Research Note: AI in UK financial services

Infographic with 9 blocks showing key facts and statistics about AI use:

• 75% of firms are using AI. 10% more plan to in the next 3 years.
• Foundation models (including LLMs) make up 17% of AI use cases.
• 33% of AI use cases are from third parties.
• 55% of AI use cases have some automated decision-making. Only 2% are fully autonomous.
• 62% of AI use cases are low materiality vs 16% high (as rated by firms).
• Greatest perceived benefits are in data and analytical insight, AML and combatting fraud, and cybersecurity of AI use cases have some automated decision-making. Only 2% are fully autonomous.
• Greatest perceived risk is cybersecurity. Growing risks are third party dependencies, model complexity, and embedded models.
• 84% of firms have a person accountable for AI.
• 34% of firms have ‘complete understanding’ of the AI they use, 46% have a ‘partial understanding’.
   

We’ve published joint reports with the Bank of England on past surveys on machine learning (ML) in 2019 and 2022 which covered:

• a quantitative overview of the use of ML
• the ML implementation strategies of firms
• approaches to the governance of ML
• the share of ML applications developed in house or by third party providers  
• respondents’ views on the benefits of ML  
• perceptions of risks and ethical considerations
• perspectives on constraints to development and deployment of ML
• a snapshot of the use of different methods, data, safeguards performance metrics, validation techniques and perceived levels of complexity of ML  

Read the 2022 survey findings

Read the 2019 survey findings (PDF)

The 2022 report closes with a selection of case studies, describing a sample of typical use cases, including:

• insurance pricing and underwriting
• credit underwriting
• marketing
• fraud prevention and anti-money laundering (AML)