AI in Finance Summit schedule

• Real-world scale outcomes vs. the business cases that funded them - which financial services use cases have genuinely reached industrial production in 2026?
• The "deployment wall" and what are the specific organisational, technical, and governance triggers that cause promising AI programs to stall between PoC and scale?
• Where is the gap structural (data, talent, legacy) vs. closeable (tooling, process, governance)? Digital-native vs. incumbent differentiation
• What does "industrial-grade AI" require, and why is it a different engineering problem than most firms anticipated?

Panellists:
Roxanne Howdle-Rowe, Managing Director, Group Head of Data & Analytics Office — BRITISH BUSINESS BANK

Parag Kumar, Executive Director, Data Analytics Lead, Compliance, Conduct and Operational Risk — J.P. MORGAN CHASE

Adam Niazi, Corporate & Investment Banking, Chief Operating Office, Business Manager — WELLS FARGO

Moderator: Disha Mukherjee, Lead Data Engineer — FORD CREDIT

Every day brings promises of a new, transformational agentic use case. Meanwhile, your organisation may be struggling to move from proofs-of-concept to secure, production-grade agents that you can govern. Achieving this requires a strategic, enterprise-wide approach to operationalising and securing AI and the data it needs to be useful. In this session, Purnima Padmanabhan introduces an actionable framework designed to address the real-world challenges of securing and operationalising agents in highly regulated industries like Finance. We will explore how to maintain compliance, sovereignty and security velocity, covering topics such as:

• Agentic Runtime Considerations: Establishing guardrails, observability, and standardisation for autonomous AI agents to ensure they operate within secure, compliant perimeters.
• Data & AI Sovereignty: Balancing regulatory compliance and local control with the flexibility of an open ecosystem of models, frameworks and data sources.
• Robust, Centralised Governance: Enforcing strict controls on agent access to sensitive enterprise tools, data, and infrastructure while maintaining sovereignty.
• Defending Against AI-Enabled Threats: Strategies for compressing the vulnerability-to-patch cycle to ensure your software supply chain is protected against the growing threat of AI-driven security vulnerabilities.

Join us to learn how to operationalise agents without compromising the integrity or security of your data and enterprise systems.

• Which financial services AI systems are now classified as High-Risk under the EU AI Act (post-August 2026), and what specific documentation, testing, and human oversight requirements are now mandatory?
• What has been harder than expected? Where compliance programs have encountered unexpected technical, legal, and operational friction
• What has been easier than expected? Practical shortcuts and interpretations that have emerged from early NANDO-notified body assessments
• Which UK firms operating under FCA/PRA supervision are now misaligned between EU AI Act obligations and domestic SS1/23 model risk requirements, and how to resolve it

Sebastian Obeta, Member of European AI Alliance — EUROPEAN COMMISSION 

Dr David, Crelley, Head of Responsible AI and Data — ADMIRAL GROUP PLC

• The production AI system architectural blueprint - what components must be owned vs. procured, and what are the vendor lock-in risks in each layer?
• The hardest infrastructure problems - latency management in multi-step LLM chains, state management across agent sessions, and consistency in high-throughput inference
• How to implement continuous LLM evaluation against financial reasoning, regulatory compliance, and domain accuracy benchmarks?
• What does production observability look like when your AI system is making thousands of consequential decisions per hour?

• Defending against "excessive agency" and unauthorised actions as autonomous systems become primary targets for cybercrime.
• Moving beyond system prompts to secure the non-human identities and "digital insiders" who manage 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.

PANEL: From Shadow AI to Automated Guardrails: How Model Risk and Engineering Are Finally Solving Governance Together

• The shadow AI crisis - how widespread is unsanctioned LLM usage in UK/EU financial institutions, and what are the realistic options for discovery, containment, and governance?
• Moving from abstract responsible AI principles to engineering-enforced controls looking at a production ML pipeline.
• When an agentic AI system causes a regulatory breach, who is responsible? The model owner, the data team, the business, or the vendor?
• What does 'engineering-enforced' EU AI Act compliance look like in a production ML pipeline?

Panellists:

Soung Low, Model Risk Data Scientist — NATWEST GROUP

Eshaan Salwan, IT Auditor — GOLDMAN SACHS

Ryan Courtier, Senior Vice President, AI Product Leader — CITI

Moderator: Temi Afeye, Technical Lead, Senior AI Scientist — LLOYDS BANKING GROUP

• 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 to ensure consistency across agent systems and meet internal audit and reliability standards.
• How to mitigate the risks of data leakage and "permission creep" as agents navigate internal banking silos?

• 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?

• Where are AI tools 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?

• What were the early warning signs that were missed?

• How are governance frameworks evolving to assign personal accountability for AI model failures to named executives?
• Who bears financial liability when an AI system causes material customer harm or regulatory breach?

• How is AI fundamentally altering alpha generation, execution logic, and portfolio construction in 2026?
• Exploring the risks of "Model Herding"—could synchronised 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?

Panellists:

Amit Kumar, Associate Director, eFx Quant — HSBC

• What does it cost to run an AI decision at scale, and why are most firms still unable to answer that question?
• How are leading institutions building AI FinOps disciplines that attribute real costs to business units, rather than burying them in an undifferentiated IT budget?
• What are the hidden cost multipliers that never appear in a vendor's ROI projection?
• What does a credible AI business case look like in 2026, and how do you present the true economics to a board that approved the budget without understanding the running costs?

• When three hyperscalers and two foundation model vendors control the inference layer of the global financial system, what is the realistic failure scenario, and what are regulators doing about it?
• What does a "demonstrable operational control" mean when your core AI capability is a third-party API? A look at specific architectural and contractual requirements that satisfy regulators
• What compliance now requires for production AI systems, which firms are unprepared, and what the enforcement timeline looks like
• How leading firms are managing the strategic tension between best-in-class third-party AI and the obligation to maintain explainability, control, and exit optionality