In Focus Collections
solutions is making the step from proof of concept to proof of value – being able to demonstrate to these potential customers in the financial services industry that your solution not only works, but may provide a more efficient and effective way of doing things than they are currently done. In other words, to develop trust in your
solution and develop the business case for deployment and implementation – to justify the security, procurement risk and cost requirements that these involve. We have also heard from RegTech firms
that a missing rung in the ladder to market is the lack of access to high-quality synthetic data assets against which to test new technology solutions. So, one of the things we are currently
looking at and beginning conversations around is what that digital testing environment might need to look like, what role we ought to play in supporting its creation, and whether it could be something that could be scaled across jurisdictions. We already have a bit of experience on this
front in terms of the way we run TechSprints, essentially creating an ephemeral ‘digital sandbox’ where very focussed proofs of concepts can be tested and then demonstrated to industry for a very finite period of time, with the environment itself usually only being in existence for a week or so. We have received great engagement from
RegTech firms at the TechSprints we have held in recent years, so feel that exploring a permanent, data-rich testing environment is something really worth pursuing. As well as calling out three outcomes that
we would like to see people with ideas bring forward in Cohort six, we also mentioned two specific technologies that we are interested in firms exploring the potential of. I mentioned at the outset the need for regulators to anticipate what might be coming next, to identify early the potential actors of change and be on the front foot rather than reactive. This is a tactic we employed at our
recent Global AML and Financial Crime TechSprint where we specifically brought tech firms and the industry together to explore the potential of Privacy Enhancing Technologies to make improvements in the area of financial crime detection.
January 2020
The first of these technologies is ‘federated
learning’ or ‘travelling algorithms’ which potentially allow entities to develop and refine more performant algorithms which are trained on multiple data assets without bringing those assets together. We recognise this is a nascent field and
are keen to work with innovators to explore its potential further. The second area is ‘complex scenario
modelling and scenario simulation’. This is basically a fancy way of saying that we would like to see how graph analytics, behavioural science and deep learning can be used to better model relationships, connections and behaviours in financial markets. And to then test various simulations and scenarios against these models. For regulators, this could mean
understanding with greater precision the impact of planned or potential policy interventions. For firms, this could help
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We recognise this is a nascent field and are keen to work with innovators to explore its potential further
them ensure that the products they are developing do not pose any unintended risks to certain customer groups, such as vulnerable consumers, before they are released into the market. These will not be the only occasions or
areas where we call out specific issues and problems to which we would like to see further innovation and progress. CCR
Edited from a speech given at the CDO Exchange 35
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