The Bank of England (BoE) and Prudential Regulation Authority (PRA) have published a letter responding to Government questions on the delivery of safe and responsible AI and machine learning ("ML").
The letter concludes that both the BoE and PRA support the Government's pro-innovation framework, and that the existing regulatory framework appropriately supports delivery of the benefits brought by AI and ML in line with that framework. The five cross-sectoral principles established by the UK's Initial Guidance for Regulators are identified as being broadly consistent with the PRA and BoE's own approach
However, the letter makes clear that the BoE and PRA will keep issues under review and continue their extensive engagement with the technology sector, academia and financial services firms due to the rapid pace of innovation and the evolution of use cases.
Approach to regulation
Taking into account their statutory objectives and approach to regulation, the BoE and PRA are currently focused on "understanding how to support the safe and responsible adoption of AI/ML in financial services from a macro-financial and prudential perspective, given the potential benefits – including driving innovation – that AI/ML could bring to firms."
For that purpose, the existing regulatory framework is considered to be 'well-equipped' for regulated firms use of AI and ML.
However, the letter emphasises that although a technology-agnostic approach is currently being followed, the use of certain technologies affecting statutory objectives and creating risk for both firms and regulators may warrant new guidance and clarification of existing rules in due course.
Current and future work on the safe adoption of AI and ML in financial services
The BoE and PRA have various projects underway/planned to understand, assess and manage the risks presented by AI and ML including:
- Exploring four potential areas where further clarification on the regulatory framework could be beneficial, being: (1) Data Management; (2) Model Risk Management; (3) Governance; and (4) Operational Resilience and Third-Party Risks. In addition, deeper analysis on the potential financial stability implications of AI/ML will be undertaken over the course of this year. This analysis will be considered by the Financial Policy Committee.
- Working alongside other relevant authorities both domestically and internationally, ensuring that the UK financial system is resilient to risks that may arise from widespread adoption of AI/ML. The need for regulatory cooperation will be an ongoing process. Discussions are continuing with the FCA and other regulators around the safe adoption of AI and ML in the financial services sector.
- Working with the Digital Regulation Cooperation Forum (DRCF) on selected AI projects, which includes conducting joint research to better understand cross-sector adoption of generative AI technology. The BoE is also a member of the Information Commissioner's AI and Regulators Working Group and is represented on the Alan Turing Institute's Standards Hub Regulators' Forum.
- Continuing to build on established frameworks to enhance the cyber and operational resilience of the financial sector.
- Commencing the third instalment of the ‘ML in UK financial services’ survey this year, and considering the establishment of an industry-wide AI consortium to follow-up on the AI Public-Private Forum.
- Establishing a cross-organisation AI task force to ensure that progress using AI is made effectively, safely and responsibly. The three aims of the taskforce are to identify and pilot promising AI/ML use cases, to develop appropriate guiderails to ensure risks from using AI are controlled and identify training need to ensure AI/ML can be used effectively.
- Internally, the BoE and PRA are currently using AI where appropriate to support and enhance their capabilities. The BoE uses AI in predictive analytics potentially helping forecast GDP growth, bank distress, and financial crises. The PRA has successfully introduced a cognitive search tool with AI capabilities to extract key patterns from unstructured and complex datasets to help supervisors gain more insight from firm management information.
A copy of the letter can be found here.
