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, Group Head | Data Product | Chief Data and AI Office — LLOYDS BANKING GROUP

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

Phani Ravindra Kaligotla, Vice President, Global Product AI & Data Strategy — MASTERCARD 

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.

• How can financial-services firms quickly identify and classify AI systems that may fall under Annex III before the 2 December 2027 deadline?
• Which governance, documentation, testing, human-oversight and third-party controls should firms put in place now to prepare for high-risk AI requirements?
• How can firms apply Article 6 guidance consistently—without over-classifying systems, missing in-scope use cases or relying on weak exemptions?
• How can UK-regulated firms align EU AI Act preparation with PRA SS1/23, FCA expectations, Consumer Duty and SM&CR—and close the gaps between them?

Sebastian Obeta, Member of European AI Alliance — EUROPEAN COMMISSION 

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

Jared Browne, Head of Privacy & AI Governance — FEXCO 

Moderator: Michael Borelli, Director — AI & PARTNERS  

• 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.

• How firms discover unsanctioned AI use across SaaS, browser tools, APIs, copilots and locally built agents.
• How they replace blanket bans with approved alternatives, data controls, identity/access management, monitoring and escalation.
• What “governance by design” looks like in the ML/LLMOps lifecycle: model inventory, policy-as-code, prompt and data controls, evaluation, logging, kill switches and incident response.
• How to design meaningful human intervention for agentic workflows—who can pause, override or roll back a system, and at what trigger.
• How accountability works operationally when the model, workflow, vendor and business owner are different teams.

Panellists:  

Eshaan Salwan, IT Auditor — GOLDMAN SACHS

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

Nikolaos Louloudakis, Ph.D., Assistant Vice President - AI Engineering & Research — BARCLAYS

Kelly Moses, Managing Director, Head of Global Payment Services Legal    — HSBC

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?

• 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

• 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?

• 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