Using AI has increased in UK financial services over the past few years, both in the number and types of uses. Although AI has many benefits, including improving operational efficiencies and providing customers with personalised services, it can also present challenges to the safety and soundness of firms, the fair treatment of consumers, and the stability of the financial system. With this in mind, the Bank of England and Financial Conduct Authority have published the results of a survey of AI and machine learning in UK financial services.

Use and adoption

75% of firms are already using AI, with a further 10% planning to use AI over the next three years. Foundation models form 17% of all AI use cases, supporting anecdotal evidence for the rapid adoption of this complex type of machine learning.

Third-party exposure

A third of all AI use cases are third-party implementations – this is likely to increase as the complexity of models increases and outsourcing costs decrease. The top three third-party providers account for 73%, 44%, and 33% of all reported cloud, model, and data providers respectively.

Automated decision-making

Respondents report that 55% of all AI use cases have some degree of automated decision-making with 24% of those being semi-autonomous – that is, while they can make a range of decisions on their own, they are designed to involve human oversight for critical or ambiguous decisions. Only 2% of use cases have fully autonomous decision-making.

Materiality

62% of all AI use cases are rated low materiality by the firms that use them with 16% rated high materiality. Materiality is a rating of the use case impact which could include quantitative and qualitative measures.

Understanding of AI systems

46% of respondent firms reported having only ‘partial understanding’ of the AI technologies they use versus 34% of firms that said they have ‘complete understanding’. This is largely due to the use of third-party models where respondent firms noted a lack of complete understanding compared to models developed internally.

Benefits and risks of AI

The highest perceived current benefits are in data and analytical insights, anti-money laundering and combating fraud, and cybersecurity. The areas with the largest expected increase in benefits over the next three years are operational efficiency, productivity, and cost base. 

Of the top five perceived current risks, four are related to data: data privacy and protection, data quality, data security, and data bias and representativeness.

The risks that are expected to increase the most over the next three years are third-party dependencies, model complexity, and embedded or ‘hidden’ models.

Cybersecurity is rated as the highest perceived systemic risk currently.

Constraints

The largest perceived regulatory constraint to the use of AI is data protection and privacy followed by resilience, cybersecurity and third-party rules and the FCA’s Consumer Duty.

The largest perceived non-regulatory constraint is safety, security and robustness of AI models, followed by insufficient talent and access to skills.

Governance and accountability

84% of firms reported having an accountable person for their AI framework. Firms use a combination of different governance frameworks, controls and/or processes specific to AI use cases – over half of firms reported having nine or more such governance components.

While 72% of firms said that their executive leadership were accountable for AI use cases, accountability is often split with most firms reporting three or more accountable persons or bodies.

In conclusion, the sector is making good use of AI but concerns remain, especially about the use of personal data and cybersecurity.