AI in Finance Summit schedule

• Which use cases are delivering true scale (fraud, ops, servicing) and why are others hitting the "deployment wall"?
• Honest lessons from the "PoC graveyard"—where are firms rolling back investments, and what should practitioners stop doing immediately?
• How digital-native banks are outstripping incumbents, and what legacy institutions can realistically adopt to stay competitive.

• Why great pilots fail in production and how to architect for success?
• Insights on overcoming the governance and testing hurdles that cause stalling.
• Methods for ensuring your AI remains observable, safe, and resilient in live financial environments.

• A step-by-step look at aligning architecture and governance to move a high-stakes project out of the lab.
• Identifying where deployments fail between PoC and scale, and the proactive fixes used to bridge those gaps.
• Practical strategies for gaining ‘yes’ from risk, compliance, and cross-functional stakeholders without slowing down.
• How to define and communicate value in a way that secures multi-year funding and firm-wide adoption.

• Moving from standalone LLMs to integrated production systems—solving for latency, consistency, and state management.
• What does “gold standard” look like for AI engineering in financial services today?
• Exploring the evolution of tooling and pipelines—how automated evaluation and real-time monitoring are enabling hands-off deployment.

• How to move from abstract "responsible AI" principles to concrete, defensible engineering practices?
• Strategies for identifying and securing "off-book" LLM usage across the enterprise.
• Where does data privacy fails in complex, agentic financial ecosystems?
• Defining clear accountability across Risk, Tech, and Business to avoid "governance by committee."

• Moving beyond "accuracy" to define rigorous KPIs for hallucination, bias, and financial reasoning.
• Practical frameworks for uncovering edge cases and security vulnerabilities before they reach production.
• Identifying intervention points where human oversight must be implemented into the automated workflow.
• How to monitor and improve performance to prevent model drift post-deployment?

• Identifying the "Goldilocks" use cases for agents: tasks with high complexity but clear, programmable constraints.
• How do you design workflows with hard-coded "Circuit Breakers" and human-intervention triggers?
• Frameworks for ensuring consistency across agent systems to meet internal audit and reliability standards.
• How to mitigate the risks of data leakage and "permission creep" as agents navigate internal banking silos?

Two simultaneous roundtables running in parallel. Attendees remain seated at their chosen table throughout. Speakers rotate between tables at 40-minutes mark, ensuring both groups hear from every facilitator.

Each table will have a printed prompt card with 3 discussion questions to guide conversation and encourage audience participation.

ROUNDTABLE 1: Embedding AI into Real Workflows — From Tooling to Daily Usage

Focus: Operational adoption and workflow integration

• Where are AI tools actually being used daily vs. abandoned after rollout—and why?
• How do you redesign workflows (not just tools) to ensure AI becomes part of decision-making?
• What incentives, training, or friction points determine whether teams adopt or reject AI?
• Who is accountable when AI influences or makes decisions—and how is that defined in practice?
• Where should the line sit between human judgment and automated decision-making?
• How do you build trust with business users who don’t fully understand the models?

Two simultaneous roundtables running in parallel. Attendees remain seated at their chosen table throughout. Speakers rotate between tables at 40-minutes mark, ensuring both groups hear from every facilitator.

Each table will have a printed prompt card with 3 discussion questions to guide conversation and encourage audience participation.

ROUNDTABLE 2: Owning the Outcome — Accountability, Trust, and Human Oversight in AI Decisions

Focus: Decision ownership and human-AI collaboration

• Who is accountable when AI influences or makes decisions—and how is that defined in practice?
• Where should the line sit between human judgment and automated decision-making?
• How do you build trust with business users who don’t fully understand the models?

• How is AI fundamentally altering alpha generation, execution logic, and portfolio construction in 2026?
• Exploring the risks of "Model Herding"—could synchronized AI decision-making trigger new forms of market volatility and flash-instability?
• Where should firms draw the line between machine execution and human judgment?
• How are regulators approaching AI in capital markets, and what should firms prepare for next?

• Defending against "excessive agency" and unauthorised actions as autonomous systems become primary targets for cybercrime.
• Moving past system prompts to secure the non-human identities and "digital insiders" managing high-value transactions.
• Strategies for securing the AI supply chain—from data poisoning and model tampering to deepfake-driven social engineering.
• Building "Defensible AI" that maintains operational integrity under the scrutiny of the EU AI Act and DORA.

• Navigating the systemic risk of high-reliance on a few dominant third-party AI and Cloud providers.
• How can firms design for resilience under DORA, including failure scenarios and service outages?
• Meeting the 2026 FCA/ESMA mandate for algorithmic accountability—who is liable when an external model fails?
• How to maintain operational control when your core intelligence sits in a third-party environment.