Artificial Intelligence Consortium minutes – June 2026

Co-chair David Geale welcomed attendees to the AI Consortium’s (AIC) fourth quarterly meeting, held at the FCA’s offices. Co-chair Sarah Breeden noted that the rapid pace of AI development in financial services underscores the importance of initiatives such as the AIC in identifying how regulators can best support responsible AI adoption.

The contributions during the session were under Chatham House Rule.

Each group provided an update on their near-complete workshop analysis followed by discussion. 

Workshop 1: Explainability and transparency in generative AI

The workshop leads set out their approach to explainability and transparency in generative AI (GenAI), anchored in an outcomes-based definition focussed on understanding decision-making and whether systems are behaving as intended. Members outlined how the group has used a quality assurance case study to demonstrate how both explainability and transparency can be addressed in AI systems through features such as human in the loop (HiTL), adversarial testing, and accountability and auditable documentation of decisions.

The workshop identified recurring model risk challenges arising from GenAI such as system complexity and interconnectivity, limited transparency from third-party providers, and variability and unpredictability of outputs. The members noted these features can mean it is difficult to apply existing model risk frameworks proportionately. To address this, the workshop considered the value of managing model risk from an AI model system perspective, recognising that most use cases involve multiple interconnected models and components. Members emphasised the importance of governance across the full AI model system, transparency on how components interact, and testing both the AI model system as whole and its individual elements, noting that models can be updated independently.