The UK regulator examines how machines will be able to interpret and implement regulatory reporting rules.
The UK Financial Conduct Authority (FCA) is embracing regulatory technology (regtech) and various artificial intelligence (AI) solutions in order to enhance regulatory compliance.
Nick Cook, Head of Data and Information Operations at the FCA, has shed some light on the plans and steps taken by the UK regulator in this respect. According to a report by CNBC, featuring Mr Cook’s comments made at London Fintech Week on Tuesday, the regulator is examining feedback from regtech firms in order to inform itself about the adoption of automated, digitized compliance.
The FCA is looking at the possibility of making its Handbook machine-readable and then fully machine-executable. This means that machines can interpret and implement the rules directly.
The range of applications currently examined by the FCA include enhanced use of speech-to-text analytics tools within the FCA, as well as solutions that allow better use of social media analytics. The regulator would also utilize AI to detect financial irregularities, Mr Cook said.
The comments by Nick Cook are in line with the latest developments around FCA’s regulatory sandbox. The regulator had received 77 submissions for the second phase of the regulatory sandbox, which allows firms to test innovative products and services in a live environment while making sure that consumers are protected in the right manner. The accepted propositions from firms in the second cohort cover a range of ideas including distributed ledger technology based payment services and artificial intelligence software to observe client behaviour and to help determine client preferences before financial advice is provided. Blockchain solutions and AI technology clearly dominate the second cohort of projects.
The Bank of England is also welcoming AI and Blockchain solutions as shown by the third round of Proofs of Concept (POCs) completed by its FinTech Accelerator.
The Bank has collaborated with Mindbridge Ai, a machine learning and AI firm, to explore the analytical value of using AI tools to detect anomalies in supervisory data sets. This PoC allowed the Bank’s internal team of scientists to compare and contrast their own findings and the underlying algorithms being used, providing an additional layer to the Bank’s work.
An interesting development was seen in the Bank’s collaboration with Ripple in the area of distributed ledger technology (DLT). In this PoC, the Bank and Ripple studied how DLT could be used to model the synchronised movement of two different currencies across two different ledgers. This formed part of the Bank’s vaster research into the future of high-value payments.
The Bank has earlier concluded that DLT is “not sufficiently mature” to support the core RTGS system, but the test with Ripple has supported the Bank’s intention to ensure its new RTGS system is compatible with DLT usage in the private sector.