is taken away from maybe some profitable purposes, and that’s a cost for the bank. So they want to minimize the amount of money that they need in order to make these payments. In a small project with Payments Canada, we used our simulator and showed that by employing some smart liquidity-saving mechanisms, and some algorithms, we could reduce the liquidity requirements by 40% from what they initially thought, which of course was very good news for the banks.
For the past year, Payments Canada has been carrying out different simulations and coming up with new ways of providing this service to banks that is more efficient than the previous one. That is a good bottomline impact of being able to do simulations and coming up with new designs that help make the system less costly and less risky, as well.
Knowledge@Wharton:
Are there still some risks that you’re unable to track, that you’d like to do better in the future?
Soramäki:
Yes, and these are often mostly related to data availability. Everyone complains that they provide more and more data to regulators. Large amounts of data already exist. But you can only get the true picture if you have pretty much everything, because there might be some risks that are taken in some part of the market and are offset in the other. So if you don’t have the full data, you really can’t get the full picture, either.
[Now], with [increased] data availability, we are in the early stages of [understanding] how we could interact with these large datasets. How do we make them so simple that we can come up with some insights from them? The challenge in every project we go to is we need to do some work in order to be able to prove that there are valuable insights that you can get from looking at the data. Another challenge with artificial neural networks and machine learning techniques is that they might give you a result, but don’t tell you why. So more research work needs to be done [in this area].
Knowledge@Wharton:
We just passed the 10th anniversary of the 2008 financial crisis. Based on everything that you see today – this wide spectrum of risks that you described – what do you think are the chances of another global financial crisis? Where do you see the greatest risks and vulnerabilities, and what can be done about them?
Soramäki: The next financial crisis is a certainty, but always it takes longer to materialize than anyone expects. I think that was the case with the last financial crisis, as well. We haven’t figured out the remedy to the financial crisis that we’ve been having every eight or 10 years for the past 150 years or even longer. Our biggest risks relate to some large changes that have happened or are happening. [Consider] quantitative easing – we don’t know how that will play out. It’s a very big risk. I think a slowdown or something bad happening in China is also another big risk. The Chinese financial market has exploded in the past years, and there is very little visibility to that. Those are the drivers that may be behind the next financial crisis.
We’ve made it easier for any central bank, any regulator or any authority to start doing data analytics and simulations….