The gang at FNA not only appreciate the complexity of mapping multifaceted relationships to better understand modern financial markets, they also take the time to appreciate Mancunian beer that showcases local artists.
What is FNA – what problem are you solving?
Since the financial crisis of 2007–2008, regulators and supervisors have recognised the interconnected nature of financial systems, and now have access to increasingly large amounts of granular data from which it’s possible to understand that interconnectedness. With the right tools they can understand how market events can affect the whole network. FNA operationalises advanced data analytics, artificial intelligence (AI) and machine learning (ML) techniques, allowing financial regulators and FMIs to map and monitor complex financial networks and to simulate operational and financial risks.
Why have strategies for understanding financial institutions and market behaviour, which have been in use for years, now become insufficient?
Markets have changed, but risk management strategies have stayed much the same. Participants are more interconnected and events propagate across the system at high speed, often before firms have a chance to react.
The data that is now available allows us to gain far deeper insights into the effects of market behaviour on the financial system, and new analytics techniques mean we can gain these insights much more quickly and operationalise them extremely efficiently. Traditional risk management methods are not capable of understanding the modern financial markets, let alone building in the effects of interconnected institutions, participants or individuals.
What are some new approaches to big data analytics that can be used?
One of the key areas in which FNA specialises, and which is instrumental to analysis of large data sets, is the application of network science or graph analytics. This allows us to model complex multifaceted relationships (between individuals, companies, counterparties, transactions, etc), and to use filtering techniques to focus on the most salient relationships. Graph of network “features” can also be used to inform and “train” machine learning models.
What advice would you give new startups in this space?
As in all areas of business and life, focus is key. Startups need to “start small” with a small problem set to which it can offer a solution(s), prove value to clients in that solution area, and then build out from there.
Another differentiator that is becoming easier to harness is global talent sourcing, with collaboration tools making it much easier to work across the world effectively, allowing for startups to look at a much broader talent pool, particularly when sourcing for roles that don’t require regular on-site time with clients/customers.
We have an array of craft beers and independent music at FinTECHTalents – Give us a great song to listen to AND tell us your favourite tipple .
There’s a great craft beer scene in the UK these days and it would be easy to suggest a London-based brewer. The rest of the UK is, however, producing some amazing, interesting beers. As an example, Cloudwater, based in Manchester, create lightly flavoured, subtle beers collaborating with local artists to design their cans. Their “A Moment at Emmanuel Head” is well worth a try.
FNA will be presenting as part of the FinTech Stories Stage at FinTECHTalents and part of RegTECHTalants this November.