The Financial Conduct Authority (FCA)'s Chief Data, Information and Intelligence Officer Jessica Rusu said Monday that the markets watchdog -- which oversees 50,000+ firms -- is launching a new service called Innovation Pathways that will provide bespoke regulatory support to firms with innovative business models, with the regulator putting greater emphasis on sharing "insights into its data" and the support provided to firms.
The news comes a week after the FCA also announced plans to explore the use of synthetic data -- AI-generated data that is "representative but not real" -- to improve innovation. (Speaking to The Stack earlier this year BNP Paribas's head of cyber risk Ramy Housseini noted that "A lot of problems can be solved by leveraging synthetic data.. you can address constraints around R&D and innovation. Yet synthetic data adoption is still very low.")
"Alongside our now ‘always open’ regulatory sandbox, Innovation Pathways will play a key role in informing and ensuring our regulatory environment is fit for future innovation" Rusu said at the Innovation Finance Global Summit 2022 -- noting that 92% of firms that use the FCA's existing innovation services end up authorised: "These services also support firms outside the regulatory perimeter. This provides us with additional insights into the technologies being used and their potential implications, to inform our future policy development."
Synthetic data: What's cooking?
The FCA opened a "call for input" on March 30, 2022 on "the extent to which synthetic data can expand data access and data sharing opportunities in the market" -- seeking case studies and insight from academics, practitioners, regtechs and fintechs, regulators and other policy-making bodies around the world. (Get involved here.)
Rusu -- who previously led analytics at eBay -- noted in her speech: "Financial data is highly sensitive and rightly subject to data privacy laws to protect consumers...
‘Synthetic’ data is a privacy-preserving technique that expands the opportunities for data to be accessed and shared; a key building block for innovation. Having observed the utility of synthetic data through our Digital Sandbox initiative, last week we published a Call for Input to gather views and assess the potential of synthetic data to further spur innovation in the market."
Good case studies of synthetic data use, are, perhaps ironically, still hard to come by.
Austria's Erste Group -- one of central and eastern Europe's largest banks -- is among those to have discussed its work on synthetic data in a project delivered alongside startup Mostly.ai. Using synthetic data generated by AI to simulate the characteristics and behavior of real data, the two worked to innovate across a range of features and test how customers might react to them, as well as working on anti-fraud techniques.
Maurizio Poletto, Chief Platform Officer at Erste Group Bank AG, told Harvard Business Review: “We could take a fraud case using synthetic data to exaggerate the cluster, exaggerate the amount of people, and so on, so the model can be trained with much more accuracy. The more cases you have, the more detailed the model can be.”
As he also noted, synthetic use can also be vital for not just iterating fast but retaining data scientists: "“Talented data engineers want to spend 100% of their time in data exploration and value creation from data. They don’t want to spend 50% of their time on bureaucracy. If we can eliminate that, we are better able to attract talent. At the moment, we may lose some or they are not even coming to the banking industry because they know it’s a super-regulated industry and they won’t have the same freedom they would have in a different industry.”
JPMorgan is another firm to have experimented widely with synthetic data, deploying it to help detect anti-money laundering (AML) behaviour; understand customer journey events, analyse markets execution data and also payments data for fraud detection. As Manuela Veloso, Head of AI Research at the firm, has reflected: “Synthetic data generation allows us to think, for example, about the full lifecycle of a customer’s journey that opens an account and asks for a loan. We’re not simply examining the data to see what people do, but we’re also able to analyze their interaction with the firm and essentially simulate the entire process.”
Are you using synthetic data? We'd love to hear how. Get in touch with your case studies.