- The Financial Conduct Authority is looking into the possible use of artificial intelligence and machine learning to enforce regulatory compliance
- U.K. regulator says it is "still learning", and is working with regulatory technology (regtech) firms and international regulators alike
- A report has said that regtech implementation could lead to "far more efficient regulatory compliance"
The Financial Conduct Authority, an independent U.K. financial regulatory body, is looking into the possible use of artificial intelligence (AI) and machine-learning tools to enforce regulatory compliance.
Nick Cook, the FCA's head of data and information operations, said that the regulator was "still learning", and that feedback from a "call for input" from regulatory technology (regtech) firms was informing it about how to support the adoption of automated, digitized compliance.
"We are looking at the extent to which we can make parts of our handbook initially machine-readable and then fully machine-executable ... Effectively converting, probably initially our regulatory reporting rules, into truly unambiguous rules that machines can interpret and implement directly," he told an audience at London Fintech Week, a financial technology (fintech) conference, on Tuesday.
"The idea being that we can put out rules which are written manually in ways that can be fully and unambiguously interpreted by machines," he added.
Regtech (regulatory technology) is a subset of the financial technology (fintech) industry. Experts believe it could drastically reduce the cost of regulatory compliance, currently estimated to be around $80 billion globally.
The industry's current focus has been improving regulatory reporting procedures for banks and financial services firms, such as increased automation and digitization of Know Your Customer (KYC) rules, Anti-Money Laundering (AML) rules, and tax reporting.
Cook said machine-learning in regtech would enable firms to operate under regulations more efficiently, and in a way which would strip down "enormous costs" needed for companies to interpret regulatory requirements.
"There's a whole series of steps in making this happen, and we're working on it quite intently at the moment," he said.
Cook added that the FCA was looking into the practical use of machine-learning to make better use of speech-to-text software, social media and media analytics.
"We have a huge amount of programs and initiatives at the moment, and they range from better use of things like speech-to-text analytics tools within the FCA, through to how we make better use of social media analytics and media analytics, through to how we use financial processing tools to automate some of our processes."
The FCA would utilize "supervised machine-learning from these analytics," Cook said, and "unsupervised AI" to detect financial irregularities.
He added: "We're looking at these underlying technology approaches and regtech solutions to try and see how we can employ them internally to be more efficient and to better identify which solution (works) for the financial markets."
Cook said that the U.K. regulator was working with global regulators as well as regtech companies to showcase its work and to learn from others around the world.
He said: "A lot of our fintech over the last year has been looking into regtech's application for ourselves as well, and encouraging and bringing other international regulators to the table. We take a very active role in trying to expand the regtech discussion globally. Equally though, we seek to learn what's going on elsewhere."
A report published last October by the University of Hong Kong identified the regtech industry as a field capable of addressing risk in "real time" and increasing the efficiency of compliance.
"Regtech to date has been focused on the digitization of manual reporting and compliance processes, for example in the context of know-your-customer requirements," it said.
"This offers tremendous cost savings to the financial services industry and regulators. However, the potential of regtech is far greater – it has the potential to enable a close to real time and proportionate regulatory regime that identifies and addresses risk while also facilitating far more efficient regulatory compliance."