Artificial Intelligence Consortium minutes – October 2025

Each group outlined its progress including the workshop’s problem statement, a summary of technical discussions to date, and proposed next steps.

Workshop 1: Concentration risk

This workshop had discussed how AI’s increased integration into UK financial services introduced a new category of risks to consider, which not only arose from the AI technology itself, but also from the structure of the AI ecosystem.

The workshop had identified five key risk areas: concentration risk in third-party AI providers, contagion and disruption from model updates, capacity constraints and scalability challenges, the need for third-party assurance and minimum standards, and talent concentration and domestic capability gaps.

Members noted an increasing reliance on a small number of third-party AI providers, and the risks which could emerge as a result. The discussion also touched on the rise of AI agents and their potential implications for concentration risk, as these agents typically depend on models and infrastructure from a few dominant providers.

Workshop 2: Evolution of AI edge cases

This workshop had explored approaches to support the confident and responsible adoption of advanced AI in UK financial services, focusing on AI edge use cases – high-value applications where complexity introduced novel risks. Members noted that scenarios considered edge cases today could become business as usual in the future.

The workshop had highlighted several key perspectives, including the growing autonomy of AI models operating with limited human oversight. Members also observed that firms were facing pressure to demonstrate returns on AI investment, which could accelerate development timelines. Members noted that this reinforced the need to strike a careful balance between innovation and safety.

Workshop 3: Explainability and transparency in generative AI

This workshop had acknowledged challenges in defining explainability, particularly as explainability, interpretability, and transparency were often used interchangeably. While members agreed that explainability and transparency were essential for the trustworthy adoption of AI in financial services, the absence of consistent definitions could impede effective risk management.

To frame its approach, the workshop had adopted an outcomes-focused definition. The workshop intended to review existing domestic and international guidance on AI explainability and transparency to assess relevance for financial services, and to identify key characteristics of explainability and transparency for AI use in financial services.

Some members cautioned against focusing too heavily on definitions, suggesting that industry should prioritise building models with inherent explainability as this had not always been embedded in practice.

Workshop 4: AI-accelerated contagion

This workshop had discussed how AI-driven automation and interconnected decision-making could rapidly amplify shocks to the financial system. The workshop had emphasised the need to reflect on the long-term implications of AI without hindering competitiveness and innovation.

The workshop had identified three drivers of contagion risk:

• Market dynamics: increasing use of similar vendors, models, strategies, and common data sources, which could lead to synchronised market moves and amplify volatility.
• Operational resilience: dependency on a few critical vendors and infrastructure could create single points of failure.
• Model concentration and homogeneity: widespread use of same or similar models – even across different vendors – could result in correlated errors and rapid propagation of flaws across institutions.

Members illustrated contagion risk through the example of multiple firms using the same AI models for coding support, leading to programs becoming very similar, which could result in operational risks. Members queried how agentic AI may further exacerbate these risks, meaning that the autonomy of agentic workflows and still-evolving interoperability protocols could accelerate the spread of flawed updates or misaligned actions across interconnected systems.