SONNETS: Scalability Oriented Novel Network of Event Triggered Systems

SONNETS - Scalability Oriented Novel Networks of Event Triggered Systems - takes a clean-slate approach to next-generation computer modelling and artificial intelligence. To drive this we have an over-arching research goal that is both nationally important and challenging: real-time modelling of UK financial risk.

It is easy to identify underlying risks after they cause a financial crisis. With hindsight, the 2008 financial crash was caused by too many banks buying too many risky mortgages. Whilst the crisis was unfolding it was all new information: no-one realised how many banks owned the risky mortgages. Then it was assumed that mortgage defaults were unlikely. Finally, it was assumed that losses in a few banks would not affect the national economy. The problem was a lack of visibility and understanding of the national picture: each bank appeared to have a manageable risk level, but most banks in the UK were exposed to the same underlying risk factor, so once mortgages started defaulting most banks started losing money and a perfect financial storm developed. What we needed then, and still do now, is national-level risk modelling that can consider risk across banks as it occurs.

Modelling risk for one bank is a difficult problem, and modelling the entire UK is much harder. Banks have complex constantly changing portfolios, so building a picture of "who owns what" means tracking millions of trades per day. Even if we have that picture we still need to somehow assess risk, but that requires anticipating the future: we must pre-emptively identify potential scenarios, then estimate how much is lost in each scenario. Currently regulators use "stress tests" to identify national risk - they define a possible challenging economic scenario, then ask all the banks to estimate how much they might lose. However, this is both slow - the process takes months - and limited - they only explore one very severe scenario, which probably isn't the one that causes the problem.

SONNETS will create a system that performs national-level risk analysis in real-time, by building a "digital twin" of the UK's financial system and using it to continually generate plausible future scenarios and assess their risk. We then use artificial intelligence to learn what risky scenarios look like. This gives regulators completely new tools:
- A day-by-day view of the current national-risk of the UK, rather than waiting months for stress tests;
- The ability to look forwards to identify and mitigate previously unknown risks as they develop, rather than waiting for a financial crisis to reveal them.

We tackle this problem by addressing challenges in three main areas:

- Computing: new paradigms for creating and running programs, exploiting multiple types of computer hardware distributed across the cloud;
- Artificial Intelligence: methods for continual learning that can be split into multiple pieces, so that learning processes can be moved closer to the data they are learning from;
- Modelling: theory and tools for automatic scenario generation, plus the ability to assess risk over large-scale models of the UK's financial institutions.

These three areas are tightly linked, with the new computing paradigms supporting execution of the new AI and modelling in the cloud, and a synergistic relationship between the modelling of the system and learning about the model.

Underpinning these three areas is the idea of event-triggered computing, where programs are split up into small fragments which send messages to each other. Using this event-triggered approach we can scale the risk analysis system up to support national-level risk analysis. It will constantly assess how risky the UK currently is, while trying to anticipate what scenarios might lead to financial crises in the future.

SONNETS will provide a powerful tool to detect and mitigate financial risk as it is building up, rather than trying to react to a financial crisis once it happens.