Artificial Intelligence in the Financial Services Sector: Uk Regulators Publish Feedback Statement

• Publication year: 2024
• Citation: Vol. 7 No. 2

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Ferdisha Snagg and Andreas Wildner ^*

In this article, the authors analyze a Feedback Statement relating to Artificial Intelligence and Machine Learning published recently by the Bank of England.

On October 26, 2023, the Bank of England, including the Prudential Regulation Authority (the Bank), and the UK Financial Conduct Authority (the FCA) published a Feedback Statement relating to Artificial Intelligence (AI) and Machine Learning (the Feedback Statement). ¹

The Feedback Statement summarizes the responses received to the regulators' earlier discussion paper published in October 2022 (the Discussion Paper). ²

While the regulators emphasize that the Feedback Statement does not include specific policy proposals or commitments to any specific regulatory approach, the Discussion Paper and Feedback Statement are important indicators of the potential direction that UK financial-services regulation will take in respect of AI. This is both in relation to overarching regulatory approaches (such as the aim to achieve cross-sectoral and cross-jurisdictional alignment) and specific areas requiring particular consideration (such as consumer protection and the significance of data).

This article sets out the context against which the Feedback Statement has been published, and the key themes emerging from it.

Context

In October 2020, the Bank and the FCA established the AI Public-Private Forum (the AIPPF). The AIPPF's aim was to bring together a diverse group of experts from across financial services,

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the tech sector, and academia, to further dialogue on AI innovation and safe adoption within financial services.

In February 2022, the AIPPF published its final report, ³ exploring the various barriers to adoption, challenges, and risks related to the use of AI in financial services. The AIPPF focuses on three core areas:

1. Data, with an emphasis on the importance of data quality, on the understanding of data attributes (including provenance, completeness, and representativeness), and on ongoing documentation, versioning, and monitoring. The final report also observes that the use of unstructured or "alternative" data in AI and machine-learning contexts can increase risks and issues relating to data (e.g., quality, provenance, and sometimes legality).
2. Model risk, the key challenge in this respect being complexity, for example, as regards inputs (e.g., because of a large number of input layers/dimensions), relationships between variables, models themselves (e.g., deep-learning models), or outputs (e.g., actions, algorithms, quantitative, or unstructured outputs). An important factor is explainability, with a focus not only on the features or parameters of models but also on engagement with, and communication to, consumers.
3. Governance, highlighting that existing frameworks (e.g., data governance, model risk management, operational risk management) provide a useful starting point for governance considerations, but should reflect risks and materiality of any specific use cases. The report also considers that governance standards should be set by a

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