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[3]

Read the 2019 survey findings (PDF)[2]

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)